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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

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Názov:	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
Autori:	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 R.M., Botinelly Mendonca Fujimoto, Luciana, Bouchmaa, Najat, Burgues, Octavio, Chardas, Alexandros, Cheang, Maggie U., Ciompi, Francesco, Cooper, Lee A.D., Coosemans, An, Corredor, German, Dahl, Anders B., Dantas Portela, Flavio Luis, Deman, Frederik, Demaria, Sandra, Dore Hansen, Johan, Dudgeon, Sarah N., Ebstrup, Thomas, Elghazawy, Mahmoud, Fernandez-Martin, Claudio, Fox, Stephen B., Gallagher, William M., Giltnane, Jennifer M., Gnjatic, Sacha, Gonzalez-Ericsson, Paula, Grigoriadis, Anita, Halama, Niels, Hanna, Matthew G., Harbhajanka, Aparna, Hart, Steven N., Hartman, Johan, Hauberg, Soren, Hewitt, Stephen, Hida, Akira, Horlings, Hugo M., Husain, Zaheed, Hytopoulos, Evangelos, Irshad, Sheeba, Janssen, Emiel A.M., Kahila, Mohamed, Kataoka, Tatsuki R., Kawaguchi, Kosuke, Kharidehal, Durga, Khramtsov, Andrey, Kiraz, Umay, Kirtani, Pawan, Kodach, Liudmila L., Korski, Konstanty, Kovacs, Aniko, 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
Prispievatelia:	Pathologie, Cancer, HAL UVSQ, Équipe, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Oncostat (U1018 (Équipe 2)), Institut Gustave Roussy (IGR)-Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Imagerie Moléculaire et Stratégies Théranostiques (IMoST), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Clermont Auvergne (UCA), Centre Jean Perrin Clermont-Ferrand (UNICANCER/CJP), UNICANCER, ANR-17-CONV-0005,Q-LIFE,Institut Q-LIFE(2017), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Zdroj:	J Pathol Thagaard, J, Broeckx, G, Page, D B, Jahangir, C A, Verbandt, S, Kos, Z, Gupta, R, Khiroya, R, Abduljabbar, K, Acosta Haab, G, Acs, B, Akturk, G, Almeida, J S, Alvarado-Cabrero, I, Amgad, M, Azmoudeh-Ardalan, F, Badve, S, Baharun, N B, Balslev, E, Bellolio, E R, Bheemaraju, V, Blenman, K R, Botinelly Mendonça Fujimoto, L, Bouchmaa, N, Burgues, O, Chardas, A, Chon U Cheang, M, Ciompi, F, Cooper, L A, Coosemans, A, Corredor, G, Dahl, A B, Dantas Portela, F L, Deman, F, Demaria, S, Doré Hansen, J, Dudgeon, S N, Ebstrup, T, Elghazawy, M, Fernandez-Martín, C, Fox, S B, Gallagher, W M, Giltnane, J M, Gnjatic, S, Gonzalez-Ericsson, P I, Grigoriadis, A, Halama, N, Hanna, M G, Harbhajanka, A, Hart, S N, Hartman, J, Hauberg, S, Hewitt, S, Hida, A I, Horlings, H M, Husain, Z, Hytopoulos, E, Irshad, S, Janssen, E A, Kahila, M, Kataoka, T R, Kawaguchi, K, Kharidehal, D, Khramtsov, A I, Kiraz, U, Kirtani, P, Kodach, L L, Korski, K, Kovács, A, Laenkholm, A-V, Lang-Schwarz, C, Larsimont, D, Lennerz, J K, Lerousseau, M, Li, X, Ly, A, Madabhushi, A, Maley, S K, Manur Narasimhamurthy, V, Marks, D K, McDonald, E S, Mehrotra, R, Michiels, S, Minhas, F U A A, Mittal, S, Moore, D A, Mushtaq, S, Nighat, H, Papathomas, T, Penault-Llorca, F, Perera, R