Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification
Résumé
Purpose
Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict if 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-PD1 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 vs >1 year and treatment response vs no response were 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 Neighbour, Logistic Regression (LR)) used with or without SMOTE data augmentation. A 5-fold cross-validation was performed at the patient level, with a tumour-based classification.
Results
The highest accuracy level for OS prediction 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 tumour with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.
Domaines
Médecine humaine et pathologie
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MM texture IJCARS 20220428 AB tout en un fichier.pdf (1.01 Mo)
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