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Lin Y, Wang J, Li M, Zhou C, Hu Y, Wang M, Zhang X. Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models. Breast 2024; 76:103737. [PMID: 38696854 PMCID: PMC11070644 DOI: 10.1016/j.breast.2024.103737] [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: 10/25/2023] [Revised: 04/05/2024] [Accepted: 04/22/2024] [Indexed: 05/04/2024] Open
Abstract
PURPOSE Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment. MATERIALS AND METHODS A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %. CONCLUSIONS Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.
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Affiliation(s)
- Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Jifei Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Meizhi Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Chunxiang Zhou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Yangling Hu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Mengyi Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University. 58th, The Second Zhongshan Road, Guangzhou, Guangdong, 510080, 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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Liu J, Leng X, Liu W, Ma Y, Qiu L, Zumureti T, Zhang H, Mila Y. An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer. Front Oncol 2024; 14:1285511. [PMID: 38500656 PMCID: PMC10946249 DOI: 10.3389/fonc.2024.1285511] [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: 09/19/2023] [Accepted: 02/20/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Methods Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. Results We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. Discussion The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.
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Affiliation(s)
- Jinhui Liu
- Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, China
| | - Xiaoling Leng
- Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, China
| | - Wen Liu
- Artificial Intelligence and Smart Mine Engineering Technology Center, Xinjiang Institute of Engineering, Urumqi, China
| | - Yuexin Ma
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lin Qiu
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Tuerhong Zumureti
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Haijian Zhang
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yeerlan Mila
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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An S, Huang G, Yu X, Liu J, Chen Y. The added diagnostic value of 18 F-FDG PET/CT radiomic analysis in multiple myeloma patients with negative visual analysis. Nucl Med Commun 2024; 45:244-252. [PMID: 38165165 DOI: 10.1097/mnm.0000000000001809] [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: 01/03/2024]
Abstract
PURPOSE A small number of patients diagnosed with multiple myeloma (MM) by bone marrow aspiration reported as being disease-free on 18 F-FDG PET/CT. We aim to evaluate the diagnostic value of radiomics approach in patients with MM who were negative by visual analysis. MATERIALS AND METHODS Thirty-three patients judged negative by visual analysis were assigned to the MM group. Contemporaneous 31 disease-free patients served as the control group. 70% of the whole data set was used as training set (23 from MM group and 22 from control group) and 30% as testing set (10 from MM group and 9 from control group). Axial skeleton volumes were automatically segmented and high-dimensional imaging features were extracted from PET and CT. The unsupervised machine learning method was used to filter and reduce the dimensions of the extracted features. Random forest was used to construct the prediction model and then validated with 10-fold cross-validation and evaluated on the independent testing set. RESULTS One thousand seven hundred two quantitative features were extracted from PET and CT. Of those, three first-order and one high-order imaging features were uncorrelated. With the cross-validation on the training group, the sensitivity, specificity, accuracy and area under the curve of random forest were 0.850, 0.792, 0.818 and 0.894, respectively. On the independent testing set, the accuracy of the model was 0.850 and the area under the curve was 0.909. CONCLUSION Radiomic analysis based on 18 F-FDG PET/CT using machine learning model provides a quantitative, objective and efficient mechanism for diagnosing patients with MM who were negative by visual analysis.
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Affiliation(s)
- Shuxian An
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Chantadisai M, Wongwijitsook J, Ritlumlert N, Rakvongthai Y. Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients. Sci Rep 2024; 14:5001. [PMID: 38424177 PMCID: PMC10904821 DOI: 10.1038/s41598-024-55755-6] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).
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Affiliation(s)
- Maythinee Chantadisai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
| | - Jirarot Wongwijitsook
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Surin Hospital, Surin, Thailand
| | - Napat Ritlumlert
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
- School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Yothin Rakvongthai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Robson N, Thekkinkattil DK. Current Role and Future Prospects of Positron Emission Tomography (PET)/Computed Tomography (CT) in the Management of Breast Cancer. Medicina (Kaunas) 2024; 60:321. [PMID: 38399608 PMCID: PMC10889944 DOI: 10.3390/medicina60020321] [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: 01/03/2024] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Breast cancer has become the most diagnosed cancer in women globally, with 2.3 million new diagnoses each year. Accurate early staging is essential for improving survival rates with metastatic spread from loco regional to distant metastasis, decreasing mortality rates by 50%. Current guidelines do not advice the routine use of positron emission tomography (PET)-computed tomography (CT) in the staging of early breast cancer in the absence of symptoms. However, there is a growing body of evidence to suggest that the use of PET-CT in this early stage can benefit the patient by improving staging and as a result treatment and outcomes, as well as psychological burden, without increasing costs to the health service. Ongoing research in PET radiomics and artificial intelligence is showing promising future prospects in its use in diagnosis, staging, prognostication, and assessment of responses to the treatment of breast cancer. Furthermore, ongoing research to address current limitations of PET-CT by improving techniques and tracers is encouraging. In this narrative review, we aim to evaluate the current evidence of the usefulness of PET-CT in the management of breast cancer in different settings along with its future prospects, including the use of artificial intelligence (AI), radiomics, and novel tracers.
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Affiliation(s)
- Nicole Robson
- Lincoln Medical School, Ross Lucas Medical Sciences Building, University of Lincoln, Lincoln LN6 7FS, UK;
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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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Nakajo M, Jinguji M, Ito S, Tani A, Hirahara M, Yoshiura T. Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology. Jpn J Radiol 2024; 42:28-55. [PMID: 37526865 PMCID: PMC10764437 DOI: 10.1007/s11604-023-01476-1] [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: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.
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Affiliation(s)
- Masatoyo Nakajo
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Megumi Jinguji
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Soichiro Ito
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atushi Tani
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuho Hirahara
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [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: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
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Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. View (Beijing) 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, 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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Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [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: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
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Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
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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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Kawaji K, Nakajo M, Shinden Y, Jinguji M, Tani A, Hirahara D, Kitazono I, Ohtsuka T, Yoshiura T. Application of Machine Learning Analyses Using Clinical and [ 18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer. Mol Imaging Biol 2023; 25:923-934. [PMID: 37193804 DOI: 10.1007/s11307-023-01823-8] [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: 11/08/2022] [Revised: 03/20/2023] [Accepted: 04/28/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic characteristics to predict disease recurrences in patients with breast cancers who underwent surgery. PROCEDURES This retrospective study included 112 patients with 118 breast cancer lesions who underwent [18F]-FDG-PET/ X-ray computed tomography (CT) preoperatively, and these lesions were assigned to training (n=95) and testing (n=23) cohorts. A total of 12 clinical and 40 [18F]-FDG-PET-based radiomic characteristics were used to predict recurrences using 7 different ML algorithms, namely, decision tree, random forest (RF), neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine (SVM) with a 10-fold cross-validation and synthetic minority over-sampling technique. Three different ML models were created using clinical characteristics (clinical ML models), radiomic characteristics (radiomic ML models), and both clinical and radiomic characteristics (combined ML models). Each ML model was constructed using the top ten characteristics ranked by the decrease in Gini impurity. The areas under ROC curves (AUCs) and accuracies were used to compare predictive performances. RESULTS In training cohorts, all 7 ML algorithms except for logistic regression algorithm in the radiomics ML model (AUC = 0.760) achieved AUC values of >0.80 for predicting recurrences with clinical (range, 0.892-0.999), radiomic (range, 0.809-0.984), and combined (range, 0.897-0.999) ML models. In testing cohorts, the RF algorithm of combined ML model achieved the highest AUC and accuracy (95.7% (22/23)) with similar classification performance between training and testing cohorts (AUC: training cohort, 0.999; testing cohort, 0.992). The important characteristics for modeling process of this RF algorithm were radiomic GLZLM_ZLNU and AJCC stage. CONCLUSIONS ML analyses using both clinical and [18F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery.
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Affiliation(s)
- Kodai Kawaji
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Masatoyo Nakajo
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Yoshiaki Shinden
- Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Megumi Jinguji
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atsushi Tani
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Daisuke Hirahara
- Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan
| | - Ikumi Kitazono
- Department of Pathology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takao Ohtsuka
- Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Abstract
New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, Naples 80138, Italy.
| | - Linda Moy
- Department of Radiology, New York University School of Medicine, 160 East 34th Street, New York, NY 10016, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY 10065, USA
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Bulut G, Atilgan HI, Çınarer G, Kılıç K, Yıkar D, Parlar T. Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT. PLoS One 2023; 18:e0290543. [PMID: 37708209 PMCID: PMC10501592 DOI: 10.1371/journal.pone.0290543] [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: 12/07/2022] [Accepted: 08/10/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC). INTRODUCTION NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis. METHODS This article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC. RESULTS Pathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response. CONCLUSION It was concluded that deep learning methods can predict breast cancer treatment.
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Affiliation(s)
- Gülcan Bulut
- Division of Medical Oncology, International Medicana Hospital, Izmir, Turkey
| | - Hasan Ikbal Atilgan
- Department of Nuclear Medicine, Mustafa Kemal University Medical School, Hatay, Turkey
| | - Gökalp Çınarer
- Department of Computer Engineering, Faculty of Engineering and Architecture, Bozok University, Yozgat, Turkey
| | - Kazım Kılıç
- Department of Computer Programming, Yozgat Vocational High School, Bozok University, Yozgat, Turkey
| | - Deniz Yıkar
- Division of Nuclear Medicine, Hatay Training and Research Hospital, Hatay, Turkey
| | - Tuba Parlar
- Department of Computer Technologies, Mustafa Kemal University, Hatay, Türkiye
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Champendal M, Marmy L, Malamateniou C, Sá Dos Reis C. Artificial intelligence to support person-centred care in breast imaging - A scoping review. J Med Imaging Radiat Sci 2023; 54:511-544. [PMID: 37183076 DOI: 10.1016/j.jmir.2023.04.001] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023]
Abstract
AIM To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies. METHODS The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage. RESULTS 3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used. CONCLUSIONS The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
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Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Laurent Marmy
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Christina Malamateniou
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, UK.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
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Najid S, Seban RD, Champion L, De Moura A, Sebbag C, Salaün H, Cabel L, Bonneau C. Clinical Utility of Pre-Therapeutic [18F]FDG PET/CT Imaging for Predicting Outcomes in Breast Cancer. J Clin Med 2023; 12:5487. [PMID: 37685551 PMCID: PMC10488013 DOI: 10.3390/jcm12175487] [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: 06/26/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND [18F]FDG PET/CT is used for staging and could also provide information associated with clinical outcomes. The objective of this study was to determine the clinical utility of biomarkers measured using [18F]FDG PET/CT to predict the absence of pathological complete response (no-pCR) and recurrence. METHODS In this retrospective study, we included patients with non-special-type breast carcinoma who underwent [18F]FDG PET/CT before neoadjuvant chemotherapy between 2011 and 2019. Clinicopathological data were collected. Tumor SUVmax and total metabolic tumor volume (TMTV) were measured from PET images. The association between biomarkers and no-pCR was studied using logistic regression. The cut-off value was determined using the area under the ROC Curve. To predict 3-year recurrence-free survival (RFS), we used a multivariable Cox model, and the cut-off value was determined using time-dependent ROC and predictiveness curves. RESULTS Two hundred and eighty-six patients were included in the analysis. One hundred and twelve patients had a pCR (39.2%). The pCR rate was significantly higher in patients with a high nuclear grade (p < 0.01), HER2+ and TNBC subtypes (p < 0.01), high Ki67 (p < 0.01), and low TMTV (p < 0.01). A high TMTV value (>9.0 cm3) was significantly associated with no-pCR in the whole cohort (OR = 2.4, 95% CI: 1.3-4.2, p < 0.01). After a median follow-up of 4.5 years, 65 patients experienced recurrence and 39 patients died. High TMTV (>13.5 cm3) was associated with shorter RFS (HR = 4.0, 95% CI: 1.9-8.4, p < 0.01). CONCLUSION High TMTV in pre-therapeutic imaging is associated with no-pCR and recurrence. It can help in identifying high-risk patients and be considered as an intensified or alternative adjuvant therapy for closely monitoring patients.
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Affiliation(s)
- Sophia Najid
- Institut Curie, Inserm U900, 92210 Saint-Cloud, France
| | - Romain-David Seban
- Department of Nuclear Medicine, Institut Curie, 92210 Saint-Cloud, France;
| | - Laurence Champion
- Department of Nuclear Medicine, Institut Curie, 92210 Saint-Cloud, France;
| | - Alexandre De Moura
- Department of Medical Oncology, Institut Curie, PSL Research University, 75005 Paris, France; (A.D.M.); (C.S.); (H.S.); (L.C.)
- UVSQ, Paris Saclay University, 92210 Saint-Cloud, France
| | - Clara Sebbag
- Department of Medical Oncology, Institut Curie, PSL Research University, 75005 Paris, France; (A.D.M.); (C.S.); (H.S.); (L.C.)
- UVSQ, Paris Saclay University, 92210 Saint-Cloud, France
| | - Hélène Salaün
- Department of Medical Oncology, Institut Curie, PSL Research University, 75005 Paris, France; (A.D.M.); (C.S.); (H.S.); (L.C.)
- UVSQ, Paris Saclay University, 92210 Saint-Cloud, France
| | - Luc Cabel
- Department of Medical Oncology, Institut Curie, PSL Research University, 75005 Paris, France; (A.D.M.); (C.S.); (H.S.); (L.C.)
