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Huang Z, Mo S, Wu H, Kong Y, Luo H, Li G, Zheng J, Tian H, Tang S, Chen Z, Wang Y, Xu J, Zhou L, Dong F. Optimizing breast cancer diagnosis with photoacoustic imaging: An analysis of intratumoral and peritumoral radiomics. Photoacoustics 2024; 38:100606. [PMID: 38665366 PMCID: PMC11044033 DOI: 10.1016/j.pacs.2024.100606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/26/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
Background The differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye. Purpose This study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy. Materials and methods From January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3-5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2 mm, 5 mm, and 8 mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features. Results The developed nomogram, combining 5 mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5 mm peritumoral region proving most effective in identifying malignant nodules. Conclusion This research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5 mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.
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Affiliation(s)
- Zhibin Huang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Sijie Mo
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Huaiyu Wu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Yao Kong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Hui Luo
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Guoqiu Li
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Jing Zheng
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Hongtian Tian
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Shuzhen Tang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Zhijie Chen
- Ultrasound Imaging System Development Department, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China
| | - Youping Wang
- Department of Clinical and Research, Shenzhen Mindray Bio-medical Electronics Co., Ltd., Shenzhen, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
| | - Luyao Zhou
- Department of Ultrasound, Shenzhen Children’ Hospital, No. 7019, Yitian Road, Futian District, Shenzhen 518026, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, China
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Yu Y, Guo H, Zhang M, Hou F, Yang S, Huang C, Duan L, Wang H. Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma. Cancer Imaging 2024; 24:59. [PMID: 38720384 PMCID: PMC11077743 DOI: 10.1186/s40644-024-00705-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/27/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.
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Affiliation(s)
- Yuan Yu
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Hongwei Guo
- Department of Operation Center, Women and Children's Hospital, Qingdao University, Shandong, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, Research and Development (R&D) center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Lisha Duan
- Department of Radiology, The Third Hospital of Hebei Medical University, Hebei, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
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Fan Y, Li X, Zhong P, Guo H, Han D, Tian W, Fan J. Radiological Features for Predicting the Status of CD8-Positive Lymphocytes in HER2 Positive Breast Cancer. Balkan Med J 2024; 41:213-221. [PMID: 38700366 PMCID: PMC11077930 DOI: 10.4274/balkanmedj.galenos.2024.2024-2-64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/04/2024] [Indexed: 05/05/2024] Open
Abstract
Background The level of tumor-infiltrating lymphocytes (TILs) in human epidermal growth factor receptor type 2 (HER2)-positive breast cancer (BC) is positively correlated with pathological complete response. Aims To investigate the relationship between ultrasound (US) and magnetic resonance imaging (MRI) features and the level of CD8-positive TILs (CD8+-TILs) in patients with HER2-positive BC. Study Design Retrospective cohort study. Methods This retrospective study included 155 consecutive women with HER2-positive BC. Patients were divided into two groups: CD8+-TILlow (< 35%) and CD8+-TILhigh (≥ 35%) groups. US and MRI features were evaluated using the BI-RADS lexicon, and the apparent diffusion coefficient (ADC) value was calculated using RadiAnt software. Univariate and multivariate analyses revealed the optimal US and MRI features for predicting CD8+-TIL levels. Receiver operating characteristic analysis and the Delong test were used to compare the diagnostic performance of US and MRI features. Furthermore, implementing a nomogram will increase clinical utility. Results Univariate analysis of US features showed significant differences in shape, orientation, and posterior echo between the two groups; however, there were no significant differences in margins, internal echo, and microcalcification. Multifactorial analysis revealed that shape, orientation, and posterior echo were independent risk factors, with odds ratios of 11.62, 2.70, and 0.16, respectively. In terms of MRI features, ADC was an independent predictor of CD8+-TIL levels. These three US features and the ADC performed well, with area under the curve (AUC) values of 0.802 and 0.705, respectively. The combination of US and ADC values had higher predictive efficacy (AUC = 0.888) than either US or ADC alone (p = 0.009, US_ADC vs. US; p < 0.001, US_ADC vs. ADC). Conclusion US features (shape, orientation, and posterior echo) and ADC value may be a valuable tool for estimating CD8+-TIL levels in HER2-positive BC. The nomogram may help clinicians in making decisions.
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Affiliation(s)
- Yuhong Fan
- Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China
| | - Xiaoguang Li
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Peng Zhong
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China
| | - Hong Guo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Dong Han
- Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China
| | - Wuguo Tian
- Department of Breast and Thyroid Surgery, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fan
- Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China
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Zhou J, Yu X, Wu Q, Wu Y, Fu C, Wang Y, Hai M, Tan H, Wang M. Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study. Heliyon 2024; 10:e28722. [PMID: 38623231 PMCID: PMC11016612 DOI: 10.1016/j.heliyon.2024.e28722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/17/2024] Open
Abstract
Purpose To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROIIntra) was dilated by 2, 4, 6 and 8 mm (ROIPeri2mm, ROIPeri4mm, ROIPeri6mm, and ROIPeri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat-saturated T2-weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. Result The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648-0.778), 0.706 (95% CI, 0.637-0.768), and 0.719 (95% CI, 0.651-0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686-0.810), 0.754 (95% CI, 0.688-0.812) and 0.725 (95% CI, 0.657-0.786), respectively, in the test set. Combining three sequences in the ROIIntra, ROIPeri2mm, ROIPeri4mm, ROIPeri6mm and ROIPeri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733-0.849) in the test set. Conclusion This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.
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Affiliation(s)
- Jing Zhou
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Xuan Yu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100089, China
| | - Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Cong Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Yunxia Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Menglu Hai
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, Henan Province, China
| | - Hongna Tan
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, 450003, Henan Province, China
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Li Z, Wang F, Zhang H, Xie S, Peng L, Xu H, Wang Y. A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer. Eur J Surg Oncol 2024; 50:108052. [PMID: 38447320 DOI: 10.1016/j.ejso.2024.108052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/24/2024] [Accepted: 02/21/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans. METHODS Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set. RESULTS Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749. CONCLUSION Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.
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Affiliation(s)
- Zhiyang Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Fuqiang Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Shenglong Xie
- Department of Thoracic Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Peng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, China.
| | - Yun Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China.
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Huang H, Chen H, Zheng D, Chen C, Wang Y, Xu L, Wang Y, He X, Yang Y, Li W. Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation. Cancer Imaging 2024; 24:44. [PMID: 38532520 DOI: 10.1186/s40644-024-00692-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/20/2024] [Indexed: 03/28/2024] Open
Abstract
PURPOSE To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA). METHODS Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis. RESULTS A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality. CONCLUSIONS The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.
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Affiliation(s)
- Haozhe Huang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Hong Chen
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Xuhui District, Shanghai, 200030, China
| | - Dezhong Zheng
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 500 Yutian Road, Hongkou District, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China
| | - Chao Chen
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Ying Wang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Lichao Xu
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Yaohui Wang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Xinhong He
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Yuanyuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 500 Yutian Road, Hongkou District, Shanghai, 200083, China.
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China.
| | - Wentao Li
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China.
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Aksu A, Güç ZG, Küçüker KA, Alacacıoğlu A, Turgut B. Intra and peritumoral PET radiomics analysis to predict the pathological response in breast cancer patients receiving neoadjuvant chemotherapy. Rev Esp Med Nucl Imagen Mol 2024:500002. [PMID: 38527731 DOI: 10.1016/j.remnie.2024.500002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/26/2024] [Indexed: 03/27/2024]
Abstract
OBJECTIVE The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). METHODS Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively. The volume of interest (VOI) of the primary tumor (VOI-T) was manually segmented, then a voxel-thick VOI was added around VOI-T to define the peritumoral area (VOI-PT). Morphological, intensity-based, histogram and texture parameters were obtained from VOIs. The patients were divided into two groups as pCR and non-complete pathological response (npCR). A "radiomic model" was created with only radiomic features, and a "patho-radiomic model" was created using radiomic features and immunohistochemical data. RESULTS Of the 66 patients included in the study, 21 were in the pCR group. The only statistically significant feature from the primary tumor among patients with pCR and npCR was Morphological_Compacity-T (AUC: 0.666). Between response groups, a significant difference was detected in 2 morphological, 1 intensity, 4 texture features from VOI-PT; no correlation was found between Morphological_Compacity-PT and NGTDM_contrast-PT. The obtained radiomic model's sensitivity and accuracy values were calculated as 61.9% and 75.8%, respectively (AUC: 0.786). When HER2 status was added, sensitivity and accuracy values of the patho-radiomic model increased to 85.7% and 81.8%, respectively (AUC: 0.903). CONCLUSIONS Evaluation of PET peritumoral radiomic features together with the primary tumor, rather than just the primary tumor, provides a better prediction of the pCR to NAC in patients with breast cancer.
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Affiliation(s)
- Ayşegül Aksu
- İzmir Kâtip Çelebi University, Atatürk Training and Research Hospital, Department of Nuclear Medicine, İzmir, Turkey.
| | - Zeynep Gülsüm Güç
- İzmir Kâtip Çelebi University, Atatürk Training and Research Hospital, Department of Medical Oncology, İzmir, Turkey
| | - Kadir Alper Küçüker
- İzmir Kâtip Çelebi University, Atatürk Training and Research Hospital, Department of Nuclear Medicine, İzmir, Turkey
| | - Ahmet Alacacıoğlu
- İzmir Kâtip Çelebi University, Atatürk Training and Research Hospital, Department of Medical Oncology, İzmir, Turkey
| | - Bülent Turgut
- İzmir Kâtip Çelebi University, Atatürk Training and Research Hospital, Department of Nuclear Medicine, İzmir, Turkey
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Li Y, Jian J, Ge H, Gao X, Qiang J. Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer. J Magn Reson Imaging 2024. [PMID: 38517321 DOI: 10.1002/jmri.29359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/11/2024] [Accepted: 03/11/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC). PURPOSE To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC. STUDY TYPE Retrospective. POPULATION 219 patients aged from 15 to 79 years were enrolled. FIELD STRENGTH/SEQUENCE 3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI). ASSESSMENT MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM). STATISTICAL TESTS Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference. RESULTS All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261). DATA CONCLUSION The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yong'ai Li
- Department of Radiology, Changzhi People's Hospital, Changzhi, Shanxi, China
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Junming Jian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Huijie Ge
- Department of Radiology, Changzhi People's Hospital, Changzhi, Shanxi, China
| | - Xin Gao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
- Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan, Shandong, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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Wang Y, Shang Y, Guo Y, Hai M, Gao Y, Wu Q, Li S, Liao J, Sun X, Wu Y, Wang M, Tan H. Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer. Front Oncol 2024; 14:1357145. [PMID: 38567148 PMCID: PMC10985134 DOI: 10.3389/fonc.2024.1357145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.
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Affiliation(s)
- Yunxia Wang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yiyan Shang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yaxin Guo
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Menglu Hai
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University &Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Yang Gao
- Heart Center, People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence Co., Ltd., Beijing, China
| | - Shunian Li
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jun Liao
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaojuan Sun
- School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yaping Wu
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hongna Tan
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Li J, Li S, Zhou W, Duan Y, Zheng H. Enhancing malignancy prediction in thyroid nodules: A multimodal ultrasound radiomics approach in TI-RADS category 4 lesions. J Clin Ultrasound 2024. [PMID: 38465504 DOI: 10.1002/jcu.23662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/03/2024] [Accepted: 02/12/2024] [Indexed: 03/12/2024]
Abstract
PURPOSE To explore the diagnostic value of intralesional and perilesional radiomics based on multimodal ultrasound (US) images in predicting the malignant ACR TIRADS 4 thyroid nodules (TNs). METHODS A total of 297 cases of TNs in patients who underwent preoperative thyroid grayscale US and shear wave elastography (STE) were enrolled (training cohort: n = 150, internal validation cohort: n = 77, external validation cohort: n = 70). Regions of interests (ROIs) were delineated on grayscale US images and STE images, and then an isotropic expansion of 1.0, 1.5, 2.0, 2.5, and 3.0 mm was applied. Predictive models were established using recursive feature elimination-support vector machines (RFE-SVM) based on radiomics features calculated by random forest. RESULTS The perilesional ROI1.5mm expansion achieved the highest area under curve (AUC) (AUC: 0.753 for grayscale US, 0.728 for STE; 95% confidence interval (CI): 0.664-0.743, 0.684-0.739, respectively). The joint model had the highest AUC values of 0.936 in the training dataset, 0.926 in internal dataset, and 0.893 in external dataset. The calibration curve showed good consistency and the decision curve indicated a greater clinical net benefit of the joint model. CONCLUSION Joint model containing perilesional radiomics (1.5 mm) had significant value in predicting the malignant ACR TIRADS 4 TNs.
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Affiliation(s)
- Jian Li
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Siyao Li
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Ultrasound, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong Province, China
| | - Wang Zhou
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Yayang Duan
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Hui Zheng
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
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Su Q, Wang N, Wang B, Wang Y, Dai Z, Zhao X, Li X, Li Q, Yang G, Nie P. Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study. Eur J Radiol 2024; 172:111350. [PMID: 38309216 DOI: 10.1016/j.ejrad.2024.111350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/09/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE To evaluate the performance of CT-based intratumoral, peritumoral and combined radiomics signatures in predicting prognosis in patients with osteosarcoma. METHODS The data of 202 patients (training cohort:102, testing cohort:100) with osteosarcoma admitted to the two hospitals from August 2008 to February 2022 were retrospectively analyzed. Progression free survival (PFS) and overall survival (OS) were used as the end points. The radiomics features were extracted from CT images, three radiomics signatures(RSintratumoral, RSperitumoral, RScombined)were constructed based on intratumoral, peritumoral and combined radiomics features, respectively, and the radiomics score (Rad-score) were calculated. Kaplan-Meier survival analysis was used to evaluate the relationship between the Rad-score with PFS and OS, the Harrell's concordance index (C-index) was used to evaluate the predictive performance of the radiomics signatures. RESULTS Finally, 8, 6, and 21 features were selected for the establishment of RSintratumoral, RSperitumoral, and RScombined, respectively. Kaplan-Meier survival analysis confirmed that the Rad-scores of the three RSs were significantly correlated with the PFS and OS of patients with osteosarcoma. Among the three radiomics signatures, RScombined had better predictive performance, the C-index of PSF prediction was 0.833 in the training cohort and 0.814 in the testing cohort, the C-index of OS prediction was 0.796 in the training cohort and 0.764 in the testing cohort. CONCLUSIONS CT-based intratumoral, peritumoral and combined radiomics signatures can predict the prognosis of patients with osteosarcoma, which may assist in individualized treatment and improving the prognosis of osteosarcoma patients.
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Affiliation(s)
- Qiushi Su
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Ning Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Bingyan Wang
- Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | | | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Xia Zhao
- Department of Radiology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Xiaoli Li
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Qiyuan Li
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangjie Yang
- Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
| | - Pei Nie
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Wang P, Wang X, Zhang M, Li G, Zhao N, Qiao Q. Combining the radiomics signature and HPV status for the risk stratification of patients with OPC. Oral Dis 2024; 30:272-280. [PMID: 36135344 DOI: 10.1111/odi.14386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/01/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The objective was to perform risk stratification of oropharyngeal cancer (OPC) for treatment de-escalation based on the radiomics analysis and human papillomavirus (HPV) status. METHODS A total of 265 patients with OPC who underwent baseline contrast-enhanced computed tomography were analyzed, and the patients were grouped into the training (n = 133) and test (n = 132) cohorts at a ratio of 1:1. Intratumoral and peritumoral radiomics features were extracted, and the radiomics signature (Rscore) was calculated using least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses. RESULTS Twelve features were selected to establish the radiomics signature (Rscore) of intratumoral regions +10-mm peritumoral regions, which yielded maximum AUCs of 0.835, 0.798, and 0.784 in the training, test, and validation cohorts, respectively. Patients with OPC were divided into the high-risk group, intermediate-risk group, and low-risk group based on the Rscore and HPV status and had different prognoses. Patients in the low-risk group benefit from radiotherapy alone, and patients in the intermediate-risk group only benefitted from chemoradiotherapy. CONCLUSION The radiomics signature appears to improve the predictive performance of clinical characteristics for oropharyngeal cancer. The combined stratification of the radiomics signature and HPV status might be preferred to select patients for de-escalated treatment.
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Affiliation(s)
- Ping Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Xuan Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Miao Zhang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Guang Li
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Ning Zhao
- Department of Otolaryngology, The First Hospital of China Medical University, Shenyang, China
| | - Qiao Qiao
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
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Deng T, Liang J, Yan C, Ni M, Xiang H, Li C, Ou J, Lin Q, Liu L, Tang G, Luo R, An X, Gao Y, Lin X. Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer. Cancer Imaging 2024; 24:31. [PMID: 38424620 PMCID: PMC10905812 DOI: 10.1186/s40644-024-00676-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/20/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Identifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to develop a nomogram incorporating ultrasound radiomic features and clinicopathological factors to predict gBRCA mutations in patients with BC. MATERIALS AND METHODS In this retrospective study, 497 women with BC who underwent gBRCA genetic testing from March 2013 to May 2022 were included, including 348 for training (84 with and 264 without a gBRCA mutation) and 149 for validation(36 patients with and 113 without a gBRCA mutation). Factors associated with gBRCA mutations were identified to establish a clinicopathological model. Radiomics features were extracted from the intratumoral and peritumoral regions (3 mm and 5 mm) of each image. The least absolute shrinkage and selection operator regression algorithm was used to select the features and logistic regression analysis was used to construct three imaging models. Finally, a nomogram that combined clinicopathological and radiomics features was developed. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness. RESULTS Age at diagnosis, family history of BC, personal history of other BRCA-related cancers, and human epidermal growth factor receptor 2 status were independent predictors of the clinicopathological model. The AUC of the imaging radiomics model combining intratumoral and peritumoral 3 mm areas in the validation set was 0.783 (95% confidence interval [CI]: 0.702-0.862), which showed the best performance among three imaging models. The nomogram yielded better performance than the clinicopathological model in validation sets (AUC: 0.824 [0.755-0.894] versus 0.659 [0.563-0.755], p = 0.007). CONCLUSION The nomogram based on ultrasound images and clinicopathological factors performs well in predicting gBRCA mutations in BC patients and may help to improve clinical decisions about genetic testing.