D, Pinard, C J, Pinto-Cardenas, J C, Pruneri, G, Pusztai, L, Rahman, A, Rajpoot, N M, Rapoport, B L, Rau, T T, Reis-Filho, J S, Ribeiro, J M, Rimm, D, Roslind, A, Vincent-Salomon, A, Salto-Tellez, M, Saltz, J, Sayed, S, Scott, E, Siziopikou, K P, Sotiriou, C, Stenzinger, A, Sughayer, M A, Sur, D, Fineberg, S, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, E A, Tramm, T, Tran, W T, van der Laak, J, van Diest, P J, Verghese, G E, Viale, G, Vieth, M, Wahab, N, Walter, T, Waumans, Y, Wen, H Y, Yang, W, Yuan, Y, Zin, R M, Adams, S, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Salgado, R & Specht Stovgaard, E 2023, ' 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 ', Journal of Pathology, vol. 260, no. 5, pp. 498-513 . https://doi.org/10.1002/path.6155 Journal of Pathology, 260, 5, pp. 498-513 Journal of Pathology The journal of pathology Thagaard, J, Broeckx, G, Page, D B, Jahangir, C A, Verbandt, S, Kos, Z, Gupta, R, Khiroya, R, Abduljabbar, K, Acosta Haab, G, Acs, B, Akturk, G, Almeida, J S, Alvarado-Cabrero, I, Amgad, M, Azmoudeh-Ardalan, F, Badve, S, Baharun, N B, Balslev, E, Bellolio, E R, Bheemaraju, V, Blenman, K R M, Botinelly Mendonça Fujimoto, L, Bouchmaa, N, Burgues, O, Chardas, A, Chon U Cheang, M, Ciompi, F, Cooper, L A D, Coosemans, A, Corredor, G, Dahl, A B, Dantas Portela, F L, Deman, F, Demaria, S, Doré Hansen, J, Dudgeon, S N, Ebstrup, T, Elghazawy, M, Fernandez-Martín, C, Fox, S B, Gallagher, W M, Giltnane, J M, Gnjatic, S, Gonzalez-Ericsson, P I, Grigoriadis, A, Halama, N, Hanna, M G, Harbhajanka, A, Hart, S N, Hartman, J, Hauberg, S, Hewitt, S, Hida, A I, Horlings, H M, Husain, Z, Hytopoulos, E, Irshad, S, Janssen, E A M, Kahila, M, Kataoka, T R, Kawaguchi, K, Kharidehal, D, Khramtsov, A I, Kiraz, U, Kirtani, P, Kodach, L L, Korski, K, Kovács, A, Laenkholm, A V, Lang-Schwarz, C, Larsimont, D, Lennerz, J K, Lerousseau, M, Li, X, Ly, A, Madabhushi, A, Maley, S K, Manur Narasimhamurthy, V, Marks, D K, McDonald, E S, Mehrotra, R, Michiels, S, Minhas, F U A A, Mittal, S, Moore, D A, Mushtaq, S, Nighat, H, Papathomas, T, Penault-Llorca, F, Perera, R D, Pinard, C J, Pinto-Cardenas, J C, Pruneri, G, Pusztai, L, Rahman, A, Rajpoot, N M, Rapoport, B L, Rau, T T, Reis-Filho, J S, Ribeiro, J M, Rimm, D, Roslind, A, Vincent-Salomon, A, Salto-Tellez, M, Saltz, J, Sayed, S, Scott, E, Siziopikou, K P, Sotiriou, C, Stenzinger, A, Sughayer, M A, Sur, D, Fineberg, S, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, E A, Tramm, T, Tran, W T, van der Laak, J, van Diest, P J, Verghese, G E, Viale, G, Vieth, M, Wahab, N, Walter, T, Waumans, Y, Wen, H Y, Yang, W, Yuan, Y, Zin, R M, Adams, S, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Salgado, R & Specht Stovgaard, E 2023, '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', Journal of Pathology, vol. 260, no. 5, pp. 498-513. https://doi.org/10.1002/path.6155 Thagaard, J, Broeckx, G, Page, D B, Jahangir, C A, Verbandt, S, Kos, Z, Gupta, R, Khiroya, R, Abduljabbar, K, Acosta Haab, G, Acs, B, Akturk, G, Almeida, J S, Alvarado-Cabrero, I, Amgad, M, Azmoudeh-Ardalan, F, Badve, S, Baharun, N B, Balslev, E, Bellolio, E R, Bheemaraju, V, Blenman, K RM, Botinelly Mendonça Fujimoto, L, Bouchmaa, N, Burgues, O, Chardas, A, Chon U Cheang, M, Ciompi, F, Cooper, L AD, Coosemans, A, Corredor, G, Dahl, A B, Dantas Portela, F L, Deman, F, Demaria, S, Doré Hansen, J, Dudgeon, S N, Ebstrup, T, Elghazawy, M, Fernandez-Martín, C, Fox, S B, Gallagher, W M, Giltnane, J M, Gnjatic, S, Gonzalez-Ericsson, P I, Grigoriadis, A, Halama, N, Hanna, M G, Harbhajanka, A, Hart, S N, Hartman, J, Hauberg, S, Hewitt, S, Hida, A I, Horlings, H M, Husain, Z, Hytopoulos, E, Irshad, S, Janssen, E AM, Kahila, M, Kataoka, T R, Kawaguchi, K, Kharidehal, D, Khramtsov, A I, Kiraz, U, Kirtani, P, Kodach, L L, Korski, K, Kovács, A, Laenkholm, AV, Lang-Schwarz, C, Larsimont, D, Lennerz, J K, Lerousseau, M, Li, X, Ly, A, Madabhushi, A, Maley, S K, Manur Narasimhamurthy, V, Marks, D K, McDonald, E S, Mehrotra, R, Michiels, S, Minhas, F U A A, Mittal, S, Moore, D A, Mushtaq, S, Nighat, H, Papathomas, T, Penault-Llorca, F, Perera, R D, Pinard, C J, Pinto-Cardenas, J C, Pruneri, G, Pusztai, L, Rahman, A, Rajpoot, N M, Rapoport, B L, Rau, T T, Reis-Filho, J S, Ribeiro, J M, Rimm, D, Roslind, A, Vincent-Salomon, A, Salto-Tellez, M, Saltz, J, Sayed, S, Scott, E, Siziopikou, K P, Sotiriou, C, Stenzinger, A, Sughayer, M A, Sur, D, Fineberg, S, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, E A, Tramm, T, Tran, W T, van der Laak, J, van Diest, P J, Verghese, G E, Viale, G, Vieth, M, Wahab, N, Walter, T, Waumans, Y, Wen, H Y, Yang, W, Yuan, Y, Zin, R M, Adams, S, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Salgado, R & Specht Stovgaard, E 2023, 'Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group', Journal of Pathology, vol. 260, no. 5, pp. 498-513. https://doi.org/10.1002/path.6155 Thagaard, J, Broeckx, G, Page, D B, Jahangir, C A, Verbandt, S, Kos, Z, Gupta, R, Khiroya, R, Abduljabbar, K, Acosta Haab, G, Acs, B, Akturk, G, Almeida, J S, Alvarado-Cabrero, I, Amgad, M, Azmoudeh-Ardalan, F, Badve, S, Baharun, N B, Balslev, E, Bellolio, E R, Bheemaraju, V, Blenman, K R M, Botinelly Mendonça Fujimoto, L, Bouchmaa, N, Burgues, O, Chardas, A, Chon U Cheang, M, Ciompi, F, Cooper, L A D, Coosemans, A, Corredor, G, Dahl, A B, Dantas Portela, F L, Deman, F, Demaria, S, Doré Hansen, J, Dudgeon, S N, Ebstrup, T, Elghazawy, M, Fernandez-Martín, C, Fox, S B, Gallagher, W M, Giltnane, J M, Gnjatic, S, Gonzalez-Ericsson, P I, Grigoriadis, A, Halama, N, Hanna, M G, Harbhajanka, A, Hart, S N, Hartman, J, Hauberg, S, Hewitt, S, Hida, A I, Horlings, H M, Husain, Z, Hytopoulos, E, Irshad, S, Janssen, E A M, Kahila, M, Kataoka, T R, Kawaguchi, K, Kharidehal, D, Khramtsov, A I, Kiraz, U, Kirtani, P, Kodach, L L, Korski, K, Kovács, A, Laenkholm, A V, Lang-Schwarz, C, Larsimont, D, Lennerz, J K, Lerousseau, M, Li, X, Ly, A, Madabhushi, A, Maley, S K, Manur Narasimhamurthy, V, Marks, D K, McDonald, E S, Mehrotra, R, Michiels, S, Minhas, F U A A, Mittal, S, Moore, D A, Mushtaq, S, Nighat, H, Papathomas, T, Penault-Llorca, F, Perera, R D, Pinard, C J, Pinto-Cardenas, J C, Pruneri, G, Pusztai, L, Rahman, A, Rajpoot, N M, Rapoport, B L, Rau, T T, Reis-Filho, J S, Ribeiro, J M, Rimm, D, Roslind, A, Vincent-Salomon, A, Salto-Tellez, M, Saltz, J, Sayed, S, Scott, E, Siziopikou, K P, Sotiriou, C, Stenzinger, A, Sughayer, M A, Sur, D, Fineberg, S, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, E A, Tramm, T, Tran, W T, van der Laak, J, van Diest, P J, Verghese, G E, Viale, G, Vieth, M, Wahab, N, Walter, T, Waumans, Y, Wen, H Y, Yang, W, Yuan, Y, Zin, R M, Adams, S, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Salgado, R & Specht Stovgaard, E 2023, ' Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer : a report of the international immuno-oncology biomarker working group ', Journal of Pathology, vol. 260, no. 5, pp. 498-513 . https://doi.org/10.1002/path.6155 Thagaard, J, Broeckx, G, Page, D B, Jahangir, C A, Verbandt, S, Kos, Z, Gupta, R, Khiroya, R, Abduljabbar, K, Acosta haab, G, Acs, B, Akturk, G, Almeida, J S, Alvarado-cabrero, I, Amgad, M, Azmoudeh-ardalan, F, Badve, S, Baharun, N B, Balslev, E, Bellolio, E R, Bheemaraju, V, Blenman, K R, Botinelly mendonça fujimoto, L, Bouchmaa, N, Burgues, O, Chardas, A, Chon u cheang, M, Ciompi, F, Cooper, L A, Coosemans, A, Corredor, G, Dahl, A B, Dantas portela, F L, Deman, F, Demaria, S, Doré hansen, J, Dudgeon, S N, Ebstrup, T, Elghazawy, M, Fernandez-martín, C, Fox, S B, Gallagher, W M, Giltnane, J M, Gnjatic, S, Gonzalez-ericsson, P I, Grigoriadis, A, Halama, N, Hanna, M G, Harbhajanka, A, Hart, S N, Hartman, J, Hauberg, S, Hewitt, S, Hida, A I, Horlings, H M, Husain, Z, Hytopoulos, E, Irshad, S, Janssen, E A, Kahila, M, Kataoka, T R, Kawaguchi, K, Kharidehal, D, Khramtsov, A I, Kiraz, U, Kirtani, P, Kodach, L L, Korski, K, Kovács, A, Laenkholm, A, Lang-schwarz, C, Larsimont, D, Lennerz, J K, Lerousseau, M, Li, X, Ly, A, Madabhushi, A, Maley, S K, Manur narasimhamurthy, V, Marks, D K, Mcdonald, E S, Mehrotra, R, Michiels, S, Minhas, F U A A, Mittal, S, Moore, D A, Mushtaq, S, Nighat, H, Papathomas, T, Penault-llorca, F, Perera, R D, Pinard, C J, Pinto-cardenas, J C, Pruneri, G, Pusztai, L, Rahman, A, Rajpoot, N M, Rapoport, B L, Rau, T T, Reis-filho, J S, Ribeiro, J M, Rimm, D, Roslind, A, Vincent-salomon, A, Salto-tellez, M, Saltz, J, Sayed, S, Scott, E, Siziopikou, K P, Sotiriou, C, Stenzinger, A, Sughayer, M A, Sur, D, Fineberg, S, Symmans, F, Tanaka, S, Taxter, T, Tejpar, S, Teuwen, J, Thompson, E A, Tramm, T, Tran, W T, Van der laak, J, Van diest, P J, Verghese, G E, Viale, G, Vieth, M, Wahab, N, Walter, T, Waumans, Y, Wen, H Y, Yang, W, Yuan, Y, Zin, R M, Adams, S, Bartlett, J, Loibl, S, Denkert, C, Savas, P, Loi, S, Salgado, R & Specht stovgaard, E 2023, ' Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group ', Journal of Pathology . https://doi.org/10.1002/path.6155 The Journal of Pathology
Informácie o vydavateľovi:	Wiley, 2023.