- UVSQ, Paris Saclay University, 92210 Saint-Cloud, France
| | - Claire Bonneau
- Department of Surgery, Institut Curie, 92210 Saint-Cloud, France
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Lim CH, Choi JY, Choi JH, Lee JH, Lee J, Lim CW, Kim Z, Woo SK, Park SB, Park JM. Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2023; 15:3842. [PMID: 37568658 PMCID: PMC10417050 DOI: 10.3390/cancers15153842] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.
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Affiliation(s)
- Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Jun-Hee Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Jihyoun Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Cheol Wan Lim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Zisun Kim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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20
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Jia T, Lv Q, Cai X, Ge S, Sang S, Zhang B, Yu C, Deng S. Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer. Front Oncol 2023; 13:1210125. [PMID: 37576897 PMCID: PMC10415070 DOI: 10.3389/fonc.2023.1210125] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose The aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice. Methods We retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status. Results The Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application. Conclusion The combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.
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Affiliation(s)
- Tongtong Jia
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qingfu Lv
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaowei Cai
- Department of Nuclear Medicine, The Affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, China
| | - Shushan Ge
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shibiao Sang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chunjing Yu
- Department of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Shengming Deng
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
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21
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Jiang C, Yuan Y, Gu B, Ahn E, Kim J, Feng D, Huang Q, Song S. Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with 18F-FDG PET/CT radiomics analysis. Clin Radiol 2023:S0009-9260(23)00219-2. [PMID: 37365115 DOI: 10.1016/j.crad.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 04/23/2023] [Accepted: 05/13/2023] [Indexed: 06/28/2023]
Abstract
AIM To develop and validate a predictive model based on 2-[18F]-fluoro-2-deoxy-d-glucose (18F-FDG) positron-emission tomography (PET)/computed tomography (CT) radiomics features and clinicopathological parameters to preoperatively identify microvascular invasion (MVI) and perineural invasion (PNI), which are important predictors of poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS Preoperative 18F-FDG PET/CT images and clinicopathological parameters of 170 patients in PDAC were collected retrospectively. The whole tumour and its peritumoural variants (tumour dilated with 3, 5, and 10 mm pixels) were applied to add tumour periphery information. A feature-selection algorithm was employed to mine mono-modality and fused feature subsets, then conducted binary classification using gradient boosted decision trees. RESULTS For MVI prediction, the model performed best on a fused subset of 18F-FDG PET/CT radiomics features and two clinicopathological parameters, with an area under the receiver operating characteristic curve (AUC) of 83.08%, accuracy of 78.82%, recall of 75.08%, precision of 75.5%, and F1-score of 74.59%. For PNI prediction, the model achieved best prediction results only on the subset of PET/CT radiomics features, with AUC of 94%, accuracy of 89.33%, recall of 90%, precision of 87.81%, and F1 score of 88.35%. In both models, 3 mm dilation on the tumour volume produced the best results. CONCLUSIONS The radiomics predictors from preoperative 18F-FDG PET/CT imaging exhibited instructive predictive efficacy in the identification of MVI and PNI status preoperatively in PDAC. Peritumoural information was shown to assist in MVI and PNI predictions.
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Affiliation(s)
- C Jiang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China; Department of Nuclear Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Y Yuan
- Biomedical and Multimedia Information Technology Research Group, School of Computer Science, University of Sydney, Sydney, Australia
| | - B Gu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - E Ahn
- Discipline of Information Technology, College of Science & Engineering, James Cook University, Australia
| | - J Kim
- Biomedical and Multimedia Information Technology Research Group, School of Computer Science, University of Sydney, Sydney, Australia
| | - D Feng
- Biomedical and Multimedia Information Technology Research Group, School of Computer Science, University of Sydney, Sydney, Australia
| | - Q Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - S Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
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22
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Huang Y, Zhu T, Zhang X, Li W, Zheng X, Cheng M, Ji F, Zhang L, Yang C, Wu Z, Ye G, Lin Y, Wang K. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study. EClinicalMedicine 2023; 58:101899. [PMID: 37007742 PMCID: PMC10050775 DOI: 10.1016/j.eclinm.2023.101899] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 04/04/2023] Open
Abstract
Background Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.
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Affiliation(s)
- YuHong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - Teng Zhu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - XiaoLing Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Li
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - XingXing Zheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - MinYi Cheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - Fei Ji
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - LiuLu Zhang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - CiQiu Yang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
| | - ZhiYong Wu
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
- Corresponding author. Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, China
| | - GuoLin Ye
- Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, Guangdong, China
- Corresponding author. Department of Breast Cancer, The First People's Hospital of Foshan, Foshan, 528000, China.
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Corresponding author. Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080 Guangdong, China
- Corresponding author. Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China
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Xu X, Sun X, Ma L, Zhang H, Ji W, Xia X, Lan X. 18F-FDG PET/CT radiomics signature and clinical parameters predict progression-free survival in breast cancer patients: A preliminary study. Front Oncol 2023; 13:1149791. [PMID: 36969043 PMCID: PMC10036789 DOI: 10.3389/fonc.2023.1149791] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
IntroductionThis study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.MethodsBreast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecularsubtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance.ResultsEleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726–0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.ConclusionThe ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.
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Affiliation(s)
- Xiaojun Xu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
| | - Xun Sun
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
| | - Ling Ma
- He Kang Corporate Management (SH) Co. Ltd, Shanghai, China
| | - Huangqi Zhang
- Department of Radiology, Affiliated Taizhou Hospital of Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Wenbin Ji
- Department of Radiology, Affiliated Taizhou Hospital of Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xiaotian Xia
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
- *Correspondence: Xiaotian Xia, ; Xiaoli Lan,
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
- *Correspondence: Xiaotian Xia, ; Xiaoli Lan,
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Oliveira C, Oliveira F, Vaz SC, Marques HP, Cardoso F. Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer. Br J Radiol 2023; 96:20220655. [PMID: 36867773 DOI: 10.1259/bjr.20220655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Complete pathological response to neoadjuvant systemic treatment (NAST) in some subtypes of breast cancer (BC) has been used as a surrogate of long-term outcome. The possibility of predicting BC pathological response to NAST based on the baseline 18F-Fluorodeoxyglucose positron emission tomography (FDG PET), without the need of an interim study, is a focus of recent discussion. This review summarises the characteristics and results of the available studies regarding the potential impact of heterogeneity features of the primary tumour burden on baseline FDG PET in predicting pathological response to NAST in BC patients. Literature search was conducted on PubMed database and relevant data from each selected study were collected. A total of 13 studies were eligible for inclusion, all of them published over the last 5 years. Eight out of 13 analysed studies indicated an association between FDG PET-based tumour uptake heterogeneity features and prediction of response to NAST. When features associated with predicting response to NAST were derived, these varied between studies. Therefore, definitive reproducible findings across series were difficult to establish. This lack of consensus may reflect the heterogeneity and low number of included series. The clinical relevance of this topic justifies further investigation about the predictive role of baseline FDG PET.
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Affiliation(s)
- Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Francisco Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Sofia C Vaz
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | | | - Fátima Cardoso
- Breast Unit, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
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Duan Y, Yang G, Miao W, Song B, Wang Y, Yan L, Wu F, Zhang R, Mao Y, Wang Z. Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. J Comput Assist Tomogr 2023; 47:199-204. [PMID: 36790871 DOI: 10.1097/rct.0000000000001426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
PURPOSE Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. METHODS A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. RESULTS Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. CONCLUSION The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.
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Affiliation(s)
- Yanli Duan
- From the Departments of Nuclear Medicine
| | | | | | - Bingxue Song
- Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong
| | | | - Lei Yan
- From the Departments of Nuclear Medicine
| | - Fengyu Wu
- From the Departments of Nuclear Medicine
| | - Ran Zhang
- Huiying Medical Technology Co, Ltd, Beijing
| | - Yan Mao
- Diagnosis and Treatment Centre of Breast Diseases, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Kitajima K, Higuchi T, Fujimoto Y, Ishikawa E, Yokoyama H, Komoto H, Inao Y, Yamakado K, Miyoshi Y. Relationship between FDG-PET and the immune microenvironment in breast cancer. Eur J Radiol 2023; 158:110661. [PMID: 36542934 DOI: 10.1016/j.ejrad.2022.110661] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To investigate the relationship between fluorodeoxyglucose (FDG) uptake (maximum standardised uptake value [SUVmax]) and immune markers (tumour-infiltrating lymphocytes [TILs] and neutrophil-to-lymphocyte ratio [NLR]) and evaluate the potential prognostic value of any correlations. METHODS Data from 502 patients with breast cancer, including 346 oestrogen receptor (ER)-positive / human epidermal growth factor receptor 2 (HER2)-negative, 88 HER2-positive, and 68 triple-negative cases, who had undergone surgery were reviewed. Relationships between the clinicopathological factors, SUVmax, TILs, NLR, recurrence-free survival (RFS), and overall survival of all patients and each subtype were evaluated using a Cox proportional hazards model and log-rank test. A sub-analysis of patients divided into low and high TIL groups was also undertaken. RESULTS High SUVmax was significantly related to high TILs (p < 0.0001). In low TIL (TILs1) group, patients with high SUVmax (≥3.585) had a significantly shorter RFS than those with low SUVmax (<3.585; p < 0.0001). In high TIL (TILs2,3) group, patients with high SUVmax had a shorter RFS than those with low SUVmax without a significant difference (p = 0.35). Multivariate analysis of 502 patients showed high SUVmax, high T status, and nodal metastasis were independent negative predictors of RFS. In 317 TILs-low patients, high SUVmax, high T status, nodal metastasis, and ER-positivity were independent predictors of RFS. In 185 TILs-high patients, nodal metastasis was an independent predictor of RFS. In ER-positive/HER2-negative and HER2-positive subtypes, SUVmax was a significant predictive parameter in the TILs-low but not TILs-high groups. CONCLUSION FDG uptake may be predictive of immunological features and aggressive features in breast cancer patients.
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Affiliation(s)
| | - Tomoko Higuchi
- Department of Surgery, Division of Breast and Endocrine Surgery, Hyogo College of Medicine, Hyogo, Japan.
| | - Yukie Fujimoto
- Department of Surgery, Division of Breast and Endocrine Surgery, Hyogo College of Medicine, Hyogo, Japan.
| | - Eri Ishikawa
- Department of Surgical Pathology, Hyogo College of Medicine, Hyogo, Japan.
| | | | - Hisashi Komoto
- Department of Radiology, Hyogo College of Medicine, Hyogo, Japan.
| | - Yoshie Inao
- Department of Radiology, Hyogo College of Medicine, Hyogo, Japan.
| | | | - Yasuo Miyoshi
- Department of Surgery, Division of Breast and Endocrine Surgery, Hyogo College of Medicine, Hyogo, Japan.
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Tsai HY, Tsai TY, Wu CH, Chung WS, Wang JC, Hsu JS, Hou MF, Chou MC. Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer. Cancers (Basel) 2022; 14:cancers14246261. [PMID: 36551746 PMCID: PMC9777141 DOI: 10.3390/cancers14246261] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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: 10/19/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7−0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical−radiomics models were trained for each algorithm. Radiomics and clinical−radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer.
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Affiliation(s)
- Huei-Yi Tsai
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Tsung-Yu Tsai
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Chia-Hui Wu
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Wei-Shiuan Chung
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Medical Imaging, Kaohsiung Municipal Siaogang Hospital, Kaohsiung 812, Taiwan
| | - Jo-Ching Wang
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Jui-Sheng Hsu
- Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Ming-Feng Hou
- Department of Biomedical Science and Environmental Biology, College of Life Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Ming-Chung Chou
- Center for Big Data Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Medical Imaging and Radiological Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
- Correspondence: ; Tel.: +886-7-312-1101 (ext. 2357-23)
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Urso L, Manco L, Castello A, Evangelista L, Guidi G, Castellani M, Florimonte L, Cittanti C, Turra A, Panareo S. PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review. Int J Mol Sci 2022; 23:13409. [PMID: 36362190 PMCID: PMC9653918 DOI: 10.3390/ijms232113409] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [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: 09/24/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 08/13/2023] Open
Abstract
Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.
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Affiliation(s)
- Luca Urso
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy
- Nuclear Medicine Unit, Oncological Medical and Specialist Department, University Hospital of Ferrara, 44124 Cona, Italy
| | - Luigi Manco
- Medical Physics Unit, Azienda USL of Ferrara, 44124 Ferrara, Italy
- Medical Physics Unit, University Hospital of Ferrara, 44124 Cona, Italy
| | - Angelo Castello
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Laura Evangelista
- Department of Medicine DIMED, University of Padua, 35128 Padua, Italy
| | - Gabriele Guidi
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy
| | - Massimo Castellani
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Luigia Florimonte
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Corrado Cittanti
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy
- Nuclear Medicine Unit, Oncological Medical and Specialist Department, University Hospital of Ferrara, 44124 Cona, Italy
| | - Alessandro Turra
- Medical Physics Unit, University Hospital of Ferrara, 44124 Cona, Italy
| | - Stefano Panareo
- Nuclear Medicine Unit, Oncology and Haematology Department, University Hospital of Modena, 41125 Modena, Italy
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Funingana I, Piyatissa P, Reinius M, Mccague C, Basu B, Sala E. Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:5076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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Duanmu H, Bhattarai S, Li H, Shi Z, Wang F, Teodoro G, Gogineni K, Subhedar P, Kiraz U, Janssen EAM, Aneja R, Kong J. A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images. Bioinformatics 2022; 38:4605-4612. [PMID: 35962988 PMCID: PMC9525016 DOI: 10.1093/bioinformatics/btac558] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/21/2022] [Accepted: 08/10/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. RESULTS The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. AVAILABILITY AND IMPLEMENTATION The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongyi Duanmu
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | | | - Hongxiao Li
- Department of Mathematics and Statistics and Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zhan Shi
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - George Teodoro
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Keerthi Gogineni
- Department of Hematology-Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA
- Department of Surgery, Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA
- Georgia Cancer Center for Excellence, Grady Health System, Atlanta, GA, USA
| | - Preeti Subhedar
- Georgia Cancer Center for Excellence, Grady Health System, Atlanta, GA, USA
| | - Umay Kiraz
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
| | - Emiel A M Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway
| | - Ritu Aneja
- Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jun Kong
- Department of Mathematics and Statistics and Computer Science, Georgia State University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
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Liu X, Hu X, Yu X, Li P, Gu C, Liu G, Wu Y, Li D, Wang P, Cai J. Frontiers and hotspots of 18F-FDG PET/CT radiomics: A bibliometric analysis of the published literature. Front Oncol 2022; 12:965773. [PMID: 36176388 PMCID: PMC9513237 DOI: 10.3389/fonc.2022.965773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To illustrate the knowledge hotspots and cutting-edge research trends of 18F-FDG PET/CT radiomics, the knowledge structure of was systematically explored and the visualization map was analyzed. Methods Studies related to 18F-FDG PET/CT radiomics from 2013 to 2021 were identified and selected from the Web of Science Core Collection (WoSCC) using retrieval formula based on an interview. Bibliometric methods are mainly performed by CiteSpace 5.8.R3, which we use to build knowledge structures including publications, collaborative and co-cited studies, burst analysis, and so on. The performance and relevance of countries, institutions, authors, and journals were measured by knowledge maps. The research foci were analyzed through research of keywords, as well as literature co-citation analysis. Predicting trends of 18F-FDG PET/CT radiomics in this field utilizes a citation burst detection method. Results Through a systematic literature search, 457 articles, which were mainly published in the United States (120 articles) and China (83 articles), were finally included in this study for analysis. Memorial Sloan-Kettering Cancer Center and Southern Medical University are the most productive institutions, both with a frequency of 17. 18F-FDG PET/CT radiomics–related literature was frequently published with high citation in European Journal of Nuclear Medicine and Molecular Imaging (IF9.236, 2020), Frontiers in Oncology (IF6.244, 2020), and Cancers (IF6.639, 2020). Further cluster profile of keywords and literature revealed that the research hotspots were primarily concentrated in the fields of image, textural feature, and positron emission tomography, and the hot research disease is a malignant tumor. Document co-citation analysis suggested that many scholars have a co-citation relationship in studies related to imaging biomarkers, texture analysis, and immunotherapy simultaneously. Burst detection suggests that adenocarcinoma studies are frontiers in 18F-FDG PET/CT radiomics, and the landmark literature put emphasis on the reproducibility of 18F-FDG PET/CT radiomics features. Conclusion First, this bibliometric study provides a new perspective on 18F-FDG PET/CT radiomics research, especially for clinicians and researchers providing scientific quantitative analysis to measure the performance and correlation of countries, institutions, authors, and journals. Above all, there will be a continuing growth in the number of publications and citations in the field of 18F-FDG PET/CT. Second, the international research frontiers lie in applying 18F-FDG PET/CT radiomics to oncology research. Furthermore, new insights for researchers in future studies will be adenocarcinoma-related analyses. Moreover, our findings also offer suggestions for scholars to give attention to maintaining the reproducibility of 18F-FDG PET/CT radiomics features.
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Affiliation(s)
- Xinghai Liu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Xianwen Hu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xiao Yu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Pujiao Li
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Cheng Gu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Guosheng Liu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- The First Clinical College, Zunyi Medical University, Zunyi, China
| | - Yan Wu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Dandan Li
- Department of Obstetrics, Zunyi Hospital of Traditional Chinese Medicine, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| | - Pan Wang
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
| | - Jiong Cai
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Jiong Cai, ; Pan Wang, ; Dandan Li,
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Derouane F, van Marcke C, Berlière M, Gerday A, Fellah L, Leconte I, Van Bockstal MR, Galant C, Corbet C, Duhoux FP. Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine. Cancers (Basel) 2022; 14:3876. [PMID: 36010869 PMCID: PMC9405974 DOI: 10.3390/cancers14163876] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [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: 07/11/2022] [Revised: 08/05/2022] [Accepted: 08/09/2022] [Indexed: 02/07/2023] Open
Abstract
Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.
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Affiliation(s)
- Françoise Derouane
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Medical Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Cédric van Marcke
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Medical Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Martine Berlière
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Gynecology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Gynecology (GYNE), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Amandine Gerday
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Gynecology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Latifa Fellah
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Radiology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Isabelle Leconte
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Radiology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Mieke R. Van Bockstal
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Pathology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Christine Galant
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Department of Pathology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
| | - Cyril Corbet
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Pharmacology and Therapeutics (FATH), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Francois P. Duhoux
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Breast Clinic, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, 1200 Brussels, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Pole of Medical Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
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Jiang C, Zhao L, Xin B, Ma G, Wang X, Song S. 18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Quant Imaging Med Surg 2022; 12:4135-4150. [PMID: 35919043 PMCID: PMC9338369 DOI: 10.21037/qims-21-1167] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [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: 12/02/2021] [Accepted: 06/06/2022] [Indexed: 12/24/2022]
Abstract
Background Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI. Methods In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models. Results For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively). Conclusions 18F-FDG PET/CT radiomic features integrating clinical factors have potential in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.
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Affiliation(s)
- Chunjuan Jiang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Liwei Zhao
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Guang Ma
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Yoo J, Lee J, Cheon M, Woo SK, Ahn MJ, Pyo HR, Choi YS, Han JH, Choi JY. Predictive Value of (18)F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer. Cancers (Basel) 2022; 14. [PMID: 35454899 DOI: 10.3390/cancers14081987] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/06/2022] [Accepted: 04/13/2022] [Indexed: 12/20/2022] Open
Abstract
We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.