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Affiliation(s)
- Tingting Deng
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Jianwen Liang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, China
| | - Cuiju Yan
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Mengqian Ni
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Huiling Xiang
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Chunyan Li
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Jinjing Ou
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Qingguang Lin
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Lixian Liu
- Department of Ultrasound, Guangdong Second Provincial General Hospital, Guangzhou, 510060, China
| | - Guoxue Tang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Ultrasound, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510060, China
| | - Rongzhen Luo
- Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xin An
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, China.
| | - Xi Lin
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
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Wang H, Chen W, Jiang S, Li T, Chen F, Lei J, Li R, Xi L, Guo S. Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research. Sci Rep 2024; 14:5020. [PMID: 38424285 PMCID: PMC10904744 DOI: 10.1038/s41598-024-55838-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/28/2024] [Indexed: 03/02/2024] Open
Abstract
The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660-947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.
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Affiliation(s)
- Hui Wang
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu, China
| | - Wei Chen
- Department of Ultrasound, The Ningxia Hui Autonomous Region People's Hospital, Yinchuan, Ningxia, China
| | - Shanshan Jiang
- Department of Advanced Technical Support, Clinical and Technical Support, Philips Healthcare, Xi'an, Shanxi, China
| | - Ting Li
- Department of Ultrasound, The Ningxia Hui Autonomous Region People's Hospital, Yinchuan, Ningxia, China
| | - Fei Chen
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Ruixia Li
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Lili Xi
- Department of Pharmacologic Bases, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Shunlin Guo
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
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Cheung SM, Chan KS, Zhou W, Husain E, Gagliardi T, Masannat Y, He J. Spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive breast cancer. Sci Rep 2024; 14:4699. [PMID: 38409583 PMCID: PMC10897464 DOI: 10.1038/s41598-024-55458-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/23/2024] [Indexed: 02/28/2024] Open
Abstract
Deregulation of lipid composition in adipose tissue adjacent to breast tumour is observed in ex vivo and animal models. Novel non-invasive magnetic resonance imaging (MRI) allows rapid lipid mapping of the human whole breast. We set out to elucidate the spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive (ER +) breast cancer. Thirteen participants (mean age, 62 ± [SD] 6 years) with ER + breast cancer and 13 age-matched postmenopausal healthy controls were scanned on MRI. The number of double bonds in triglycerides was computed from MRI images to derive lipid composition maps of monounsaturated, polyunsaturated, and saturated fatty acids (MUFA, PUFA, SFA). The spatial heterogeneity measures (mean, median, skewness, entropy and kurtosis) of lipid composition in the peri-tumoural region and the whole breast of participants and in the whole breast of controls were computed. The Ki-67 proliferative activity marker and CD163 antibody on tumour-associated macrophages were assessed histologically. Mann Whitney U or Wilcoxon tests and Spearman's coefficients were used to assess group differences and correlations, respectively. For comparison against the whole breast in participants, peri-tumoural MUFA had a lower mean (median (IQR), 0.40 (0.02), p < .001), lower median (0.42 (0.02), p < .001), a negative skewness with lower magnitude (- 1.65 (0.77), p = .001), higher entropy (4.35 (0.64), p = .007) and lower kurtosis (5.13 (3.99), p = .001). Peri-tumoural PUFA had a lower mean (p < .001), lower median (p < .001), a positive skewness with higher magnitude (p = .005) and lower entropy (p = .002). Peri-tumoural SFA had a higher mean (p < .001), higher median (p < .001), a positive skewness with lower magnitude (p < .001) and lower entropy (p = .012). For comparison against the whole breast in controls, peri-tumoural MUFA had a negative skewness with lower magnitude (p = .01) and lower kurtosis (p = .009), however there was no difference in PUFA or SFA. CD163 moderately correlated with peri-tumoural MUFA skewness (rs = - .64), PUFA entropy (rs = .63) and SFA skewness (rs = .59). There was a lower MUFA and PUFA while a higher SFA, and a higher heterogeneity of MUFA while a lower heterogeneity of PUFA and SFA, in the peri-tumoural region in comparison with the whole breast tissue. The degree of lipid deregulation was associated with inflammation as indicated by CD163 antibody on macrophages, serving as potential marker for early diagnosis and response to therapy.
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Affiliation(s)
- Sai Man Cheung
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
| | - Kwok-Shing Chan
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Wenshu Zhou
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Ehab Husain
- Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, UK
| | - Tanja Gagliardi
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Department of Radiology, Royal Marsden Hospital, London, UK
| | - Yazan Masannat
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Broomfield Breast Unit, Broomfield Hospital, Mid and South Essex NHS Trust, Chelmsford, UK
- London Breast Institute, Princess Grace Hospital, London, UK
| | - Jiabao He
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
- Faculty of Medical Sciences, Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
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Tominaga M, Yamazaki M, Umezu H, Sugino H, Fuzawa Y, Yagi T, Ishikawa H. Prognostic Value and Pathological Correlation of Peritumoral Radiomics in Surgically Resected Non-Small Cell Lung Cancer. Acad Radiol 2024:S1076-6332(24)00055-2. [PMID: 38402002 DOI: 10.1016/j.acra.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/24/2024] [Accepted: 01/24/2024] [Indexed: 02/26/2024]
Abstract
RATIONALE AND OBJECTIVES To determine the additional value of peritumoral radiomics in predicting overall survival (OS) in surgically resected non-small cell lung cancer (NSCLC) and its correlation with pathological findings. METHODS A total of 526 patients with surgically resected NSCLC were included (191 training, 160 internal validation, and 175 external validation cohorts). CT images were used to segment the gross tumor volume (GTV) and peritumoral volume (PTV) within distances of 3, 6, 9 mm from the tumor boundary (PTV3, PTV6, and PTV9), and radiomic features were extracted. Four prognostic models for OS (GTV, GTV + PTV3, GTV + PTV6, and GTV + PTV9) were constructed using the training cohort. The prognostic ability and feature importance were evaluated using the validation cohorts. Pathological findings were compared between the two patient groups (n = 30 for each) having the top 30 and bottom 30 values of the most important peritumoral feature. RESULTS The GTV+ PTV3 models exhibited the highest predictive ability, which was higher than that of the GTV model in the internal validation cohort (C-index: 0.666 vs. 0.616, P = 0.027) and external validation cohort (C-index: 0.705 vs. 0.656, P = 0.048). The most important feature was GLDM_Dependence_Entropy, extracted from PTV3. High peritumoral GLDM_Dependence_Entropy was associated with a high proportion of invasive histological types, tumor spread through air spaces, and tumor-infiltrating lymphocytes (all P < 0.05). CONCLUSION The GTV and PTV3 combination demonstrated a higher prognostic ability, compared to GTV alone. Peritumoral radiomic features may be associated with various pathological prognostic factors.
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Affiliation(s)
- Masaki Tominaga
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Motohiko Yamazaki
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
| | - Hajime Umezu
- Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan
| | - Hideaki Sugino
- Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan
| | - Yuma Fuzawa
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takuya Yagi
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hiroyuki Ishikawa
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Jiang W, Meng R, Cheng Y, Wang H, Han T, Qu N, Yu T, Hou Y, Xu S. Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer. J Magn Reson Imaging 2024; 59:613-625. [PMID: 37199241 DOI: 10.1002/jmri.28776] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. PURPOSE To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. STUDY TYPE Retrospective. POPULATION Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). ASSESSMENT Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC. STATISTICAL TESTS Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram. RESULTS A total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. DATA CONCLUSION The constructed preoperative nomogram might effectively assess LVI. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Ruiqing Meng
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Yuan Cheng
- Department of Biomedical Engineering, China Medical University, Shenyang, China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Tingting Han
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ning Qu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shu Xu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
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Liu Z, Luo C, Chen X, Feng Y, Feng J, Zhang R, Ouyang F, Li X, Tan Z, Deng L, Chen Y, Cai Z, Zhang X, Liu J, Liu W, Guo B, Hu Q. Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study. Int J Surg 2024; 110:1039-1051. [PMID: 37924497 PMCID: PMC10871628 DOI: 10.1097/js9.0000000000000881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/22/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. METHODS This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. RESULTS Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). CONCLUSIONS We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.
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Affiliation(s)
- Ziwei Liu
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Chun Luo
- Department of Radiology, The First People’s Hospital of Foshan
| | - Xinjie Chen
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Yanqiu Feng
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
- School of Biomedical Engineering, Southern Medical University
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology
- Guangdong-Hong Kong-Macao Greater Bay Area Centre for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, 1023 Sha-Tai South Road, Guangzhou, China
| | - Jieying Feng
- Department of Radiology, The Sixth Affiliated Hospital, South China University of Technology, Foshan
| | - Rong Zhang
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Fusheng Ouyang
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Xiaohong Li
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Zhilin Tan
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Lingda Deng
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Yifan Chen
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Zhiping Cai
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Ximing Zhang
- Department of Radiology, The First People’s Hospital of Foshan
| | - Jiehong Liu
- School of Biomedical Engineering, Southern Medical University
| | - Wei Liu
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Baoliang Guo
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
| | - Qiugen Hu
- Department of Radiology,Southern Medical University (The First People’s Hospital of Shunde)
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Zhang W, Wang S, Wang Y, Sun J, Wei H, Xue W, Dong X, Wang X. Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer. Radiol Med 2024; 129:211-221. [PMID: 38280058 DOI: 10.1007/s11547-024-01768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/03/2024] [Indexed: 01/29/2024]
Abstract
PURPOSE We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions. MATERIALS AND METHODS A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson's coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses. RESULTS Among all the radiomics models, the "Mass + Margin3mm" model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882-0.930) and 0.922 (95% CI 0.894-0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816-0.889) in primary cohort, 0.870 (95% CI 0.818-0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781-0.884) in primary cohort, 0.911 (95% CI 0.858-0.964) in validation cohort). CONCLUSIONS The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.
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Affiliation(s)
- Wuyue Zhang
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Siying Wang
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Yichun Wang
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Jiawei Sun
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Hong Wei
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Weili Xue
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Xueying Dong
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China
| | - Xiaolei Wang
- In-Patient Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, NanGang District, Harbin, 150086, China.
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Yan M, Yao J, Zhang X, Xu D, Yang C. Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results. Cancer Med 2024; 13:e6946. [PMID: 38234171 PMCID: PMC10905683 DOI: 10.1002/cam4.6946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/25/2023] [Accepted: 01/09/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND We aimed to predict human epidermal growth factor receptor 2 (HER2) 2+ status in patients with breast cancer by constructing and validating machine learning models utilizing ultrasound (US) radiomics and clinical features. METHODS We analyzed 203 breast cancer cases immunohistochemically determined as HER2 2+ and used fluorescence in situ hybridization (FISH) as the confirmation method. From each case, the study analyzed 840 extracted radiomics features and 11 clinicopathologic features. Cases were randomly split into training (n = 141) and validation sets (n = 62) at a 7:3 ratio. Univariate logistic regression analysis was first performed on the 11 clinicopathologic characteristics. The least absolute shrinkage and selection operator (LASSO) and decision tree (DT) techniques were employed for post-feature selection. Finally, 19 radiomics features were utilized in logistic regression (LR) and Naive Bayesian (NB) classifiers. Model performance was gauged using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS Our models exhibited notable diagnostic efficacy in differentiating HER2-positive from negative breast cancer cases. In the validation sets, the LR model outperformed the NB model with an AUC of 0.860 and accuracy of 83.8% compared to NB's AUC of 0.684 and accuracy of 79.0%. The LR model demonstrated higher sensitivity (92.3% vs. 46.2%) while the NB model had a better specificity (91.8% vs. 63.3%) in the validation set. CONCLUSIONS Machine learning models grounded on radiomics efficiently predicted IHC HER2 2+ status in breast cancer patients, suggesting potential enhancements in clinical decision-making for treatment and management.
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Affiliation(s)
- Meiying Yan
- Department of ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Jincao Yao
- Department of ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Xiao Zhang
- Zhejiang Chinese Medical University, Hangzhou, China
- Department of ultrasound, the First People's Hospital of Hangzhou Lin'an District, Hangzhou, China
| | - Dong Xu
- Department of ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Chen Yang
- Department of ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
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Yu Y, Wang Z, Wang Q, Su X, Li Z, Wang R, Guo T, Gao W, Wang H, Zhang B. Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Front Oncol 2024; 13:1249339. [PMID: 38357424 PMCID: PMC10865896 DOI: 10.3389/fonc.2023.1249339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 02/16/2024] Open
Abstract
Purpose To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients. Method MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves. Results The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram. Conclusion We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.
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Affiliation(s)
- Yimiao Yu
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhibo Wang
- Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qi Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaohui Su
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhenghao Li
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
- Department of Galactophore, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruifeng Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianhui Guo
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wen Gao
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haiji Wang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Biyuan Zhang
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Liu C, Li L, Chen X, Huang C, Wang R, Liu Y, Gao J. Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer. Insights Imaging 2024; 15:23. [PMID: 38270724 PMCID: PMC10811314 DOI: 10.1186/s13244-023-01584-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/25/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND To investigate whether intratumoral and peritumoral radiomics may predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. METHODS Clinical, pathological, and CT data from 231 patients with advanced gastric cancer who underwent neoadjuvant chemotherapy at our hospital between July 2014 and February 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 161) and a validation group (n = 70). The support vector machine classifier was used to establish radiomics models. A clinical model was established based on the selected clinical indicators. Finally, the radiomics and clinical models were combined to generate a radiomics-clinical model. ROC analyses were used to evaluate the prediction efficiency for each model. Calibration curves and decision curves were used to evaluate the optimal model. RESULTS A total of 91 cases were recorded with good response and 140 with poor response. The radiomics model demonstrated that the AUC was higher in the combined model than in the intratumoral and peritumoral models (training group: 0.949, 0.943, and 0.846, respectively; validation group: 0.815, 0.778, and 0.701, respectively). Age, Borrmann classification, and Lauren classification were used to construct the clinical model. Among the radiomics-clinical models, the combined-clinical model showed the highest AUC (training group: 0.960; validation group: 0.843), which significantly improved prediction efficiency. CONCLUSION The peritumoral model provided additional value in the evaluation of pathological response after neoadjuvant chemotherapy against advanced gastric cancer, and the combined-clinical model showed the highest predictive efficiency. CRITICAL RELEVANCE STATEMENT Intratumoral and peritumoral radiomics can noninvasively predict the pathological response against advanced gastric cancer after neoadjuvant chemotherapy to guide early treatment decision and provide individual treatment for patients. KEY POINTS 1. Radiomics can predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. 2. Peritumoral radiomics has additional predictive value. 3. Radiomics-clinical models can guide early treatment decisions and improve patient prognosis.
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Affiliation(s)
- Chenchen Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Xingzhi Chen
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Yiyang Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China.
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Zheng G, Hou J, Shu Z, Peng J, Han L, Yuan Z, He X, Gong X. Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue. BMC Med Imaging 2024; 24:22. [PMID: 38245712 PMCID: PMC10800060 DOI: 10.1186/s12880-024-01198-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.
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Affiliation(s)
- Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Xiangyang Gong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China.
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Li Q, Wang N, Wang Y, Li X, Su Q, Zhang J, Zhao X, Dai Z, Wang Y, Sun L, Xing X, Yang G, Gao C, Nie P. Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study. Insights Imaging 2024; 15:9. [PMID: 38228977 DOI: 10.1186/s13244-023-01582-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 11/29/2023] [Indexed: 01/18/2024] Open
Abstract
OBJECTIVE To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS). METHODS In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index). RESULTS Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764-0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681-0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS. CONCLUSION The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients. CRITICAL RELEVANCE STATEMENT Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS. KEY POINTS • Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma.
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Affiliation(s)
- Qiyuan Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Ning Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yanmei Wang
- GE Healthcare China, Pudong New Town, Shanghai, China
| | - Xiaoli Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Qiushi Su
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Jing Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Xia Zhao
- Department of Radiology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Yao Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Li Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Xuxiao Xing
- Department of Radiology, The First Hospital of Xingtai, No. 376, Shunde Road, Xingtai, Hebei, China
| | - Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
| | - Chuanping Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China.
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China.
