Multi-modal computational data analysis for prediction of response to systemic treatment in metastatic melanoma.

INTRODUCTION

Novel therapies like immunotherapy (ICI) and targeted therapy (TT) have significantly improved survival in metastatic melanoma, but treatment failure and/or development of treatment resistance pose major challenges. Radiomics has been shown to be a valuable tool for imaging-based and/or metadata-based (clinomics), non-invasive data analysis to predict therapy response in oncologic patients. This study aims to evaluate the predictive performance of clinical, radiomics, radio-clinomics, and combined radio-clinomics + body composition analysis (BCA) features for treatment response in metastatic melanoma patients undergoing ICI or TT.

METHODS

A retrospective study was conducted on 120 metastatic melanoma patients receiving ICI or TT. Inclusion criteria comprised a minimum of one RECIST 1.1.-identified lesion in pre- and posttherapeutic CT scans as well as metadata including therapy information and clinical features. Radiomics features and BCA biomarkers were extracted. Lesion- and patient-specific models based on clinical, radiomics, radio-clinomics, and BCA parameters were trained twice: with and without therapy information (type and line).

RESULTS

All patient-based models showed improved AUCs after inclusion of therapy information. The clinical model achieved modest AUC of 0.657, when compared to imaging included models with AUCs ranging from 0.92 (radiomics) to 0.97 (radio-clinomics + BCA). The lesion-specific radiomics model showed variable performance spanning from poor AUC of 0.76 in peritoneum/soft-tissue lesions to excellent AUC in liver lesions (AUC 0.98).

CONCLUSION

Our results demonstrate that integrating multi-modal data, particularly therapy information and automated BCA features, substantially improves the accuracy of treatment response prediction, highlighting the potential for improved patient stratification and personalized oncologic care.