@article {PMID:37608772,
	Title = {Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group},
	Author = {Thagaard, Jeppe and Broeckx, Glenn and Page, David B and Jahangir, Chowdhury Arif and Verbandt, Sara and Kos, Zuzana and Gupta, Rajarsi and Khiroya, Reena and Abduljabbar, Khalid and Acosta Haab, Gabriela and Acs, Balazs and Akturk, Guray and Almeida, Jonas S and Alvarado-Cabrero, Isabel and Amgad, Mohamed and Azmoudeh-Ardalan, Farid and Badve, Sunil and Baharun, Nurkhairul Bariyah and Balslev, Eva and Bellolio, Enrique R and Bheemaraju, Vydehi and Blenman, Kim Rm and Botinelly Mendonça Fujimoto, Luciana and Bouchmaa, Najat and Burgues, Octavio and Chardas, Alexandros and Chon U Cheang, Maggie and Ciompi, Francesco and Cooper, Lee Ad and Coosemans, An and Corredor, Germán and Dahl, Anders B and Dantas Portela, Flavio Luis and Deman, Frederik and Demaria, Sandra and Doré Hansen, Johan and Dudgeon, Sarah N and Ebstrup, Thomas and Elghazawy, Mahmoud and Fernandez-Martín, Claudio and Fox, Stephen B and Gallagher, William M and Giltnane, Jennifer M and Gnjatic, Sacha and Gonzalez-Ericsson, Paula I and Grigoriadis, Anita and Halama, Niels and Hanna, Matthew G and Harbhajanka, Aparna and Hart, Steven N and Hartman, Johan and Hauberg, Søren and Hewitt, Stephen and Hida, Akira I and Horlings, Hugo M and Husain, Zaheed and Hytopoulos, Evangelos and Irshad, Sheeba and Janssen, Emiel Am and Kahila, Mohamed and Kataoka, Tatsuki R and Kawaguchi, Kosuke and Kharidehal, Durga and Khramtsov, Andrey I and Kiraz, Umay and Kirtani, Pawan and Kodach, Liudmila L and Korski, Konstanty and Kovács, Anikó and Laenkholm, Anne-Vibeke and Lang-Schwarz, Corinna and Larsimont, Denis and Lennerz, Jochen K and Lerousseau, Marvin and Li, Xiaoxian and Ly, Amy and Madabhushi, Anant and Maley, Sai K and Manur Narasimhamurthy, Vidya and Marks, Douglas K and McDonald, Elizabeth S and Mehrotra, Ravi and Michiels, Stefan and Minhas, Fayyaz Ul Amir Afsar and Mittal, Shachi and Moore, David A and Mushtaq, Shamim and Nighat, Hussain and Papathomas, Thomas and Penault-Llorca, Frederique and Perera, Rashindrie D and Pinard, Christopher J and Pinto-Cardenas, Juan Carlos and Pruneri, Giancarlo and Pusztai, Lajos and Rahman, Arman and Rajpoot, Nasir Mahmood and Rapoport, Bernardo Leon and Rau, Tilman T and Reis-Filho, Jorge S and Ribeiro, Joana M and Rimm, David and Roslind, Anne and Vincent-Salomon, Anne and Salto-Tellez, Manuel and Saltz, Joel and Sayed, Shahin and Scott, Ely and Siziopikou, Kalliopi P and Sotiriou, Christos and Stenzinger, Albrecht and Sughayer, Maher A and Sur, Daniel and Fineberg, Susan and Symmans, Fraser and Tanaka, Sunao and Taxter, Timothy and Tejpar, Sabine and Teuwen, Jonas and Thompson, E Aubrey and Tramm, Trine and Tran, William T and van der Laak, Jeroen and van Diest, Paul J and Verghese, Gregory E and Viale, Giuseppe and Vieth, Michael and Wahab, Noorul and Walter, Thomas and Waumans, Yannick and Wen, Hannah Y and Yang, Wentao and Yuan, Yinyin and Zin, Reena Md and Adams, Sylvia and Bartlett, John and Loibl, Sibylle and Denkert, Carsten and Savas, Peter and Loi, Sherene and Salgado, Roberto and Specht Stovgaard, Elisabeth},
	DOI = {10.1002/path.6155},
	Month = {August},
	Year = {2023},
	Journal = {The Journal of pathology},
	ISSN = {0022-3417},
	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.},
	URL = {https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/path.6155},
}