Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group
| Primer Autor |
Stovgaard, Elisabeth Specht
|
| Co-autores |
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, I
Grigoriadis, Anita
Halama, Niels
Hanna, Matthew G.
Harbhajanka, Aparna
Hart, Steven N.
Hartman, Johan
Hauberg, Soren
Hewitt, Stephen
Hida, Akira, I
Horlings, Hugo M.
Husain, Zaheed
Hytopoulos, Evangelos
Irshad, Sheeba
Janssen, Emiel A. M.
Kahila, Mohamed
Kataoka, Tatsuki R.
Kawaguchi, Kosuke
Kharidehal, Durga
Khramtsov, Andrey, I
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
Salto-Tellez, Manuel
Tran, William T.
|
| Título |
Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group
|
| Editorial |
WILEY
|
| Revista |
JOURNAL OF PATHOLOGY
|
| Lenguaje |
en
|
| Resumen |
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. & COPY, 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
|
| Fecha Publicación |
2023
|
| Tipo de Recurso |
artículo de revisión
|
| doi |
10.1002/path.6155
|
| Formato Recurso |
PDF
|
| Palabras Claves |
deep learning
machine learning
digital pathology
guidelines
image analysis
pitfalls
prognostic biomarker
triple-negative breast cancer
tumor-infiltrating lymphocytes
|
| Ubicación del archivo | |
| Categoría OCDE |
Oncología
Patología
|
| Materias |
deep learning
machine learning
patología digital
pautas
análisis de imagen
trampas
biomarcador pronóstico
cáncer de mama triple negativo
linfocitos infiltrantes de tumores
|
| Página de inicio (Recomendado-único) |
498.0
|
| Página final (Recomendado-único) |
513
|
| Identificador del recurso (Mandatado-único) |
artículo de revisión
|
| Versión del recurso (Recomendado-único) |
versión publicada
|
| License |
CC BY-NC 4.0
|
| Condición de la licencia (Recomendado-repetible) |
CC BY-NC 4.0
|
| Derechos de acceso |
acceso abierto
|
| Access Rights |
acceso abierto
|
| Id de Web of Science |
WOS:001053491400001
|
| ISSN |
0022-3417
|
| Tipo de ruta |
Verde# hibrida
|
| Categoría WOS |
Oncología
Patología
|
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