The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study

• Published in: European radiology 2022-12, Vol.32 (12), p.8737-8747
• Main Authors: Wang, Yixin, Lang, Jinwei, Zuo, Joey Zhaoyu, Dong, Yaqin, Hu, Zongtao, Xu, Xiuli, Zhang, Yongkang, Wang, Qinjie, Yang, Lizhuang, Wong, Stephen T. C., Wang, Hongzhi, Li, Hai
• Format: Article
• Language: English
• Subjects: Brain; Brain - diagnostic imaging; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - radiotherapy; Diagnostic Radiology; Feature extraction; Game theory; Health care facilities; Health services; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Metastases; Metastasis; Neuroimaging; Neuroradiology; Oncology; Patients; Radiation therapy; Radiology; Radiomics; Retrospective Studies; Support vector machines; Training; Tumors; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-08887-0

Objective To develop and validate a pretreatment magnetic resonance imaging (MRI)–based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models. Methods We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution. Results Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901–0.949) and 0.851 (95%CI 0.816–0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature’s value affected the feature’s impact attributed to model, and SHAP force plot showed the integration of features’ impact attributed to individual response. Conclusion The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner. Key Points • Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.