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

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.

CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma.

Introduction: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumour characteristics on readily available computed tomography (CT) imaging.

The clinical course and treatment of black mamba ( envenomations: a narrative review.

Context: The black mamba ( is, due to its extremely toxic venom, one of the most dangerous snake species in Sub-Saharan Africa. A bite is a medical emergency and requires adequate action to prevent severe complications. However, there are no comprehensive reviews available based on clinical cases, and no readily accessible guidelines for standardized treatment.