Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
Michael Moore, Isabel D. Friesner, Emanuelle M. Rizk, Benjamin T. Fullerton, Manas Mondal, Megan H. Trager, Karen Mendelson, Ijeuru Chikeka, Tahsin Kurç, Rajarsi Gupta, Bethany R. Rohr, Eric Robinson, Balázs Ács, Rui Chang, Harriet M. Kluger, Bret Taback, Larisa J. Geskin, Basil A. Horst, Kevin Gardner, George Niedt, Jülide Tok Çelebi, Robyn D. Gartrell‐Corrado, Jane L. Messina, Tammie Ferringer, David L. Rimm, Joel Saltz, Jing Wang, R. Vanguri, Yvonne M. Saenger
Abstract
Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan-Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51-11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.