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Umutlu L, Kirchner J, Bruckmann NM, Morawitz J, Antoch G, Ting S, Bittner AK, Hoffmann O, Häberle L, Ruckhäberle E, Catalano OA, Chodyla M, Grueneisen J, Quick HH, Fendler WP, Rischpler C, Herrmann K, Gibbs P, Pinker K. Multiparametric 18F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2022; 14:1727. [PMID: 35406499 DOI: 10.3390/cancers14071727] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The aim of this study was to assess whether multiparametric 18F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. METHODS A total of 73 female patients (mean age 49 years; range 27-77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous 18F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model. RESULTS The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2- group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets. CONCLUSION 18F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2- receptor status.
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Liu S, Li R, Liu Q, Sun D, Yang H, Pan H, Wang L, Song S. Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer. Cancer Biomark 2022; 33:249-259. [PMID: 35213357 DOI: 10.3233/cbm-210201] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND To explore an effective predictive model based on PET/CT radiomics for the prognosis of early-stage uterine cervical squamous cancer. METHODS Preoperative PET/CT data were collected from 201 uterine cervical squamous cancer patients with stage IB-IIA disease (FIGO 2009) who underwent radical surgery between 2010 and 2015. The tumor regions were manually segmented, and 1318 radiomic features were extracted. First, model-based univariate analysis was performed to exclude features with small correlations. Then, the redundant features were further removed by feature collinearity. Finally, the random survival forest (RSF) was used to assess feature importance for multivariate analysis. The prognostic models were established based on RSF, and their predictive performances were measured by the C-index and the time-dependent cumulative/dynamics AUC (C/D AUC). RESULTS In total, 6 radiomic features (5 for CT and 1 for PET) and 6 clinicopathologic features were selected. The radiomic, clinicopathologic and combination prognostic models yielded C-indexes of 0.9338, 0.9019 and 0.9527, and the mean values of the C/D AUC (mC/D AUC) were 0.9146, 0.8645 and 0.9199, respectively. CONCLUSIONS PET/CT radiomics could achieve approval power in predicting DFS in early-stage uterine cervical squamous cancer.
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Affiliation(s)
- Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.,Key Laboratory of Nuclear Physics and Ion-beam Application, Fudan University, Shanghai, China.,Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ruikun Li
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.,Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qiufang Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.,Key Laboratory of Nuclear Physics and Ion-beam Application, Fudan University, Shanghai, China
| | - Dazheng Sun
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Hongxing Yang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.,Key Laboratory of Nuclear Physics and Ion-beam Application, Fudan University, Shanghai, China
| | - Herong Pan
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.,Key Laboratory of Nuclear Physics and Ion-beam Application, Fudan University, Shanghai, China
| | - Lisheng Wang
- SJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai China.,Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Center for Biomedical Imaging, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China.,Key Laboratory of Nuclear Physics and Ion-beam Application, Fudan University, Shanghai, China
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Bouron C, Mathie C, Seegers V, Morel O, Jézéquel P, Lasla H, Guillerminet C, Girault S, Lacombe M, Sher A, Lacoeuille F, Patsouris A, Testard A. Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [ 18F]FDG PET/CT in Early Triple-Negative Breast Cancer. Cancers (Basel) 2022; 14:cancers14030637. [PMID: 35158904 PMCID: PMC8833829 DOI: 10.3390/cancers14030637] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [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: 12/04/2021] [Revised: 01/22/2022] [Accepted: 01/23/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The aim of this study was to evaluate PET/CT parameters to determine different prognostic groups in TNBC, in order to select patients with a high risk of relapse, for whom therapeutic escalation can be considered. We have demonstrated that the MTV, TLG and entropy of the primary breast lesion could be of interest to predict the prognostic outcome of TNBC patients. Abstract (1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose ([18F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [18F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [18F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [18F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [18F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.
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Affiliation(s)
- Clément Bouron
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
- Department of Nuclear Medicine, University Hospital of Angers, 4 rue Larrey, 49100 Angers, France;
- Correspondence:
| | - Clara Mathie
- Department of Medical Oncology, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (C.M.); (A.P.)
| | - Valérie Seegers
- Research and Statistics Department, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France;
| | - Olivier Morel
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Pascal Jézéquel
- Omics Data Science Unit, ICO Pays de la Loire, Bd Jacques Monod, CEDEX, 44805 Saint-Herblain, France; (P.J.); (H.L.)
- CRCINA, UMR 1232 INSERM, Université de Nantes, Université d’Angers, Institut de Recherche en Santé, 8 Quai Moncousu—BP 70721, CEDEX 1, 44007 Nantes, France
| | - Hamza Lasla
- Omics Data Science Unit, ICO Pays de la Loire, Bd Jacques Monod, CEDEX, 44805 Saint-Herblain, France; (P.J.); (H.L.)
| | - Camille Guillerminet
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
- Department of Medical Physics, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France
| | - Sylvie Girault
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Marie Lacombe
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Avigaelle Sher
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Franck Lacoeuille
- Department of Nuclear Medicine, University Hospital of Angers, 4 rue Larrey, 49100 Angers, France;
- CRCINA, University of Nantes and Angers, INSERM UMR1232 équipe 17, 49055 Angers, France
| | - Anne Patsouris
- Department of Medical Oncology, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (C.M.); (A.P.)
- INSERM UMR1232 équipe 12, 49055 Angers, France
| | - Aude Testard
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
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Kuramoto Y, Wada N, Uchiyama Y. Prediction of pathological complete response using radiomics on MRI in patients with breast cancer undergoing neoadjuvant pharmacotherapy. Int J Comput Assist Radiol Surg 2022; 17:619-625. [PMID: 35023018 DOI: 10.1007/s11548-022-02560-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 04/29/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE Neoadjuvant pharmacotherapy is essential for patients with breast cancer who wish to preserve the breast by shrinking the malignant tumor, allowing breast-conserving surgery. It may eliminate cancer cells completely, which is known as pathologic complete response (pCR). Patients with pCR have a lower risk of recurrence. The purpose of this study was to develop a method for predicting patients who achieve pCR by neoadjuvant pharmacotherapy using radiomic features in MR images. METHODS Fat-suppressed T2-weighted MR images of 64 cases were identified from the ISPY1 dataset. There were 26 cases of pCR and 38 cases of non-pCR. The image slice with the largest tumor diameter was selected from MR images, and the tumor region was manually segmented. A total of 371 radiomic features were calculated from the tumor region. We selected nine radiomic features using Lasso in this study. A support vector machine (SVM) with nine radiomic features was used for predicting patients with pCR. RESULTS The result of the ROC analysis showed that the area under the curve of SVM was 0.92 for distinguishing between pCR and non-pCR. Although the input data contain data that were misclassified by SVM, the survival curve classified into the pCR group was at a higher position than the non-pCR group. However, the log-rank test was [Formula: see text]. CONCLUSIONS We developed a method to predict patients with pCR by neoadjuvant pharmacotherapy using noninvasive MR images. The survival curve of patients classified as having pCR by the proposed method was higher than those classified as non-pCR. Since the proposed method predicts patients who achieve pCR by neoadjuvant pharmacotherapy, it enhances the value of preoperative image information.
