Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models.

ABSTRACT:

Introduction

Melanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images.

Methods

To identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased cli

SUBMITTER: Couetil J 

PROVIDER: S-EPMC9853175 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature