Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

ABSTRACT:

Purpose

Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma.

Experimental design

We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Ap

SUBMITTER: Johannet P 

PROVIDER: S-EPMC7785656 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature