Development of a scoring system via an interpretable end-to-end neural network for prognostic stratification of patients with advanced melanoma

Abstract We created an interpretable end-to-end neural network (ScoreNet) which can be used to develop an easy-to-implement scoring system for risk stratification. We applied it to data from 2711 patients with advanced melanoma across 5 trials that supported new drug approvals and developed a scoring system for overall survival (OS) and progression-free survival (PFS). The scoring system was then validated in two separate trials. This score is a weighted summation based on eight baseline features (sum of longest diameter for all target lesions, lactate dehydrogenase, systemic immune inflammation index, hemoglobin, ECOG performance status, liver metastasis, pulse rate, and PD-L1 immunohistochemistry status). Using the score, patients can be classified into low, moderate, or high-risk categories of reduced OS and PFS. Risk stratification was demonstrated for patients treated with PD-1/PD-L1 inhibitors, chemotherapy, CTLA-4 inhibitors, BRAF or MEK inhibitors, and their combinations. ScoreNet-based risk stratification can be a new tool for clinical research/practice.