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Objective Assessment of Tumor Infiltrating Lymphocytes as a Prognostic Marker in Melanoma using Machine Learning Algorithms

ABSTRACT: Tumor infiltrating lymphocytes scores visually assessed by the pathologists have not been broadly clinically implemented due to the lack of reproducibility caused by subjective assessment between pathologists and institutions. Automated TIL% (eTILs%) score, defined by the machine learning algorithm NN192, developed using open-source software, QuPath, has been shown to be prognostic in melanoma but its clinical utility has not yet been broadly proven. Added value of this study This study pools patients with melanoma from a series of international cohorts and supports the previous finding that automated TIL score (eTIL%) is an independent prognostic marker in primary melanoma patients. Implications of all the available evidence we additionally show that the prognostic performance of eTIL% is stage specific. The use of NN192 machine learning algorithm could be a valuable and easy-to-implement tool for prospective testing of patients with early-stage melanoma and could be validated as a selector for patients that can safely omit immunotherapy in the adjuvant setting.

ORGANISM(S): Homo sapiens (human)

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PROVIDER: S-BIAD470 | bioimages |

REPOSITORIES: bioimages

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