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

Date publication

août 2022

Journal

EBioMedicine

Auteurs

Membres identifiés du Cancéropôle Est :
Dr AMARAL Teresa Maria

Tous les auteurs :
Aung TN, Shafi S, Wilmott JS, Nourmohammadi S, Vathiotis I, Gavrielatou N, Fernandez A, Yaghoobi V, Sinnberg T, Amaral T, Ikenberg K, Khosrotehrani K, Osman I, Acs B, Bai Y, Martinez-Morilla S, Moutafi M, Thompson JF, Scolyer RA, Rimm DL

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/35810563

Résumé

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.

Mots clés

Digital image analysis, Early-stage melanoma, Machine learning cell segmentation algorithm, Prognostic marker, Tumor-infiltrating lymphocytes (TILs)

Référence

EBioMedicine. 2022 08;82:104143