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Zheng X, Liu K, Gao Z, Li C, Tong L, Rong C, Li S, Liu Y, Wu X. Predicting overall survival and prophylactic cranial irradiation benefit in small cell lung cancer patients: a multicenter cohort study. BMC Cancer 2024; 24:1507. [PMID: 39643886 PMCID: PMC11622659 DOI: 10.1186/s12885-024-13274-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 11/29/2024] [Indexed: 12/09/2024] Open
Abstract
BACKGROUND To construct a CT-based radiomics nomogram, enabling the estimation of overall survival (OS) in small cell lung cancer (SCLC) patients and facilitating the identification of prophylactic cranial irradiation (PCI) beneficiaries through risk stratification using the radiomics score (RS). METHODS A retrospective recruitment of 375 patients with pathologically confirmed SCLC was conducted across three medical centers, followed by their division into different cohorts. To generate the RS, a series of analyses were performed, including Pearson correlation analysis, univariate Cox analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Subsequently, patients were stratified into either the low RS or high RS group, determined by identifying the optimal RS cutoff value. Subsequently, a radiomics nomogram was constructed using the RS, followed by assessments of its discrimination, calibration, clinical utility and reclassification. Moreover, we evaluated the potential benefits of PCI following stratification by RS. RESULTS For the internal and external validation cohorts, the radiomics nomogram (concordance index [C-index]: 0.770, 0.763) outperformed clinical nomogram (C-index: 0.625, 0.570) in predicting OS. Besides, patients with high RS had survival benefit from PCI in both the limited and extensive stage (hazard ratio [HR]: 0.304, 95% confidence interval [CI]: 0.087-1.065, P = 0.003; HR: 0.481, 95% CI: 0.270-0.860, P = 0.019, respectively), while no significant association were observed in patients with low RS. CONCLUSION A radiomics nomogram based on CT shows potential in predicting OS for individuals with SCLC. The RS could assist in tailoring treatment plans to identify patients likely to benefit from PCI.
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Affiliation(s)
- Xiaomin Zheng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230031, China
| | - Kaicai Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230031, China
| | - Zhao Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230031, China
| | - Cuiping Li
- Department of Radiology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, China
- Department of Radiology, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Li Tong
- Department of Radiology, The Third Affiliated Hospital of Anhui Medical University, Hefei, 230061, China
| | - Chang Rong
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230031, China
| | - Shuai Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230031, China
| | - Yichao Liu
- Department of Radiology, The Affiliated Bozhou Hospital of Anhui Medical University, No. 616 Duzhong Road, Qiaocheng District, Bozhou, Anhui Province, 236000, China.
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, Anhui Province, 230031, China.
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Li L, Qin Z, Bo J, Hu J, Zhang Y, Qian L, Dong J. Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy. Insights Imaging 2024; 15:284. [PMID: 39613907 DOI: 10.1186/s13244-024-01853-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 10/28/2024] [Indexed: 12/01/2024] Open
Abstract
OBJECTIVES To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses. METHODS A total of 170 patients with pathologically and endoscopically confirmed proximal esophageal cancer from two centers were enrolled. Radiomics models were established by five machine learning approaches. The optimal radiomics model was selected using receiver operating curve analysis. Bioinformatics methods were applied to explore the potential biological mechanisms. Nomograms based on radiomics and clinical-radiomics features were constructed and assessed by receiver operating characteristics, calibration, and decision curve analyses net reclassification improvement, and integrated discrimination improvement evaluations. RESULTS The peritumoral models performed well with the majority of classifiers in the training and validation sets, with the dual-region radiomics model showing the highest integrated area under the curve values of 0.9763 and 0.9471, respectively, and outperforming the single-region models. The clinical-radiomics nomogram showed better predictive performance than the clinical nomogram, with a net reclassification improvement of 34.4% (p = 0.02) and integrated discrimination improvement of 10% (p = 0.007). Gene ontology enrichment analysis revealed that lipid metabolism-related functions are potentially crucial in the process by which the radiomics score could stratify patients. CONCLUSIONS A combination of peritumoral radiomics features could improve the predictive performance of intratumoral radiomics to estimate overall survival after definitive chemoradiotherapy in patients with proximal esophageal cancer. Radiomics features could provide insights into the lipid metabolism associated with radioresistance and hold great potential to guide personalized care. CRITICAL RELEVANCE STATEMENT This study demonstrates that incorporating peritumoral radiomics features enhances the predictive accuracy of overall survival in proximal esophageal cancer patients after chemoradiotherapy, and suggests a link between radiomics and lipid metabolism in radioresistance, highlighting its potential for personalized treatment strategies. KEY POINTS Peritumoral region radiomics features could predict the prognosis of proximal esophageal cancer. Dual-region radiomics features showed significantly better predictive performance. Radiomics features can provide insights into the lipid metabolism associated with radioresistance.