Rok vydania:	2023
Predmety:	SDG-03: Good health and well-being, SDG-09: Industry, pitfalls, Triple Negative Breast Neoplasms, Review, Tumor-infiltrating lymphocytes, tumor‐infiltrating lymphocytes, Image analysis, Machine Learning, Pathology, Invited Reviews, name=SDG 3 - Good Health and Well-being, guidelines, prognostic biomarker, innovation and infrastructure, tumor-infilitrating lymphocytes, digitial pathology, 3. Good health, machine learning, Oncology, tumor-infiltrating lymphocytes, triple-negative breast cancer, Life Sciences & Biomedicine, Tumor-infiltrating lymphocytes (TILs), Prognostic biomarker, Radboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences, [SDV.CAN]Life Sciences [q-bio]/Cancer, Mammary Neoplasms, Animal, Guidelines, Pathology and Forensic Medicine, All institutes and research themes of the Radboud University Medical Center, Lymphocytes, Tumor-Infiltrating, [SDV.CAN] Life Sciences [q-bio]/Cancer, Triple-negative breast cancer, image analysis, 3211 Oncology and carcinogenesis, Machine learning, Journal Article, Digital pathology, Humans, Animals, VDP::Medisinske Fag: 700, Science & Technology, 3202 Clinical sciences, deep learning, Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences, Deep learning, 1103 Clinical Sciences, triple‐negative breast cancer, Human medicine, Pitfalls, digital pathology, Biomarkers
Popis:	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.
Druh dokumentu:	Article Other literature type
Popis súboru:	application/pdf
Jazyk:	English
ISSN:	1096-9896 0022-3417
DOI:	10.1002/path.6155
Prístupová URL adresa:	https://pubmed.ncbi.nlm.nih.gov/37608772 https://dspace.library.uu.nl/handle/1874/449901 https://lirias.kuleuven.be/handle/20.500.12942/725784 https://doi.org/10.1002/path.6155 https://orbit.dtu.dk/en/publications/62e573d1-8568-4715-baac-c5346721b453 https://repository.ubn.ru.nl/handle/2066/296181 https://repository.ubn.ru.nl//bitstream/handle/2066/296181/296181.pdf https://hdl.handle.net/11250/3118658 https://repository.uantwerpen.be/docstore/d:irua:20284 https://hdl.handle.net/10067/2004380151162165141 https://pure.au.dk/portal/en/publications/35ef01d0-dde2-449d-b854-df251b5876a9 https://pure.qub.ac.uk/en/publications/fbd81c5e-8209-4130-aeb5-d03d5ad317b8 https://curis.ku.dk/ws/files/387276022/The_Journal_of_Pathology_2023_Thagaard_Pitfalls_in_machine_learning_based_assessment_of_tumor_infiltrating.pdf https://hal.science/hal-04209913v1/document https://doi.org/10.1002/path.6155 https://hal.science/hal-04209913v1 https://hdl.handle.net/20.500.11820/61c8effd-f556-4d49-86af-adf8c9993dc1 https://www.pure.ed.ac.uk/ws/files/372705161/The_Journal_of_Pathology_2023_Thagaard.pdf https://pure.au.dk/portal/en/publications/35ef01d0-dde2-449d-b854-df251b5876a9 http://www.scopus.com/inward/record.url?scp=85167995389&partnerID=8YFLogxK https://pure.au.dk/ws/files/411431790/The_Journal_of_Pathology_-_2023_-_Thagaard_-_Pitfalls_in_machine_learning_based_assessment_of_tumor_infiltrating.pdf https://doi.org/10.1002/path.6155
Rights:	CC BY NC
Prístupové číslo:	edsair.doi.dedup.....e3912160dabcf49edf0e52aff7551a61
Databáza:	OpenAIRE

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Abstrakt:	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.
ISSN:	10969896 00223417
DOI:	10.1002/path.6155