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Huang Y, Cao Y, Hu X, Lan X, Chen H, Tang S, Li L, Cheng Y, Gong X, Wang W, Jiang F, Yin T, Wang X, Zhang J. Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study. J Magn Reson Imaging 2023. [PMID: 38109316 DOI: 10.1002/jmri.29188] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection. PURPOSE To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection. STUDY TYPE Multicenter, longitudinal. POPULATION Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. FIELD STRENGTH/SEQUENCE Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence. ASSESSMENT Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features. STATISTICAL TESTS Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant. RESULTS In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709). DATA CONCLUSION The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Yao Huang
- School of Medicine, Chongqing University, Chongqing, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Ying Cao
- School of Medicine, Chongqing University, Chongqing, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Huifang Chen
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Sun Tang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Yue Cheng
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Xueqin Gong
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Wei Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Fujie Jiang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Chengdu, China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China
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Li Q, Huang Y, Xia Y, Li M, Tang W, Zhang M, Zhao Z. Radiogenomics for predicting microsatellite instability status and PD-L1 expression with machine learning in endometrial cancers: A multicenter study. Heliyon 2023; 9:e23166. [PMID: 38149198 PMCID: PMC10750045 DOI: 10.1016/j.heliyon.2023.e23166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/28/2023] Open
Abstract
Purpose To evaluate the effectiveness of machine learning model based on magnetic resonance imaging (MRI) in identifying microsatellite instability (MSI) status and PD-L1 expression in endometrial cancer (EC). Methods This retrospective study included 82 EC patients from 2 independent centers. Radiomics features from the intratumoral and peritumoral regions, obtained from four conventional MRI sequences (T2-weighted images; contrast-enhanced T1-weighted images; diffusion-weighted images; apparent diffusion coefficient), were combined with clinicopathologic characteristics to develop machine learning model for predicting MSI status and PD-L1 expression. 60 patients from center 1 were used as the training set for model construction, while 22 patients from center 2 were used as an external validation set for model evaluation. Results For predicting MSI status, the clinicopathologic model, radscore model, and combination model achieved area under the curves (AUCs) of 0.728, 0.833, and 0.889 in the training set, respectively, and 0.595, 0.790, and 0.848 in the validation set, respectively. For predicting PD-L1 expression, the clinicopathologic model, radscore model, and combination model achieved AUCs of 0.648, 0.814, and 0.834 in the training set, respectively, and 0.660, 0.708, and 0.764 in the validation set, respectively. Calibration curve analysis and decision curve analysis demonstrated good calibration and clinical utility of the combination model. Conclusion The machine learning model incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for predicting MSI status and PD-L1 expression in EC. This approach may contribute to precision medicine for EC patients.
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Affiliation(s)
- Qianling Li
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Zhejiang University School of Medicine, Shaoxing, 312000, China
| | - Ya'nan Huang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Yang Xia
- Department of Radiology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, 312000, China
| | - Meiping Li
- Department of Pathology, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang, 312000, China
| | - Wei Tang
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, Hangzhou, 310000, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
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Abbas E, Fanni SC, Bandini C, Francischello R, Febi M, Aghakhanyan G, Ambrosini I, Faggioni L, Cioni D, Lencioni RA, Neri E. Delta-radiomics in cancer immunotherapy response prediction: A systematic review. Eur J Radiol Open 2023; 11:100511. [PMID: 37520768 PMCID: PMC10371799 DOI: 10.1016/j.ejro.2023.100511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023] Open
Abstract
Background The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy. Purpose This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment. Methods A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool. Results Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis. Conclusions Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.
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Affiliation(s)
- Engy Abbas
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
| | | | - Claudio Bandini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | | | - Emanuele Neri
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
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Campana A, Gandomkar Z, Giannotti N, Reed W. The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review. J Med Radiat Sci 2023; 70:462-478. [PMID: 37534540 PMCID: PMC10715343 DOI: 10.1002/jmrs.709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023] Open
Abstract
Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI-based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n = 35), pathologically complete response (pCR) prediction (n = 15), lymph node metastasis (LNM) detection (n = 7) and recurrence rate prediction (n = 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual-targeted treatment. However, radiomics is still in the pre-clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited.
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Affiliation(s)
- Annalise Campana
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Ziba Gandomkar
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Nicola Giannotti
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
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Ma Y, Li Q. An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning. Cancer Radiother 2023; 27:705-711. [PMID: 37932182 DOI: 10.1016/j.canrad.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 11/08/2023]
Abstract
PURPOSE The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-treated CT images. MATERIALS AND METHODS This study enrolled 462 patients with NSCLC who received chemoradiation. On the basis of pretreated CT images, we developed three models to compare the prediction of chemoradiation: intratumoral, peritumoral and combined regions. To further illustrate each model, we established different feature integration methods: a) radiomics model with 1500 features; b) deep learning model with a multiple instance learning algorithm; c) integrated model by integrating radiomic and deep learning features. For radiomics and integrated models, support vector machine and the least absolute shrinkage and selection operator were used to extract and select features. Transfer learning and max pooling algorithms were used to identify high informative features in deep learning models. We applied ten-fold cross validation in model training and testing. RESULTS The best area under the curve (AUC) of intratumoral, peritumoral and combined models were 0.89 (95% CI, 0.74-0.93), 0.86 (95% CI, 0.75-0.92) and 0.92 (95% CI, 0.81-0.95), respectively. It indicated the importance of the peritumoral region for treatment response prediction and should be used in combination with the intratumoral region. Integrated models gave better results than models with radiomics and deep learning features alone in all regions of interest and radiomics models outperformed deep learning models in any comparative models. CONCLUSIONS The model that integrate radiomic and deep learning features and combined intra- and peritumoral regions provide valuable information in predicting treatment response of chemoradiation. It can help oncologists customize personalized clinical treatment plans for NSCLC patients.
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Affiliation(s)
- Y Ma
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
| | - Q Li
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Wang J, Gao W, Lu M, Yao X, Yang D. Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features. Front Oncol 2023; 13:1290313. [PMID: 38044998 PMCID: PMC10691503 DOI: 10.3389/fonc.2023.1290313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/02/2023] [Indexed: 12/05/2023] Open
Abstract
Background Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-centered radiomics offers sufficient data, its unsuitability for repeated examinations, along with limited accessibility and an intratumoral focus, constrain the application of predictive models in evaluating Ki-67 levels. Objective This study aims to explore ultrasound (US) image-based radiomics, incorporating both intra- and peritumoral features, to develop an interpretable ML model for predicting Ki-67 expression in BC patients. Methods A retrospective analysis was conducted on 263 BC patients, divided into training and external validation cohorts. From intratumoral and peritumoral regions of interest (ROIs) in US images, 849 distinctive radiomics features per ROI were derived. These features underwent systematic selection to analyze Ki-67 expression relationships. Four ML models-logistic regression, random forests, support vector machine (SVM), and extreme gradient boosting-were formulated and internally validated to identify the optimal predictive model. External validation was executed to ascertain the robustness of the optimal model, followed by employing Shapley Additive Explanations (SHAP) to reveal the significant features of the model. Results Among 231 selected BC patients, 67.5% exhibited high Ki-67 expression, with consistency observed across both training and validation cohorts as well as other clinical characteristics. Of the 1698 radiomics features identified, 15 were significantly correlated with Ki-67 expression. The SVM model, utilizing combined ROI, demonstrated the highest accuracy [area under the receiver operating characteristic curve (AUROC): 0.88], making it the most suitable for predicting Ki-67 expression. External validation sustained an AUROC of 0.82, affirming the model's robustness above a 40% threshold. SHAP analysis identified five influential features from intra- and peritumoral ROIs, offering insight into individual prediction. Conclusion This study emphasized the potential of SVM model using radiomics features from both intra- and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.