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Affiliation(s)
- Linrui Li
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
- Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Zhihui Qin
- Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Juan Bo
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Jiaru Hu
- Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Yu Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Liting Qian
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China.
- Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Hefei, China.
| | - Jiangning Dong
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China.
- Department of Radiology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Hefei, China.
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Wang F, Chen W, Chen F, Lu J, Xu Y, Fang M, Jiang H. Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics. Sci Rep 2024; 14:22659. [PMID: 39349536 PMCID: PMC11442625 DOI: 10.1038/s41598-024-73331-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 09/16/2024] [Indexed: 10/02/2024] Open
Abstract
The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient's images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.
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Affiliation(s)
- Fang Wang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Wujie Chen
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Fangmin Chen
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Jinlan Lu
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Yanjun Xu
- Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Min Fang
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China
| | - Haitao Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, Zhejiang, China.
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Zheng X, Liu K, Shen N, Gao Y, Zhu C, Li C, Rong C, Li S, Qian B, Li J, Wu X. Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study. Radiother Oncol 2024; 195:110221. [PMID: 38479441 DOI: 10.1016/j.radonc.2024.110221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/26/2024] [Accepted: 03/07/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND AND PURPOSE To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. MATERIALS AND METHODS This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. RESULTS Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. CONCLUSIONS The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.
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Affiliation(s)
- Xiaomin Zheng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China; Department of Radiation Oncology, Anhui Provincial Cancer Hospital, Hefei 230031, China
| | - Kaicai Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China; Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China
| | - Na Shen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Chang Rong
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Shuai Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Baoxin Qian
- Huiying Medical Technology, Beijing 100192, China
| | - Jianying Li
- CT Advanced Application, GE HealthCare China, Beijing 100186, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, China.
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Shi X, Wang P, Li Y, Xu J, Yin T, Teng F. Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer. Thorac Cancer 2024; 15:738-748. [PMID: 38376861 PMCID: PMC10961221 DOI: 10.1111/1759-7714.15259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Brain metastases (BMs) are common in small cell lung cancer (SCLC), and the efficacy of immune checkpoint inhibitors (ICIs) in these patients is uncertain. In this study we aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) for intracranial efficacy prediction of ICIs in patients with BMs from SCLC. METHODS The training and validation cohorts consisted of 101 patients from two centers. The interclass correlation coefficient (ICC), logistic univariate regression analysis, and random forest were applied to select the radiomic features, generating the radiomics score (Rad-score) through the formula. Using multivariable logistic regression analysis, a nomogram was created by the combined model. The discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. Kaplan-Meier curves were plotted based on the nomogram scores. RESULTS Ten radiomic features were selected for calculating the Rad-score as they could differentiate the intracranial efficacy in the training (area under the curve [AUC], 0.759) and the validation cohort (AUC, 0.667). A nomogram was created by combining Rad-score, treatment lines, and neutrophil-to-lymphocyte ratio (NLR). The training cohort obtained an AUC of 0.878 for the combined model, verified in the validation cohort (AUC = 0.875). Kaplan-Meier analyses showed the nomogram was associated with progression-free survival (PFS) (p = 0.0152) and intracranial progression-free survival (iPFS) (p = 0.0052) but not overall survival (OS) (p = 0.4894). CONCLUSION A radiomics nomogram model for predicting the intracranial efficacy of ICIs in SCLC patients with BMs can provide suggestions for exploring individual-based treatments for patients.
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Affiliation(s)
- Xiaonan Shi
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Peiliang Wang
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of MedicineShandong UniversityJinanChina
| | - Yikun Li
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Junhao Xu
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Tianwen Yin
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Feifei Teng
- Department of Radiation OncologyShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesJinanChina
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Jinnouchi H, Yamashita H, Nozawa Y, Nakamoto T, Sawayanagi S, Katano A. Prognostic value of radiomic features in patients with esophageal cancer treated with chemoradiotherapy. J Cancer Res Ther 2024; 20:243-248. [PMID: 38554328 DOI: 10.4103/jcrt.jcrt_1627_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 04/01/2024]
Abstract
BACKGROUND The aim of the present study was to evaluate the prognostic value of radiomic features in patients who underwent chemoradiotherapy for esophageal cancer. METHODS In this retrospective study, two independent cohorts of esophageal cancer patients treated with chemoradiotherapy were included. Radiomics features of each patient were extracted from pre-treatment computed tomography (CT) images. Radiomic features were selected by employing univariate and multivariate analyses in the test cohort. Selected radiomic features were verified in the validation cohort. The endpoint of the present study was overall survival. RESULTS A total of 101 esophageal cancer patients were included in our study, with 71 patients in the test cohort and 30 patients in the validation cohort. Univariate analysis identified 158 radiomic features as prognostic factors for overall survival in the test cohort. A multivariate analysis revealed that root mean squared and Low-High-High (LHH) median were prognostic factors for overall survival with a hazard ratio of 2.23 (95% confidence interval [CI]: 1.16-4.70, P = 0.017) and 0.26 (95% CI: 0.13-0.54, P < 0.001), respectively. In the validation cohort, root mean squared high/LHH median low group had the most preferable prognosis with a median overall survival of 73.30 months (95% CI: 32.13-NA), whereas root mean squared low/LHH median low group had the poorest prognosis with a median overall survival of 9.72 months (95% CI: 2.50-NA), with a P value of < 0.001. CONCLUSIONS We identified two radiomic features that might be independent prognostic factors of overall survival of esophageal cancer patients treated with chemoradiotherapy.
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Affiliation(s)
- Haruka Jinnouchi
- Departments of Radiology, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan
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Zheng X, Liu K, Li C, Zhu C, Gao Y, Li J, Wu X. A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study. LA RADIOLOGIA MEDICA 2023; 128:1386-1397. [PMID: 37597124 DOI: 10.1007/s11547-023-01702-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/10/2023] [Indexed: 08/21/2023]
Abstract
PURPOSE To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation. METHODS 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.
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Affiliation(s)
- Xiaomin Zheng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Kaicai Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Jianying Li
- CT Advanced Application, GE HealthCare China, Beijing, 100186, People's Republic of China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China.
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Madani MH, Riess JW, Brown LM, Cooke DT, Guo HH. Imaging of lung cancer. Curr Probl Cancer 2023:100966. [PMID: 37316337 DOI: 10.1016/j.currproblcancer.2023.100966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/29/2023] [Accepted: 05/23/2023] [Indexed: 06/16/2023]
Abstract
Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imaging appearances. The most frequently used imaging modalities include chest radiography, computed tomography, magnetic resonance imaging, and positron emission tomography. Artificial intelligence algorithms and radiomics are emerging technologies with potential applications in lung cancer imaging.
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Affiliation(s)
- Mohammad H Madani
- Department of Radiology, University of California, Davis, Sacramento, CA.
| | - Jonathan W Riess
- Division of Hematology/Oncology, Department of Internal Medicine, UC Davis Medical Center, UC Davis Comprehensive Cancer Center, Sacramento, CA
| | - Lisa M Brown
- Division of General Thoracic Surgery, Department of Surgery, UC Davis Health, Sacramento, CA
| | - David T Cooke
- Division of General Thoracic Surgery, Department of Surgery, UC Davis Health, Sacramento, CA
| | - H Henry Guo
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
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Su Y, Lu C, Zheng S, Zou H, Shen L, Yu J, Weng Q, Wang Z, Chen M, Zhang R, Ji J, Wang M. Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram. Front Oncol 2023; 13:1006172. [PMID: 37007144 PMCID: PMC10061075 DOI: 10.3389/fonc.2023.1006172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
ObjectivesTo develop and validate a CT-based radiomics nomogram that can provide individualized pretreatment prediction of the response to platinum treatment in small cell lung cancer (SCLC).MaterialsA total of 134 SCLC patients who were treated with platinum as a first-line therapy were eligible for this study, including 51 patients with platinum resistance (PR) and 83 patients with platinum sensitivity (PS). The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were applied for feature selection and model construction. The selected texture features were calculated to obtain the radiomics score (Rad-score), and the predictive nomogram model was composed of the Rad-score and the clinical features selected by multivariate analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to assess the performance of the nomogram.ResultsThe Rad-score was calculated using 10 radiomic features, and the resulting radiomics signature demonstrated good discrimination in both the training set (area under the curve [AUC], 0.727; 95% confidence interval [CI], 0.627–0.809) and the validation set (AUC, 0.723; 95% CI, 0.562–0.799). To improve diagnostic effectiveness, the Rad-score created a novel prediction nomogram by combining CA125 and CA72-4. The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.900; 95% CI, 0.844-0.947) and the validation set (AUC, 0.838; 95% CI, 0.534-0.735). The radiomics nomogram proved to be clinically beneficial based on decision curve analysis.ConclusionWe developed and validated a radiomics nomogram model for predicting the response to platinum in SCLC patients. The outcomes of this model can provide useful suggestions for the development of tailored and customized second-line chemotherapy regimens.
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Affiliation(s)
- Yanping Su
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, Institute of Aging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Alzheimer’s Disease of Zhejiang, Wenzhou, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Shenfei Zheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Hao Zou
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Lin Shen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Junchao Yu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Zufei Wang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Ran Zhang
- AI Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
- *Correspondence: Meihao Wang, ; Jiansong Ji,
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, Institute of Aging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Alzheimer’s Disease of Zhejiang, Wenzhou, Zhejiang, China
- *Correspondence: Meihao Wang, ; Jiansong Ji,
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Qu H, Zhai H, Zhang S, Chen W, Zhong H, Cui X. Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases. Front Oncol 2023; 13:992096. [PMID: 36814812 PMCID: PMC9939899 DOI: 10.3389/fonc.2023.992096] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/12/2023] [Indexed: 02/08/2023] Open
Abstract
Background and objective For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions. Methods In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS). Results For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis). Conclusions This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.
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Affiliation(s)
- Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, P.R, China
| | - Huan Zhai
- Department of Interventional Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China,Key Laboratory of Diagnostic Imaging and Interventional Radiology of Liaoning Province, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Shuairan Zhang
- Department of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Wenjuan Chen
- Department of Medical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Hongshan Zhong
- Department of Interventional Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China,Key Laboratory of Diagnostic Imaging and Interventional Radiology of Liaoning Province, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China,*Correspondence: Xiaoyu Cui, ; Hongshan Zhong,
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, P.R, China,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China,*Correspondence: Xiaoyu Cui, ; Hongshan Zhong,
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11
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Shao C, Zhang J, Guo J, Zhang L, Zhang Y, Ma L, Gong C, Tian Y, Chen J, Yu N. A radiomics nomogram model for predicting prognosis of pancreatic ductal adenocarcinoma after high-intensity focused ultrasound surgery. Int J Hyperthermia 2023; 40:2184397. [PMID: 36888994 DOI: 10.1080/02656736.2023.2184397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023] Open
Abstract
OBJECTIVE To develop and validate a radiomics nomogram for predicting the survival of patients with pancreatic ductal adenocarcinoma (PDAC) after receiving high-intensity focused ultrasound (HIFU) treatment. METHODS A total of 52 patients with PDAC were enrolled. To select features, the least absolute shrinkage and selection operator algorithm were applied, and the radiomics score (Rad-Score) was obtained. Radiomics model, clinics model, and radiomics nomogram model were constructed by multivariate regression analysis. The identification, calibration, and clinical application of nomogram were evaluated. Survival analysis was performed using Kaplan-Meier (K-M) method. RESULTS According to conclusions made from the multivariate Cox model, Rad-Score, and tumor size were independent risk factors for OS. Compared with the clinical model and radiomics model, the combination of Rad-Score and clinicopathological factors could better predict the survival of patients. Patients were divided into high-risk and low-risk groups according to Rad-Score. K-M analysis showed that the difference between the two groups was statistically significant (p < 0.05). In addition, the radiomics nomogram model indicated better discrimination, calibration, and clinical practicability in training and validation cohorts. CONCLUSION The radiomics nomogram effectively evaluates the prognosis of patients with advanced pancreatic cancer after HIFU surgery, which could potentially improve treatment strategies and promote individualized treatment of advanced pancreatic cancer.
