Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma
العنوان: | Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma |
---|---|
المؤلفون: | Farbod Darvishian, Paul Johannet, Richard L. Shapiro, Ben Minxi, Iman Osman, Hua Zhong, Russell S. Berman, George Jour, Irineu Illa-Bochaca, Margaret M. Chou, Una Moran |
المصدر: | Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc |
سنة النشر: | 2020 |
مصطلحات موضوعية: | 0301 basic medicine, Adult, Male, Skin Neoplasms, Databases, Factual, Biopsy, H&E stain, chemical and pharmacologic phenomena, Risk Assessment, Article, Pathology and Forensic Medicine, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Lymphocytes, Tumor-Infiltrating, Predictive Value of Tests, Risk Factors, Image Interpretation, Computer-Assisted, medicine, Tumor Microenvironment, Humans, Diagnosis, Computer-Assisted, Lymphocyte Count, Melanoma, Aged, Neoplasm Staging, Retrospective Studies, Automation, Laboratory, Microscopy, business.industry, Tumor-infiltrating lymphocytes, Cancer, Reproducibility of Results, hemic and immune systems, Ajcc staging, Middle Aged, medicine.disease, Progression-Free Survival, Clinical Practice, 030104 developmental biology, 030220 oncology & carcinogenesis, Disease Progression, Female, Neoplasm Recurrence, Local, business, Algorithm |
الوصف: | Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the adoption of TIL into clinical practice. Computational image analysis offers a solution to this obstacle, representing a methodological approach for reproducibly counting TIL. We sought to evaluate the ability of a TIL-quantifying machine learning algorithm to predict survival in primary melanoma. Digitized hematoxylin and eosin (HE) slides from prospectively enrolled patients in the NYU melanoma database were scored for % TIL using machine learning and manually graded by pathologists using Clark's model. We evaluated the association of % TIL with recurrence-free survival (RFS) and overall survival (OS) using Cox proportional hazards modeling and concordance indices. Discordance between algorithmic and manual TIL quantification was assessed with McNemar's test and visually by an attending dermatopathologist. In total, 453 primary melanoma patients were scored using machine learning. Automated % TIL scoring significantly differentiated survival using an estimated cutoff of 16.6% TIL (log-rank P 0.001 for RFS; P = 0.002 for OS). % TIL was associated with significantly longer RFS (adjusted HR = 0.92 [0.84-1.00] per 10% increase in % TIL) and OS (adjusted HR = 0.90 [0.83-0.99] per 10% increase in % TIL). In comparison, a subset of the cohort (n = 240) was graded for TIL by melanoma pathologists. However, TIL did not associate with RFS between groups (P 0.05) when categorized as brisk, nonbrisk, or absent. A standardized and automated % TIL scoring algorithm can improve the prognostic impact of TIL. Incorporation of quantitative TIL scoring into the AJCC staging criteria should be considered. |
اللغة: | English |
تدمد: | 1530-0285 0893-3952 |
الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eda44d03b66c872bd7da2b3aba015188Test http://europepmc.org/articles/PMC7983061Test |
حقوق: | OPEN |
رقم الانضمام: | edsair.doi.dedup.....eda44d03b66c872bd7da2b3aba015188 |
قاعدة البيانات: | OpenAIRE |
تدمد: | 15300285 08933952 |
---|