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Affiliation(s)
- Yuka Kuramoto
- Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, Kumamoto, 862-0976, Japan
| | - Natsumi Wada
- Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, Kumamoto, 862-0976, Japan
| | - Yoshikazu Uchiyama
- Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, Kumamoto, 862-0976, Japan.
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Ozawa H, Fukui K, Komukai S, Y Baden M, Fujita S, Fujita Y, Kimura T, Tokunaga A, Iwahashi H, Kozawa J, Shimomura I. Maximum body mass index before onset of type 2 diabetes is independently associated with advanced diabetic complications. BMJ Open Diabetes Res Care 2021; 9:e002466. [PMID: 34952840 PMCID: PMC8710853 DOI: 10.1136/bmjdrc-2021-002466] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 11/23/2021] [Indexed: 11/15/2022] Open
Abstract
INTRODUCTION The maximum body mass index (BMI) before onset of type 2 diabetes (MBBO) might be used to estimate a patient's insulin secretion capacity. There have been few factors that can predict future diabetic complications at the time of diagnosis of diabetes mellitus. This study aimed to clarify the clinical usefulness of MBBO for predicting the development of advanced diabetic microvascular complications. RESEARCH DESIGN AND METHODS This was a cross-sectional observational study. Of 1304 consecutively admitted patients with type 2 diabetes, we enrolled 435 patients for whom we could confirm their MBBO. Univariate and multivariate logistic regression analyses were performed to examine whether MBBO or BMI on admission was associated with advanced diabetic retinopathy or nephropathy. To evaluate the predictive performance of these indexes, we performed cross-validation in various models with MBBO or BMI and evaluated the areas under the curve (AUCs) yielded by these analyses. RESULTS Univariate analyses suggested that MBBO was associated with advanced retinopathy and nephropathy, while BMI on admission was associated only with advanced nephropathy. In multivariate analyses, MBBO was significantly associated with advanced complications, while BMI on admission was not. For advanced diabetic retinopathy, the AUCs were 0.70-0.72, and for advanced nephropathy, the AUCs were 0.81-0.83. When comparing the AUCs among models, the models with MBBO sustained high predictive performance for diabetic complications. CONCLUSIONS MBBO was independently associated with advanced diabetic complications, while BMI on admission was not. Diabetic microvascular complications in patients with high MBBO could progress more rapidly. At the time of the diagnosis of diabetes mellitus, MBBO would enable us to predict the progress of diabetic complications.
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Affiliation(s)
- Harutoshi Ozawa
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Kenji Fukui
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Sho Komukai
- Department of Integrated Medicine Division of Biomedical Statistics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Megu Y Baden
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Shingo Fujita
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yukari Fujita
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
- Department of Community Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takekazu Kimura
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Ayumi Tokunaga
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hiromi Iwahashi
- Diabetes Center, Toyonaka Municipal Hospital, Toyonaka, Osaka, Japan
| | - Junji Kozawa
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
- Department of Diabetes Care Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Iichiro Shimomura
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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Chen Y, Wang Z, Yin G, Sui C, Liu Z, Li X, Chen W. Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning. Ann Nucl Med 2021. [PMID: 34716873 DOI: 10.1007/s12149-021-01688-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/20/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Human epidermal growth factor receptor 2 (HER2) expression status determination significantly contributes to HER2-targeted therapy in breast cancer (BC). The purpose of this study was to evaluate the role of radiomics and machine learning based on PET/CT images in HER2 status prediction, and to identify the most effective combination of machine learning model and radiomic features. METHODS A total of 217 BC patients who underwent PET/CT examination were involved in the study and randomly divided into a training set (n = 151) and a testing set (n = 66). For all four models, the model parameters were determined using a threefold cross-validation in the training set. Each model's performance was evaluated on the independent testing set using the receiver operating characteristic (ROC) curve, and AUC was calculated to get a quantified performance measurement of each model. RESULTS Among the four developed machine learning models, the XGBoost model outperformed other machine learning models in HER2 status prediction. Furthermore, compared to the XGBoost model based on PET alone or CT alone radiomic features, the predictive power for HER2 status by using XGBoost model based on PET/CTmean or PET/CTconcat radiomic fusion features was dramatically improved with an AUC of 0.76 (95% confidence interval [CI] 0.69-0.83) and 0.72 (0.65-0.80), respectively. CONCLUSIONS The established machine learning classifier based on PET/CT radiomic features is potentially predictive of HER2 status in BC.
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Liu Y, Zhou Q, Song S, Tang S. Integrating metabolic reprogramming and metabolic imaging to predict breast cancer therapeutic responses. Trends Endocrinol Metab 2021; 32:762-775. [PMID: 34340886 DOI: 10.1016/j.tem.2021.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/26/2021] [Accepted: 07/04/2021] [Indexed: 01/10/2023]
Abstract
Metabolic reprogramming is not only an emerging hallmark of cancer, but also an essential regulator of cancer cell adaptation to the microenvironment. Metabolic imaging targeting metabolic signatures has been widely used for breast cancer diagnosis. However, limited implications have been explored for monitoring breast cancer therapy response, although metabolic plasticity is notably associated with therapy resistance. In this review, we focus on the metabolic alterations upon breast cancer therapy and their potential for evaluating breast cancer therapeutic responses. We summarize the metabolic network and regulatory changes upon breast cancer therapy in terms of cancer pathological and genetic differences and discuss the implications of metabolic imaging with various probes in selecting target beneficiaries for precision treatment.
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Affiliation(s)
- Yi Liu
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China; Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China
| | - Qian Zhou
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China.
| | - Shuang Tang
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China; Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 201321, PR China.
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Lin Q, Qi Q, Hou S, Chen Z, Jiang N, Zhang L, Lin C. Application of Pet-CT Fusion Deep Learning Imaging in Precise Radiotherapy of Thyroid Cancer. J Healthc Eng 2021; 2021:2456429. [PMID: 34413967 PMCID: PMC8370813 DOI: 10.1155/2021/2456429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/23/2021] [Indexed: 11/18/2022]
Abstract
This article explores the value of wall F-FDG PET/Cr imaging in the diagnosis of thyroid cancer, studies its ability to distinguish benign and malignant thyroid lesions, and seeks ways to improve the accuracy of diagnosis. The normal control group selected 40 patients who came to our center for physical examination. In the normal control group, the average value of the standard uptake value of both sides of the thyroid was used as the SUV of the thyroid gland and the highest SUV value of the patient's lesion (SUV max) represented the SUV of the lesion. After injection of imaging agent 18F-FD1G, routine imaging was performed at 1h, time-lapse imaging was performed at 2.5 h, and the changes with conventional imaging were compared to infer the benign and malignant lesions. We used SPSS software to carry out statistical analysis, respectively, carrying out analysis of variance, paired t-test, independent sample t-test, and linear correlation analysis. In the thyroid cancer group, 87.5% of the delayed imaging SUV was higher than the conventional imaging SUV, while 83.33% of the benign disease group had a lower SUV than the conventional imaging SUV. 18F-FDG PET/CT imaging has higher sensitivity and specificity for the diagnosis of recurrence or metastasis in patients with Tg positive. However, it has lower sensitivity and specificity for the diagnosis of 131I-Dx-WBS negative DTC and 18F-FDG PET/CT. The specificity increases with the increase of serum Tg level. The above results confirm that 18F-FDG PET/CT imaging is of great significance for the diagnosis of recurrence or metastasis in patients; with PET/CT imaging, the results changed 16.13% of the Tg-positive and 131I-Dx-WBS negative DTC patients' later treatment decision. The decision-making and curative effect evaluation have certain value.
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Affiliation(s)
- Qiuyu Lin
- Nuclear Medicine Department, The First Hospital of Jilin University, Changchun 130000, Jilin, China
| | - Qianle Qi
- Nuclear Medicine Department, The First Hospital of Jilin University, Changchun 130000, Jilin, China
| | - Sen Hou
- Nuclear Medicine Department, The First Hospital of Jilin University, Changchun 130000, Jilin, China
| | - Zhen Chen
- Chengdu Xinke Pharmaceutical Co. Ltd., Chengdu 610000, Sichuan, China
| | - Nan Jiang
- Nuclear Medicine Department, The First Hospital of Jilin University, Changchun 130000, Jilin, China
| | - Lan Zhang
- Biological Sciences, Cornell University, Ithaca 14850, NY, USA
| | - Chenghe Lin
- Nuclear Medicine Department, The First Hospital of Jilin University, Changchun 130000, Jilin, China
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Roy S, Whitehead TD, Li S, Ademuyiwa FO, Wahl RL, Dehdashti F, Shoghi KI. Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer. Eur J Nucl Med Mol Imaging 2021; 49:550-562. [PMID: 34328530 PMCID: PMC8800941 DOI: 10.1007/s00259-021-05489-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [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: 04/27/2021] [Accepted: 07/04/2021] [Indexed: 02/07/2023]
Abstract
Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. Methods TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Results Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. Conclusions We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05489-8.
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Affiliation(s)
- Sudipta Roy
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy D Whitehead
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Shunqiang Li
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Foluso O Ademuyiwa
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Richard L Wahl
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Farrokh Dehdashti
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kooresh I Shoghi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
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Romeo V, Accardo G, Perillo T, Basso L, Garbino N, Nicolai E, Maurea S, Salvatore M. Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives. Cancers (Basel) 2021; 13:cancers13143521. [PMID: 34298733 PMCID: PMC8303777 DOI: 10.3390/cancers13143521] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [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: 06/16/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
- Correspondence: ; Tel.: +39-3930426928; Fax: +39-081-746356
| | - Giuseppe Accardo
- Department of Breast Surgery, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Rionero in Vulture, 85028 Potenza, Italy;
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
| | - Luca Basso
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
| | - Nunzia Garbino
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
| | | | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
| | - Marco Salvatore
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
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Huang Y, Chen W, Zhang X, He S, Shao N, Shi H, Lin Z, Wu X, Li T, Lin H, Lin Y. Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer. Front Bioeng Biotechnol 2021; 9:662749. [PMID: 34295877 PMCID: PMC8291046 DOI: 10.3389/fbioe.2021.662749] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 02/01/2021] [Accepted: 06/07/2021] [Indexed: 01/01/2023] Open
Abstract
Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer. Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer. Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811). Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.
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Affiliation(s)
- Yuhong Huang
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenben Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shaofu He
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Nan Shao
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huijuan Shi
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhenzhe Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xueting Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Tongkeng Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ying Lin
- Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Krajnc D, Papp L, Nakuz TS, Magometschnigg HF, Grahovac M, Spielvogel CP, Ecsedi B, Bago-Horvath Z, Haug A, Karanikas G, Beyer T, Hacker M, Helbich TH, Pinker K. Breast Tumor Characterization Using [ 18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers (Basel) 2021; 13:cancers13061249. [PMID: 33809057 PMCID: PMC8000810 DOI: 10.3390/cancers13061249] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [18F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models. Abstract Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.
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Affiliation(s)
- Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Thomas S. Nakuz
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Heinrich F. Magometschnigg
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Marko Grahovac
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Clemens P. Spielvogel
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | | | - Alexander Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Georgios Karanikas
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
- Correspondence:
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas H. Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Katja Pinker
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
- Memorial Sloan Kettering Cancer Center, Breast Imaging Service, Department of Radiology, New York, NY 10065, USA
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Jiang M, Li CL, Luo XM, Chuan ZR, Lv WZ, Li X, Cui XW, Dietrich CF. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. Eur J Cancer 2021; 147:95-105. [PMID: 33639324 DOI: 10.1016/j.ejca.2021.01.028] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/07/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound. METHODS Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness. RESULTS The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91-0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful. CONCLUSION A deep learning-based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment.
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Yuan Z, Frazer M, Rishi A, Latifi K, Tomaszewski MR, Moros EG, Feygelman V, Felder S, Sanchez J, Dessureault S, Imanirad I, Kim RD, Harrison LB, Hoffe SE, Zhang GG, Frakes JM. Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy. ACTA ACUST UNITED AC 2021; 26:29-34. [PMID: 33948299 DOI: 10.5603/rpor.a2021.0004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 05/28/2020] [Accepted: 12/12/2020] [Indexed: 01/07/2023]
Abstract
Background The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T). Materials and methods An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET. Results The patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 vs. TRG 1-3; 91% accuracy in predicting TRG 0-1 vs. TRG 2-3. For the surrogate of DFS and OS, it resulted in 67.7% accuracy in predicting low vs. intermediate vs. high NAR scores. Conclusion The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation.
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Affiliation(s)
- Zhigang Yuan
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Marissa Frazer
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Anupam Rishi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Kujtim Latifi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | | | - Eduardo G Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Vladimir Feygelman
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Seth Felder
- Department of GI Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Julian Sanchez
- Department of GI Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Sophie Dessureault
- Department of GI Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Iman Imanirad
- Department of GI Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Richard D Kim
- Department of GI Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Louis B Harrison
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Sarah E Hoffe
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Geoffrey G Zhang
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
| | - Jessica M Frakes
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States
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Piñeiro-Fiel M, Moscoso A, Pubul V, Ruibal Á, Silva-Rodríguez J, Aguiar P. A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:380. [PMID: 33672285 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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