Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
| Autor: | Brandon D. Gallas, Alexander Binder, Stephen M. Hewitt, Carsten Denkert, Sunil S. Badve, Cinzia Solinas, Christos Sotiriou, Klaus-Robert Müller, Giancarlo Pruneri, Scooter Willis, Roberto Salgado, Morag Park, Stephan Wienert, David L. Rimm, Frederick Klauschen, Michael Bockmayr, Sibylle Loibl, Sylvia Adams, Ian A. Cree, Fraser Symmans, Sherene Loi, Benjamin Haibe-Kains, Stefan Michiels, Tina Gruosso, Miriam Hägele, Giuseppe Viale, Torsten O. Nielsen, S. de Maria, Philipp Seegerer, Matthias Preusser, Peter Savas, Alastair M. Thompson |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Cancer Research Standardization Computer science Neoplasms -- metabolism -- pathology chemical and pharmacologic phenomena Machine learning computer.software_genre Machine Learning 03 medical and health sciences Lymphocytes Tumor-Infiltrating 0302 clinical medicine Lymphocytes Tumor-Infiltrating -- metabolism -- pathology Neoplasms Biomarkers Tumor Humans Segmentation Visual estimation Tumor-infiltrating lymphocytes business.industry Médecine pathologie humaine Contrast (statistics) hemic and immune systems Image segmentation Medical research Biomarkers Tumor -- metabolism Expression (mathematics) Cancérologie Enseignement des sciences bio-médicales et agricoles 030104 developmental biology 030220 oncology & carcinogenesis Artificial intelligence business computer |
| Zdroj: | Seminars in cancer biology, 52 |
| ISSN: | 1044-579X |
| DOI: | 10.1016/j.semcancer.2018.07.001 |
| Popis: | The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their “black-box” characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics. SCOPUS: re.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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