Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma.

ABSTRACT: Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. Adult patients that were treated with ICI for advanced melanoma were retrospectively identified from ten participating centers. A deep learning model (DLM) was trained on volumes of lesions on baseline CT to predict clinical benefit. The DLM was compared to and combined with a model of known clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected or

SUBMITTER: Ter Maat LS 

PROVIDER: S-EPMC11686296 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature