Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms.

ABSTRACT:

Background

The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment.

Methods

We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the

SUBMITTER: Aung TN 

PROVIDER: S-EPMC9272337 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature