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
Authors: Thagaard Jeppe , Broeckx Glenn , Page David B , Jahangir Chowdhury Arif , Verbandt Sara , Kos Zuzana , Gupta Rajarsi , Khiroya Reena , Abduljabbar Khalid , Acosta Haab Gabriela , Acs Balazs , Akturk Guray , Almeida Jonas S , Alvarado‐Cabrero Isabel , Amgad Mohamed , Azmoudeh‐Ardalan Farid , Badve Sunil , Baharun Nurkhairul Bariyah , Balslev Eva , Bellolio Enrique R , Bheemaraju Vydehi , Blenman Kim RM , Botinelly Mendonça Fujimoto Luciana , Bouchmaa Najat , Burgues Octavio , Chardas Alexandros , Chon U Cheang Maggie , Ciompi Francesco , Cooper Lee AD , Coosemans An , Corredor Germán , Dahl Anders B , Dantas Portela Flavio Luis , Deman Frederik , Demaria Sandra , Doré Hansen Johan , Dudgeon Sarah N , Ebstrup Thomas , Elghazawy Mahmoud , Fernandez‐Martín Claudio , Fox Stephen B , Gallagher William M , Giltnane Jennifer M , Gnjatic Sacha , Gonzalez‐Ericsson Paula I , Grigoriadis Anita , Halama Niels , Hanna Matthew G , Harbhajanka Aparna , Hart Steven N , Hartman Johan , Hauberg Søren , Hewitt Stephen , Hida Akira I , Horlings Hugo M , Husain Zaheed , Hytopoulos Evangelos , Irshad Sheeba , Janssen Emiel AM , Kahila Mohamed , Kataoka Tatsuki R , Kawaguchi Kosuke , Kharidehal Durga , Khramtsov Andrey I , Kiraz Umay , Kirtani Pawan , Kodach Liudmila L , Korski Konstanty , Kovács Anikó , Laenkholm Anne‐Vibeke , Lang‐Schwarz Corinna , Larsimont Denis , Lennerz Jochen K , Lerousseau Marvin , Li Xiaoxian , Ly Amy , Madabhushi Anant , Maley Sai K , Manur Narasimhamurthy Vidya , Marks Douglas K , McDonald Elizabeth S , Mehrotra Ravi , Michiels Stefan , Minhas Fayyaz ul Amir Afsar , Mittal Shachi , Moore David A , Mushtaq Shamim , Nighat Hussain , Papathomas Thomas , Penault‐Llorca Frederique , Perera Rashindrie D , Pinard Christopher J , Pinto‐Cardenas Juan Carlos , Pruneri Giancarlo , Pusztai Lajos , Rahman Arman , Rajpoot Nasir Mahmood , Rapoport Bernardo Leon , Rau Tilman T , Reis‐Filho Jorge S , Ribeiro Joana M , Rimm David , Roslind Anne , Vincent‐Salomon Anne , Salto‐Tellez Manuel , Saltz Joel , Sayed Shahin , Scott Ely , Siziopikou Kalliopi P , Sotiriou Christos , Stenzinger Albrecht , Sughayer Maher A , Sur Daniel , Fineberg Susan , Symmans Fraser , Tanaka Sunao , Taxter Timothy , Tejpar Sabine , Teuwen Jonas , Thompson E Aubrey , Tramm Trine , Tran William T , van der Laak Jeroen , van Diest Paul J , Verghese Gregory E , Viale Giuseppe , Vieth Michael , Wahab Noorul , Walter Thomas , Waumans Yannick , Wen Hannah Y , Yang Wentao , Yuan Yinyin , Zin Reena Md , Adams Sylvia , Bartlett John , Loibl Sibylle , Denkert Carsten , Savas Peter , Loi Sherene , Salgado Roberto , Specht Stovgaard Elisabeth 
Published: August 2023

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.