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Wu Z, Lin Q, Wang H, Chen J, Wang G, Fu G, Li L, Bian T. Intratumoral and Peritumoral Radiomics Based on Preoperative MRI for Evaluation of Programmed Cell Death Ligand-1 Expression in Breast Cancer. J Magn Reson Imaging 2023. [PMID: 37916918 DOI: 10.1002/jmri.29109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Programmed cell death ligand-1 (PD-L1) is a promising target for immune checkpoint blockade therapy in breast cancer. However, the preoperative evaluation of PD-L1 expression in breast cancer is rarely explored. PURPOSE To determine the ability of radiomics signatures based on preoperative dynamic contrast-enhanced (DCE) MRI to evaluate PD-L1 expression in breast cancer. STUDY TYPE Retrospective. POPULATION 196 primary breast cancer patients with preoperative MRI and postoperative pathological evaluation of PD-L1 expression, divided into training (n = 137, 28 PD-L1-positive) and test cohorts (n = 59, 12 PD-L1-positive). FIELD STRENGTH/SEQUENCE 3.0T; volume imaging for breast assessment DCE sequence. ASSESSMENT Radiomics features were extracted from the first phase of DCE-MRI by using the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. Three radiomics signatures were constructed based on the intratumoral, peritumoral, and combined intra- and peritumoral regions. The performance of the signatures was assessed using area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy. STATISTICAL TESTS Univariable and multivariable logistic regression analysis, t-tests, chi-square tests, Fisher exact test or Yates correction, ROC analysis, and one-way analysis of variance. P < 0.05 was considered significant. RESULTS In the test cohort, the combined radiomics signature (AUC, 0.853) exhibited superior performance compared to the intratumoral (AUC, 0.816; P = 0.528) and peritumoral radiomics signatures (AUC, 0.846; P = 0.905) in PD-L1 status evaluation, although the differences did not reach statistical significance. DATA CONCLUSION Intratumoral and peritumoral radiomics signatures based on preoperative breast MRI showed some potential accuracy for the non-invasive evaluation of PD-L1 status in breast cancer. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zengjie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Qing Lin
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Haibo Wang
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jingjing Chen
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guanqun Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangming Fu
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Lili Li
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Tiantian Bian
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Ma Q, Li Z, Li W, Chen Q, Liu X, Feng W, Lei J. MRI radiomics for the preoperative evaluation of lymphovascular invasion in breast cancer: A meta-analysis. Eur J Radiol 2023; 168:111127. [PMID: 37801997 DOI: 10.1016/j.ejrad.2023.111127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/08/2023] [Accepted: 09/28/2023] [Indexed: 10/08/2023]
Abstract
PURPOSE To evaluate the ability of preoperative MRI-based radiomic features in predicting lymphovascular invasion (LVI) in patients with breast cancer. METHODS PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases were searched to identify relevant studies published up until June 15, 2023. Two reviewers screened all papers independently for eligibility. We included diagnostic accuracy studies that used radiomics-MRI for LVI in patients with breast cancer, using histopathology as the reference standard. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score. Overall diagnostic odds ratio (DOR), sensitivity, specificity and area under the curve (AUC) were calculated to assess the prediction efficacy of MRI-based radiomic features in patients with breast cancer. Spearman's correlation coefficient was calculated and subgroup analysis performed to investigate causes of heterogeneity. RESULTS Eight studies comprising 1685 female patients were included. The pooled DOR, sensitivity, specificity, and AUC of radiomics in detecting LVI were 23 [confidence interval (CI) 16,32], 0.89(0.86,0.92), 0.82 (0.78,0.86), and 0.83(0.78,0.87), respectively. The meta-analysis showed significant heterogeneity among the included studies. No threshold effect was detected. Subgroup analysis showed that more than 200 participants, radiomics with clinical factors, semiautomatic segmentation method and peritumoral or intra- and peritumoral model [DOR: 28(18,42), 26(19,37), 34(16,70), 40(10,156), respectively] could improve diagnostic performance compared with less than 200 participants, only radiomics, manual segmentation method, and tumor model [DOR: 16(7,37), 21(6,73), 20(12,32), 21(13,32), respectively], but 3.0 T MR and multiple sequences approach [DOR: 27(15,49),17(8,35)] couldn't improve diagnostic performance compared with 1.5 T and DCE radiomic features [DOR:27(7,99),25(17,37)]. CONCLUSION Our meta-analysis showed that preoperative MRI-based radiomic features performs well in predicting LVI in patients with breast cancer. This noninvasive and convenient tool may be used to facilitate preoperative identification of LVI in breast cancer.
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Affiliation(s)
- Qinqin Ma
- Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
| | - Zhifan Li
- Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
| | - Wenjing Li
- Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
| | - Qitian Chen
- No.2 Hospital of Baiyin City, Baiyin 730900, China.
| | - Xinran Liu
- Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
| | - Wen Feng
- Lanzhou University, Lanzhou 730000, China; Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
| | - Junqiang Lei
- Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China.
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Malhaire C, Selhane F, Saint-Martin MJ, Cockenpot V, Akl P, Laas E, Bellesoeur A, Ala Eddine C, Bereby-Kahane M, Manceau J, Sebbag-Sfez D, Pierga JY, Reyal F, Vincent-Salomon A, Brisse H, Frouin F. Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy. Eur Radiol 2023; 33:8142-8154. [PMID: 37318605 DOI: 10.1007/s00330-023-09797-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/14/2023] [Accepted: 05/13/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. RESULTS Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction. CONCLUSION Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. CLINICAL RELEVANCE STATEMENT A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes. KEY POINTS • Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.
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Affiliation(s)
- Caroline Malhaire
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France.
- Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France.
| | - Fatine Selhane
- Gustave Roussy, Department of Imaging, Paris-Saclay University, 94805, Villejuif, France
| | | | - Vincent Cockenpot
- Pathology Unit, Centre Léon Bérard, 28 Rue Laennec, 69008, Lyon, France
| | - Pia Akl
- Women Imaging Unit, HCL, Radiologie du Groupement Hospitalier Est, 3 Quai Des Célestins, 69002, Lyon, France
| | - Enora Laas
- Department of Surgical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Audrey Bellesoeur
- Department of Medical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Catherine Ala Eddine
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Melodie Bereby-Kahane
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Julie Manceau
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Delphine Sebbag-Sfez
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Jean-Yves Pierga
- Department of Medical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Fabien Reyal
- Department of Surgical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | | | - Herve Brisse
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Frederique Frouin
- Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France
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Fan Y, Zhao D, Su J, Yuan W, Niu S, Guo W, Jiang W. Radiomic Signatures Based on Mammography and Magnetic Resonance Imaging as New Markers for Estimation of Ki-67 and HER-2 Status in Breast Cancer. J Comput Assist Tomogr 2023; 47:890-897. [PMID: 37948363 DOI: 10.1097/rct.0000000000001502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
OBJECTIVE The aim of the study is to investigate the values of intratumoral and peritumoral regions based on mammography and magnetic resonance imaging for the prediction of Ki-67 and human epidermal growth factor (HER-2) status in breast cancer (BC). METHODS Two hundred BC patients were consecutively enrolled between January 2017 and March 2021 and divided into training (n = 133) and validation (n = 67) groups. All the patients underwent breast mammography and magnetic resonance imaging screening. Features were derived from intratumoral and peritumoral regions of the tumor and selected using the least absolute shrinkage and selection operator regression to build radiomic signatures (RSs). Receiver operating characteristic curve analysis and the DeLong test were performed to assess and compare each RS. RESULTS For each modality, the combined RSs integrating features from intratumoral and peritumoral regions always showed better prediction performance for predicting Ki-67 and HER-2 status compared with the RSs derived from intratumoral or peritumoral regions separately. The multimodality and multiregional combined RSs achieved the best prediction performance for predicting the Ki-67 and HER-2 status with an area under the receiver operating characteristic curve of 0.888 and 0.868 in the training cohort and 0.800 and 0.848 in the validation cohort, respectively. CONCLUSIONS Peritumoral areas provide complementary information to intratumoral regions of BC. The developed multimodality and multiregional combined RSs have good potential for noninvasive evaluation of Ki-67 and HER-2 status in BC.
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Affiliation(s)
- Ying Fan
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Dan Zhao
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning
| | - Juan Su
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Wendi Yuan
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Shuxian Niu
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Wei Guo
- College of Computer Science, Shenyang Aerospace University, Shenyang
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, People's Republic. China
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Yu Y, Ren W, He Z, Chen Y, Tan Y, Mao L, Ouyang W, Lu N, Ouyang J, Chen K, Li C, Zhang R, Wu Z, Su F, Wang Z, Hu Q, Xie C, Yao H. Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study. Breast Cancer Res 2023; 25:132. [PMID: 37915093 PMCID: PMC10619251 DOI: 10.1186/s13058-023-01688-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/17/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.
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Affiliation(s)
- Yunfang Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, People's Republic of China
| | - Wei Ren
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Zifan He
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Yongjian Chen
- Department of Medical Oncology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Yujie Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Luhui Mao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Wenhao Ouyang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Nian Lu
- Imaging Diagnostic and Interventional Center, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng East Road, Guangzhou, Guangdong, People's Republic of China
| | - Jie Ouyang
- Department of Breast Surgery, Dongguan Tungwah Hospital, Dongguan, People's Republic of China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Chenchen Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Rong Zhang
- Department of Radiology, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao Town, Shunde District, Foshan, 528300, People's Republic of China
| | - Zhuo Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Fengxi Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China
| | - Zehua Wang
- Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Hong Kong Baptist University, Zhuhai, People's Republic of China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University, No. 1 Jiazi Road, Lunjiao Town, Shunde District, Foshan, 528300, People's Republic of China.
| | - Chuanmiao Xie
- Imaging Diagnostic and Interventional Center, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, No. 651 Dongfeng East Road, Guangzhou, Guangdong, People's Republic of China.
| | - Herui Yao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Center, Phase I Clinical Trial Centre, Artificial Intelligence Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, 510120, Guangzhou, People's Republic of China.
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Crispin-Ortuzar M, Woitek R, Reinius MAV, Moore E, Beer L, Bura V, Rundo L, McCague C, Ursprung S, Escudero Sanchez L, Martin-Gonzalez P, Mouliere F, Chandrananda D, Morris J, Goranova T, Piskorz AM, Singh N, Sahdev A, Pintican R, Zerunian M, Rosenfeld N, Addley H, Jimenez-Linan M, Markowetz F, Sala E, Brenton JD. Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer. Nat Commun 2023; 14:6756. [PMID: 37875466 PMCID: PMC10598212 DOI: 10.1038/s41467-023-41820-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/20/2023] [Indexed: 10/26/2023] Open
Abstract
High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.
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Affiliation(s)
- Mireia Crispin-Ortuzar
- Department of Oncology, University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
| | - Ramona Woitek
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Centre for Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria
| | - Marika A V Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Elizabeth Moore
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Lucian Beer
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Vlad Bura
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Leonardo Rundo
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
| | - Cathal McCague
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Stephan Ursprung
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Lorena Escudero Sanchez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Florent Mouliere
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Pathology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - James Morris
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Teodora Goranova
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Anna M Piskorz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Naveena Singh
- Department of Cellular Pathology, Barts Health NHS Trust, London, UK
| | - Anju Sahdev
- Department of Radiology, Barts Health NHS Trust, London, UK
| | - Roxana Pintican
- "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Radiology, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - Marta Zerunian
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome-Sant'Andrea University Hospital, Rome, Italy
| | - Nitzan Rosenfeld
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Helen Addley
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Western Balkans University, Tirana, Albania
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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Wang K, George-Jones NA, Chen L, Hunter JB, Wang J. Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model. Laryngoscope 2023; 133:2754-2760. [PMID: 36495306 PMCID: PMC10256836 DOI: 10.1002/lary.30516] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/17/2022] [Accepted: 11/20/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To develop a deep-learning-based multi-task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast-enhanced T1-weighted (ceT1) magnetic resonance images (MRIs). METHODS Initial ceT1 MRIs for VS patients meeting the inclusion/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow-up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (≥20% volumetric growth) were used as the ground truth for training and evaluating the TS and TEP modules, respectively. RESULTS We performed 5-fold cross-validation with the eligible patients (n = 103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p = 0.03) and marginally lower than the state-of-the-art segmentation model nnU-Net (dice coefficient of 86.45%, p = 0.16). The TEP performance is significantly better than the single-task prediction model (AUC = 0.60, p = 0.01) and marginally better than a radiomics-based prediction model (AUC = 0.70, p = 0.17). CONCLUSION The proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management. LEVEL OF EVIDENCE NA Laryngoscope, 133:2754-2760, 2023.
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Affiliation(s)
- Kai Wang
- The Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Nicholas A George-Jones
- The Department of Otolaryngology-Head and Neck Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- The Department of Otolaryngology-Head and Neck Surgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA
| | - Liyuan Chen
- The Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jacob B Hunter
- The Department of Otolaryngology-Head and Neck Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jing Wang
- The Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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Qiu Y, Liu YF, Shu X, Qiao XF, Ai GY, He XJ. Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer. Acad Radiol 2023; 30 Suppl 1:S1-S13. [PMID: 37393175 DOI: 10.1016/j.acra.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and evaluate a peritumoral radiomic-based machine learning model to differentiate low-Gleason grade group (L-GGG) and high-GGG (H-GGG) prostate lesions. MATERIALS AND METHODS In this retrospective study, a total of 175 patients with prostate cancer (PCa) confirmed by puncture biopsy were recruited and included 59 patients with L-GGG and 116 patients with H-GGG. The original PCa regions of interest (ROIs) were delineated on T2-weighted (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, and then centra-tumoral and peritumoral ROIs were defined. Features were meticulously extracted from each ROI to establish radiomics models, employing distinct sequence datasets. Peritumoral radiomics models were specifically developed for both the peripheral zone (PZ) and transitional zone (TZ), utilizing dedicated PZ and TZ datasets, respectively. The performances of the models were evaluated by using the receiver operating characteristic (ROC) curve and precision-recall curve. RESULTS The classification model with combined peritumoral features based on T2 + DWI + ADC sequence dataset demonstrated superior performance compared to the original tumor and centra-tumoral classification models. It achieved an area under the ROC curve (AUC) of 0.850 [95% confidence interval, 0.849, 0.860] and an average accuracy of 0.950. The combined peritumoral model outperformed the regional peritumoral models with AUC of 0.85 versus 0.75 for PZ lesions and 0.88 versus 0.69 for TZ lesions, respectively. The peritumoral classification models exhibit greater efficacy in predicting PZ lesions as opposed to TZ lesions. CONCLUSION The peritumoral radiomics features showed excellent performance in predicting GGG in PCa patients and might be a valuable addition to the non-invasive assessment of PCa aggressiveness.
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Affiliation(s)
- Yang Qiu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Yun-Fan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiao-Feng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Guang-Yong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiao-Jing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Kennedy LC, Kazerouni AS, Chau B, Biswas D, Alvarez R, Durenberger G, Dintzis SM, Stanton SE, Partridge SC, Gadi V. Associations of Multiparametric Breast MRI Features, Tumor-Infiltrating Lymphocytes, and Immune Gene Signature Scores Following a Single Dose of Trastuzumab in HER2-Positive Early-Stage Breast Cancer. Cancers (Basel) 2023; 15:4337. [PMID: 37686613 PMCID: PMC10486523 DOI: 10.3390/cancers15174337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/17/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
Dynamic biomarkers that permit the real-time monitoring of the tumor microenvironment response to therapy are an unmet need in breast cancer. Breast magnetic resonance imaging (MRI) has demonstrated value as a predictor of pathologic complete response and may reflect immune cell changes in the tumor microenvironment. The purpose of this pilot study was to investigate the value of breast MRI features as early markers of treatment-induced immune response. Fourteen patients with early HER2+ breast cancer were enrolled in a window-of-opportunity study where a single dose of trastuzumab was administered and both tissue and MRIs were obtained at the pre- and post-treatment stages. Functional diffusion-weighted and dynamic contrast-enhanced MRI tumor measures were compared with tumor-infiltrating lymphocytes (TILs) and RNA immune signature scores. Both the pre-treatment apparent diffusion coefficient (ADC) and the change in peak percent enhancement (DPE) were associated with increased tumor-infiltrating lymphocytes with trastuzumab therapy (r = -0.67 and -0.69, p < 0.01 and p < 0.01, respectively). Low pre-treatment ADC and a greater decrease in PE in response to treatment were also associated with immune-activated tumor microenvironments as defined by RNA immune signatures. Breast MRI features hold promise as biomarkers of early immune response to treatment in HER2+ breast cancer.
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Affiliation(s)
- Laura C. Kennedy
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Anum S. Kazerouni
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Bonny Chau
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Debosmita Biswas
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Rebeca Alvarez
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | | | - Suzanne M. Dintzis
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | - Sasha E. Stanton
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Cancer Immunoprevention Laboratory, Earle A. Chiles Research Institute, Portland, OR 97213, USA
| | - Savannah C. Partridge
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Vijayakrishna Gadi
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA
- Translational Oncology Program, University of Illinois Cancer Center, Chicago, IL 60612, USA
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Khorrami M, Viswanathan VS, Reddy P, Braman N, Kunte S, Gupta A, Abraham J, Montero AJ, Madabhushi A. Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer. NPJ Breast Cancer 2023; 9:67. [PMID: 37567880 PMCID: PMC10421862 DOI: 10.1038/s41523-023-00574-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
The combination of Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is the standard of care for hormone receptor-positive (HR + ), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC). Currently, there are no robust biomarkers that can predict response to CDK4/6i, and it is not clear which patients benefit from this therapy. Since MBC patients with liver metastases have a poorer prognosis, developing predictive biomarkers that could identify patients likely to respond to CDK4/6i is clinically important. Here we show the ability of imaging texture biomarkers before and a few cycles after CDK4/6i therapy, to predict early response and overall survival (OS) on 73 MBC patients with known liver metastases who received palbociclib plus ET from two sites. The delta radiomic model was associated with OS in validation set (HR: 2.4; 95% CI, 1.06-5.6; P = 0.035; C-index = 0.77). Compared to RECIST response, delta radiomic features predicted response with area under the curve (AUC) = 0.72, 95% confidence interval (CI) 0.67-0.88. Our study revealed that radiomics features can predict a lack of response earlier than standard anatomic/RECIST 1.1 assessment and warrants further study and clinical validation.
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Affiliation(s)
| | | | - Priyanka Reddy
- Department of Medicine, Division of Hematology and Oncology, University Hospitals/Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Siddharth Kunte
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Amit Gupta
- Department of Medicine, Division of Hematology and Oncology, University Hospitals/Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Jame Abraham
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Alberto J Montero
- Department of Medicine, Division of Hematology and Oncology, University Hospitals/Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.
