1. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer
- Author
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Thagaard J, Broeckx G, Page DB, Jahangir CA, Verbandt S, Kos Z, Gupta R, Khiroya R, Abduljabbar K, Acosta Haab G, Acs B, Akturk G, Almeida JS, Alvarado-Cabrero I, Amgad M, Azmoudeh-Ardalan F, Badve S, Baharun NB, Balslev E, Bellolio ER, Bheemaraju V, Blenman KR, Botinelly Mendonça Fujimoto L, Bouchmaa N, Burgues O, Chardas A, Chon U Cheang M, Ciompi F, Cooper LA, Coosemans A, Corredor G, Dahl AB, Dantas Portela FL, Deman F, Demaria S, Doré Hansen J, Dudgeon SN, Ebstrup T, Elghazawy M, Fernandez-Martín C, Fox SB, Gallagher WM, Giltnane JM, Gnjatic S, Gonzalez-Ericsson PI, Grigoriadis A, Halama N, Hanna MG, Harbhajanka A, Hart SN, Hartman J, Hauberg S, Hewitt S, Hida AI, Horlings HM, Husain Z, Hytopoulos E, Irshad S, Janssen EA, Kahila M, Kataoka TR, Kawaguchi K, Kharidehal D, Khramtsov AI, Kiraz U, Kirtani P, Kodach LL, Korski K, Kovács A, Laenkholm AV, Lang-Schwarz C, Larsimont D, Lennerz JK, Lerousseau M, Li X, Ly A, Madabhushi A, Maley SK, Manur Narasimhamurthy V, Marks DK, McDonald ES, Mehrotra R, Michiels S, Minhas FUAA, Mittal S, Moore DA, Mushtaq S, Nighat H, Papathomas T, Penault-Llorca F, Perera RD, Pinard CJ, Pinto-Cardenas JC, Pruneri G, Pusztai L, Rahman A, Rajpoot NM, Rapoport BL, Rau TT, Reis-Filho JS, Ribeiro JM, Rimm D, Roslind A, Vincent-Salomon A, Salto-Tellez M, Saltz J, Sayed S, Scott E, Siziopikou KP, Sotiriou C, Stenzinger A, Sughayer MA, Sur D, Fineberg S, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson EA, Tramm T, Tran WT, van der Laak J, van Diest PJ, Verghese GE, Viale G, Vieth M, Wahab N, Walter T, Waumans Y, Wen HY, Yang W, Yuan Y, Zin RM, Adams S, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Salgado R, and Specht Stovgaard E
- Subjects
- Humans, Animals, Lymphocytes, Tumor-Infiltrating, Biomarkers, Machine Learning, Triple Negative Breast Neoplasms, Mammary Neoplasms, Animal
- Abstract
The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)
- Published
- 2023
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