Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification

• Published in: International journal for computer assisted radiology and surgery Vol. 17; no. 10; pp. 1867 - 1877
• Main Authors: Ungan, Gulnur, Lavandier, Anne-Flore, Rouanet, Jacques, Hordonneau, Constance, Chauveau, Benoit, Pereira, Bruno, Boyer, Louis, Garcier, Jean-Marc, Mansard, Sandrine, Bartoli, Adrien, Magnin, Benoit
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2022; Springer Nature B.V; Springer Verlag
• Subjects: Accuracy; Biomarkers; Classification; Classifiers; Computed tomography; Computer Imaging; Computer Science; Data augmentation; Data integration; Feature extraction; Feature selection; Health Informatics; Imaging; Immunotherapy; Lesions; Life Sciences; Medical prognosis; Medicine; Medicine & Public Health; Melanoma; Metastasis; Original Article; Patients; Pattern Recognition and Graphics; Pretreatment; Prognosis; Radiology; Radiomics; Segmentation; Support vector machines; Surgery; Tumors; Vision; Metastatic melanoma; Texture analysis; Biomarker; Survival; Immunotherapy
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-022-02662-8

Purpose Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy. Methods We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification. Results The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p -value 0.001). Conclusion This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.