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Affiliation(s)
- Changjie Shao
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | | | - Jing Guo
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Liang Zhang
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yuhan Zhang
- University of Southern California, Los Angeles, CA, USA
| | - Leiyuan Ma
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chuanxin Gong
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yaqi Tian
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ning Yu
- Department of Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
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12
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Ma ZP, Li XL, Gao K, Zhang TL, Wang HD, Zhao YX. Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1333-1340. [PMID: 37840466 DOI: 10.3233/xst-230189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
OBJECTIVE To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC). METHODS 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness. RESULTS LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05). CONCLUSION The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.
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Affiliation(s)
- Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Xiao-Lei Li
- Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang City, Hebei Province, China
| | - Kai Gao
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Tian-Le Zhang
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Heng-Di Wang
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
| | - Yong-Xia Zhao
- Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China
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Alilou M, Khorrami M, Prasanna P, Bera K, Gupta A, Viswanathan VS, Patil P, Velu PD, Fu P, Velcheti V, Madabhushi A. A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors. SCIENCE ADVANCES 2022; 8:eabq4609. [PMID: 36427313 PMCID: PMC9699671 DOI: 10.1126/sciadv.abq4609] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 10/06/2022] [Indexed: 05/30/2023]
Abstract
Tumor vasculature is a key component of the tumor microenvironment that can influence tumor behavior and therapeutic resistance. We present a new imaging biomarker, quantitative vessel tortuosity (QVT), and evaluate its association with response and survival in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitor (ICI) therapies. A total of 507 cases were used to evaluate different aspects of the QVT biomarkers. QVT features were extracted from computed tomography imaging of patients before and after ICI therapy to capture the tortuosity, curvature, density, and branching statistics of the nodule vasculature. Our results showed that QVT features were prognostic of OS (HR = 3.14, 0.95% CI = 1.2 to 9.68, P = 0.0006, C-index = 0.61) and could predict ICI response with AUCs of 0.66, 0.61, and 0.67 on three validation sets. Our study shows that QVT imaging biomarker could potentially aid in predicting and monitoring response to ICI in patients with NSCLC.
