Výsledky vyhľadávania - "Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences"

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

Autori: Thagaard, Jeppe Broeckx, Glenn Page, David B. a ďalší

Prispievatelia: Thagaard, Jeppe Broeckx, Glenn Page, David B. a ďalší

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

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 súboru: application/pdf

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

View record at OpenAIRE

Full Text Finder

Výsledky vyhľadávania - "Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences"

Vyhľadávacie nástroje:

Upresniť hľadanie

Médium

Predmet

Vydavateľ

Vydané v

Zdroj

Zbierka

Rok vydania