Development of A Scoring System via An Interpretable End-to-end Neural Network for Prognostic Stratification of Patients with Advanced Melanoma

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 2,711 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 a separate trial. This score is a weighted summation based on 8 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, and their combinations with BRAF and MEK inhibitors. ScoreNet-based risk stratification can be a new tool for precision medicine.