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Affiliation(s)
- Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | | | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Amit Gupta
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH 44106, USA
| | | | - Pradnya Patil
- Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Priya Darsini Velu
- Pathology and Laboratory Medicine, Weill Cornell Medicine Physicians, New York, NY 10021, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH 44106, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY 10016, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, USA
- Atlanta Veterans Administration Medical Center, Atlanta, GA 30322, USA
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Viswanathan VS, Khorrami M, Jazieh K, Fu P, Pennell N, Madabhushi A. Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by Zheng et al. J Immunother Cancer 2022; 10:jitc-2022-005086. [PMID: 35640931 PMCID: PMC9157364 DOI: 10.1136/jitc-2022-005086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
| | | | - Khalid Jazieh
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Pingfu Fu
- Population and Quantitattive Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Nathan Pennell
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Xiao C, Zhou M, Yang X, Wang H, Tang Z, Zhou Z, Tian Z, Liu Q, Li X, Jiang W, Luo J. Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images. Front Oncol 2022; 12:844067. [PMID: 35433467 PMCID: PMC9010865 DOI: 10.3389/fonc.2022.844067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesMetachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images.MethodsSix-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the validation cohort. The performance of the integrated nomogram was assessed with respect to its calibration, discrimination, and clinical application value.ResultsPatients were divided into low- and high-LM risk score groups according to the cutoff value and significant differences were observed in the LM of the different risk score groups in the training and validation cohorts (P<0.001). Multivariable analysis revealed that the LM risk score, VELIPI, pT stage and pN stage were independent predictors of LM. Then, the prediction model was developed and presented as a nomogram to predict the 1-, 2-, and 3-year probability of LM. The integrated nomogram achieved satisfactory discrimination, with C-indexes of 0.807 (95% CI: 0.787, 0.827) and 0.812 (95% CI: 0.773, 0.850) and AUCs of 0.840 (95% CI: 0.795, 0.885) and 0.848 (95% CI: 0.766, 0.931) in the training and validation cohorts, respectively. Favorable calibration of the nomogram was confirmed in the training and validation cohorts. Integrated discrimination improvement and net reclassification index indicated that the integrated nomogram was superior to the traditional clinicopathological model. Decision curve analysis confirmed that the nomogram has clinical application value.ConclusionsThe LM risk score based on ResNet-50 and digital HE images was significantly associated with LM. The integrated nomogram could identify stage I-III CRC patients at high risk of LM after primary colectomy, so it may serve as a potential tool to choose the appropriate treatment to improve the prognosis of stage I-III CRC patients.
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Affiliation(s)
- Chanchan Xiao
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Microbiology and Immunology, Institute of Geriatric Immunology, School of Medicine, Jinan University, Guangzhou, China
| | - Meihua Zhou
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xihua Yang
- Department of Surgical Oncology, Chenzhou No. 1 People’s Hospital, Chenzhou, China
| | - Haoyun Wang
- Department of Microbiology and Immunology, Institute of Geriatric Immunology, School of Medicine, Jinan University, Guangzhou, China
| | - Zhen Tang
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Zheng Zhou
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Zeyu Tian
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Qi Liu
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xiaojie Li
- Department of Pathology, Chenzhou No. 1 People’s Hospital, Chenzhou, China
| | - Wei Jiang
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
- Department of Surgical Oncology, Chenzhou No. 1 People’s Hospital, Chenzhou, China
- *Correspondence: Jihui Luo, ; Wei Jiang,
| | - Jihui Luo
- Department of General Surgery, Hunan Provincial People’s Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, China
- *Correspondence: Jihui Luo, ; Wei Jiang,
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Chen N, Li R, Jiang M, Guo Y, Chen J, Sun D, Wang L, Yao X. Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT. Front Med (Lausanne) 2022; 9:833283. [PMID: 35280863 PMCID: PMC8911879 DOI: 10.3389/fmed.2022.833283] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Purposes and Objectives The aim of this study was to predict the progression-free survival (PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast-enhanced computed tomography (CT). Methods A total of 186 cases with pathological confirmed small cell lung cancer were retrospectively assembled. First, 1,218 radiomic features were automatically extracted from tumor region of interests (ROIs) on the lung window and mediastinal window, respectively. Then, the prognostic and robust features were selected by machine learning methods, such as (1) univariate analysis based on a Cox proportional hazard (CPH) model, (2) redundancy removing using the variance inflation factor (VIF), and (3) multivariate importance analysis based on random survival forests (RSF). Finally, PFS predictive models were established based on RSF, and their performances were evaluated using the concordance index (C-index) and the cumulative/dynamic area under the curve (C/D AUC). Results In total, 11 radiomic features (6 for mediastinal window and 5 for lung window) were finally selected, and the predictive model constructed from them achieved a C-index of 0.7531 and a mean C/D AUC of 0.8487 on the independent test set, better than the predictions by single clinical features (C-index = 0.6026, mean C/D AUC = 0.6312), and single radiomic features computed in lung window (C-index = 0.6951, mean C/D AUC = 0.7836) or mediastinal window (C-index = 0.7192, mean C/D AUC = 0.7964). Conclusion The radiomic features computed from tumor ROIs on both lung window and mediastinal window can predict the PFS for patients with SCLC by a high accuracy, which could be used as a useful tool to support the personalized clinical decision for the diagnosis and patient management of patients with SCLC.