- Atlanta VA Medical Center, Atlanta, GA, USA.
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Zhang S, Shao H, Li W, Zhang H, Lin F, Zhang Q, Zhang H, Wang Z, Gao J, Zhang R, Gu Y, Wang Y, Mao N, Xie H. Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study. Eur Radiol 2023; 33:5411-5422. [PMID: 37014410 DOI: 10.1007/s00330-023-09513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 12/20/2022] [Accepted: 02/01/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVE To construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography. METHODS A total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists. RESULTS The nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram. CONCLUSION The nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities. KEY POINTS • Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions. • The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.
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Affiliation(s)
- Shijie Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Huafei Shao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Wenjuan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Qianqian Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Han Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000
| | - Jing Gao
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, People's Republic of China, 2640003
| | - Ran Zhang
- Huiying Medical Technology Co, Ltd, Beijing, People's Republic of China, 100192
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200000, China
| | - Yunqiang Wang
- Department of Radiology, Yantai Hospital of Traditional Chinese Medicine, Yantai, Shandong, People's Republic of China, 264000.
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, Shandong, People's Republic of China, 264000.
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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Dong W, Ji Y, Pi S, Chen QF. Noninvasive imaging-based machine learning algorithm to identify progressive disease in advanced hepatocellular carcinoma receiving second-line systemic therapy. Sci Rep 2023; 13:10690. [PMID: 37393336 PMCID: PMC10314898 DOI: 10.1038/s41598-023-37862-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023] Open
Abstract
The aim of this study was to predict tyrosine kinase inhibitors (TKI) plus anti-PD-1 antibodies (TKI-PD-1) efficacy as second-line treatment in advanced hepatocellular carcinoma (HCC) using radiomics analysis. From November 2018 to November 2019, a total of 55 patients were included. Radiomic features were obtained from the CT images before treatment and filtered using intraclass correlation coefficients (ICCs) and least absolute shrinkage and selection operator (LASSO) methods. Subsequently, ten prediction algorithms were developed and validated based on radiomic characteristics. The accuracy of the constructed model was measured through area under the receiver operating characteristic curve (AUC) analysis; survival analysis was performed via Kaplan-Meier and Cox regression analyses. Overall, 18 (32.7%) out of 55 patients had progressive disease. Through ICCs and LASSO, ten radiomic features were entered into the algorithm construction and validation. Ten machine learning algorithms showed different accuracies, with the support vector machine (SVM) model having the highest AUC value of 0.933 in the training cohort and 0.792 in the testing cohort. The radiomic features were associated with overall survival. In conclsion, the SVM algorithm is a useful method to predict TKI-PD-1 efficacy in patients with advanced HCC using images taken prior to treatment.
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Affiliation(s)
- Wei Dong
- Department of Medical Oncology, Nanyang Second People's Hospital, Nanyang, China
| | - Ye Ji
- Department of Medical Oncology, Nanyang Central Hospital, Nanyang, China
| | - Shan Pi
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, Guangdong, China.
| | - Qi-Feng Chen
- Department of Medical Imaging and Interventional Radiology, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
- State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, China.
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
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Teng X, Zhang J, Zhang X, Fan X, Zhou T, Huang YH, Wang L, Lee EYP, Yang R, Cai J. Noninvasive imaging signatures of HER2 and HR using ADC in invasive breast cancer: repeatability, reproducibility, and association with pathological complete response to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:77. [PMID: 37381020 DOI: 10.1186/s13058-023-01674-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND The immunohistochemical test (IHC) of HER2 and HR can provide prognostic information and treatment guidance for invasive breast cancer patients. We aimed to develop noninvasive image signatures ISHER2 and ISHR of HER2 and HR, respectively. We independently evaluate their repeatability, reproducibility, and association with pathological complete response (pCR) to neoadjuvant chemotherapy. METHODS Pre-treatment DWI, IHC receptor status HER2/HR, and pCR to neoadjuvant chemotherapy of 222 patients from the multi-institutional ACRIN 6698 trial were retrospectively collected. They were pre-separated for development, independent validation, and test-retest. 1316 image features were extracted from DWI-derived ADC maps within manual tumor segmentations. ISHER2 and ISHR were developed by RIDGE logistic regression using non-redundant and test-retest reproducible features relevant to IHC receptor status. We evaluated their association with pCR using area under receiver operating curve (AUC) and odds ratio (OR) after binarization. Their reproducibility was further evaluated using the test-retest set with intra-class coefficient of correlation (ICC). RESULTS A 5-feature ISHER2 targeting HER2 was developed (AUC = 0.70, 95% CI 0.59 to 0.82) and validated (AUC = 0.72, 95% CI 0.58 to 0.86) with high perturbation repeatability (ICC = 0.92) and test-retest reproducibility (ICC = 0.83). ISHR was developed using 5 features with higher association with HR during development (AUC = 0.75, 95% CI 0.66 to 0.84) and validation (AUC = 0.74, 95% CI 0.61 to 0.86) and similar repeatability (ICC = 0.91) and reproducibility (ICC = 0.82). Both image signatures showed significant associations with pCR with AUC of 0.65 (95% CI 0.50 to 0.80) for ISHER2 and 0.64 (95% CI 0.50 to 0.78) for ISHER2 in the validation cohort. Patients with high ISHER2 were more likely to achieve pCR to neoadjuvant chemotherapy with validation OR of 4.73 (95% CI 1.64 to 13.65, P value = 0.006). Low ISHR patients had higher pCR with OR = 0.29 (95% CI 0.10 to 0.81, P value = 0.021). Molecular subtypes derived from the image signatures showed comparable pCR prediction values to IHC-based molecular subtypes (P value > 0.05). CONCLUSION Robust ADC-based image signatures were developed and validated for noninvasive evaluation of IHC receptors HER2 and HR. We also confirmed their value in predicting treatment response to neoadjuvant chemotherapy. Further evaluations in treatment guidance are warranted to fully validate their potential as IHC surrogates.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Fan
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Lu Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Elaine Yuen Phin Lee
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Y920, Lee Shau Kee Building, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Hong Kong, China.
- Research Institute for Smart Aging, The Hong Kong Polytechnic University, Hong Kong, China.
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Huang H, Li Z, Xia Y, Zhao Z, Wang D, Jin H, Liu F, Yang Y, Shen L, Lu Z. Association between radiomics features of DCE-MRI and CD8 + and CD4 + TILs in advanced gastric cancer. Pathol Oncol Res 2023; 29:1611001. [PMID: 37342362 PMCID: PMC10277864 DOI: 10.3389/pore.2023.1611001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer. Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density. Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630-0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = -0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = -0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856). Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.
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Affiliation(s)
- Huizhen Huang
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhiheng Li
- Department of Radiology, Anhui Provincial Hospital, Hefei, China
| | - Yue Xia
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hongyan Jin
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Fang Liu
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ye Yang
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Liyijing Shen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
- The First Affiliated Hospital of Shaoxing University, Shaoxing, China
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Chiacchiaretta P, Mastrodicasa D, Chiarelli AM, Luberti R, Croce P, Sguera M, Torrione C, Marinelli C, Marchetti C, Domenico A, Cocco G, Di Credico A, Russo A, D’Eramo C, Corvino A, Colasurdo M, Sensi SL, Muzi M, Caulo M, Delli Pizzi A. MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients. J Digit Imaging 2023; 36:1071-1080. [PMID: 36698037 PMCID: PMC10287859 DOI: 10.1007/s10278-023-00781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/26/2023] Open
Abstract
Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.
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Affiliation(s)
- Piero Chiacchiaretta
- Advanced Computing Core, Center of Advanced Studies and Technology (CAST), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
- Department of Innovative Technologies in Medicine and Odonoiatry, “G. d’Annunzio” University, Chieti, Italy
| | | | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Riccardo Luberti
- Unit of Radiology, “Santissima Annunziata” Hospital, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Mario Sguera
- Unit of Radiology, “Santissima Annunziata” Hospital, Chieti, Italy
| | | | | | - Chiara Marchetti
- Unit of Radiology, “Santissima Annunziata” Hospital, Chieti, Italy
| | | | - Giulio Cocco
- Unit of Ultrasound in Internal Medicine, Department of Medicine and Science of Aging, “G. D’Annunzio” University, Chieti, Italy
| | | | | | | | - Antonio Corvino
- Motor Science and Wellness Department, University of Naples “Parthenope”, 80133 Naples, Italy
| | - Marco Colasurdo
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Stefano L. Sensi
- Advanced Computing Core, Center of Advanced Studies and Technology (CAST), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Marzia Muzi
- Breast Unit, “Gaetano Bernabeo” Hospital, Ortona, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University, Chieti, Italy
| | - Andrea Delli Pizzi
- Department of Innovative Technologies in Medicine and Odonoiatry, “G. d’Annunzio” University, Chieti, Italy
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