Computationally Derived Histologic Features for Predicting Recurrence Risk in Early-Stage Melanoma

Abstract

Melanoma is known as the deadliest skin cancer and mortality rate of recurrent patients is two times that of non-recurrent patients. Although adjuvant immunotherapy can be applied to reduce recurrence risk for early-stage melanoma patients, the existing staging system based on Breslow thickness and ulceration is insufficient to stratify patients who are actually at high risk of recurrence. Histologic features like mitotic figures (MF) and tumor-infiltrating lymphocytes (TIL) are clinically established and computationally validated prognostic factors of melanoma recurrence, but the current clinical workflow of manual extraction of these features is subject to high inter-observer variability. Despite various MF and TIL detection algorithms having been established, the prognostic value of computationally derived histologic engineered features using MF and TIL is yet to be validated in diverse cohorts. This thesis project aims to fill in the gap by leveraging MF- and TIL-derived features for recurrence prediction in the largest early-stage melanoma recurrence prediction cohort reported to date. Six cell morphology features (mitotic ratios, eTIL score, and TIL density variations) are significantly associated with recurrence risk and reflect clinical features of mitotic rate and TIL infiltration type. In addition, a cell morphology risk combining MF and TIL features remains statistically significant in a multi-variate Cox regression model after adjusting for clinical covariates, indicating that a combined risk score of tumor proliferation and immune response encodes independent prognostic significance beyond existing clinical features. This project provides a computational pathology framework for detecting MFs and TILs, engineering cell morphology features, and using these features for early-stage melanoma recurrence risk prediction.