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Affiliation(s)
- Ningxin Chen
- Department of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Ruikun Li
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Mengmeng Jiang
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Yixian Guo
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Jiejun Chen
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Dazhen Sun
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Lisheng Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Xiuzhong Yao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital of Fudan University, Shanghai, China
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Jazieh K, Khorrami M, Saad A, Gad M, Gupta A, Patil P, Viswanathan VS, Rajiah P, Nock CJ, Gilkey M, Fu P, Pennell NA, Madabhushi A. Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab. J Immunother Cancer 2022; 10:e003778. [PMID: 35256515 PMCID: PMC8905876 DOI: 10.1136/jitc-2021-003778] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The landmark study of durvalumab as consolidation therapy in NSCLC patients (PACIFIC trial) demonstrated significantly longer progression-free survival (PFS) in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC) treated with durvalumab (immunotherapy, IO) therapy after chemoradiotherapy (CRT). In clinical practice in the USA, durvalumab continues to be used in patients across all levels of programmed cell death ligand-1 (PD-L1) expression. While immune therapies have shown promise in several cancers, some patients either do not respond to the therapy or have cancer recurrence after an initial response. It is not clear so far who will benefit of this therapy or what the mechanisms behind treatment failure are. METHODS A total of 133 patients with unresectable stage III NSCLC who underwent durvalumab after CRT or CRT alone were included. Patients treated with durvalumab IO after CRT were randomly split into training (D1=59) and test (D2=59) sets and the remaining 15 patients treated with CRT alone were grouped in D3. Radiomic textural patterns from within and around the target nodules were extracted. A radiomic risk score (RRS) was built and was used to predict PFS and overall survival (OS). Patients were divided into high-risk and low-risk groups based on median RRS. RESULTS RRS was found to be significantly associated with PFS in D1 (HR=2.67, 95% CI 1.85 to 4.13, p<0.05, C-index=0.78) and D2 (HR=2.56, 95% CI 1.63 to 4, p<0.05, C-index=0.73). Similarly, RRS was associated with OS in D1 (HR=1.89, 95% CI 1.3 to 2.75, p<0.05, C-index=0.67) and D2 (HR=2.14, 95% CI 1.28 to 3.6, p<0.05, C-index=0.69), respectively. RRS was found to be significantly associated with PFS in high PD-L1 (HR=3.01, 95% CI 1.41 to 6.45, p=0.0044) and low PD-L1 (HR=2.74, 95% CI 1.8 to 4.14, p=1.77e-06) groups. Moreover, RRS was not significantly associated with OS in the high PD-L1 group (HR=2.08, 95% CI 0.98 to 4.4, p=0.054) but was significantly associated with OS in the low PD-L1 group (HR=1.61, 95% CI 1.14 to 2.28, p=0.0062). In addition, RRS was significantly associated with PFS (HR=2.77, 95% CI 1.17 to 6.52, p=0.019, C-index=0.77) and OS (HR=2.62, 95% CI 1.25 to 5.51, p=0.01, C-index=0.77) in D3, respectively. CONCLUSIONS Tumor radiomics of pretreatment CT images from patients with stage III unresectable NSCLC were prognostic of PFS and OS to CRT followed by durvalumab IO and CRT alone.
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Affiliation(s)
- Khalid Jazieh
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Anas Saad
- Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mohamed Gad
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio, USA
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Pradnya Patil
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | | | | | - Charles J Nock
- Louis Stokes Cleveland VA Medical Center Mental Health Services, Cleveland, Ohio, USA
| | - Michael Gilkey
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Nathan A Pennell
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Louis Stokes Cleveland VA Medical Center Mental Health Services, Cleveland, Ohio, USA
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