Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
| Autor: | Gabi W. van Pelt, Babak Ehteshami Bejnordi, Joost M. Klaase, Francesco Ciompi, Oscar Geessink, Iris D. Nagtegaal, Jeroen van der Laak, Alexi Baidoshvili, Geert Litjens, Wilma E. Mesker |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
0301 basic medicine Oncology Cancer Research Multivariate analysis Colorectal cancer Kaplan-Meier Estimate Tumours of the digestive tract Radboud Institute for Health Sciences [Radboudumc 14] 0302 clinical medicine Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14] Rectal Adenocarcinoma Diagnosis Computer-Assisted Lymph node Pathology Clinical Tumor-stroma ratio General Medicine Automated analysis Middle Aged Prognosis Computational pathology Women's cancers Radboud Institute for Health Sciences [Radboudumc 17] medicine.anatomical_structure Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] 030220 oncology & carcinogenesis Molecular Medicine Female medicine.medical_specialty Rectal carcinoma 03 medical and health sciences All institutes and research themes of the Radboud University Medical Center Stroma Internal medicine medicine Adjuvant therapy Humans Aged Neoplasm Staging Cancer och onkologi Rectal Neoplasms business.industry Digital pathology Deep learning medicine.disease Confidence interval 030104 developmental biology Cancer and Oncology Multivariate Analysis Stromal Cells business |
| Zdroj: | Cellular Oncology, 42(3), 331-341 Cellular Oncology, 42, 331-341 Cellular Oncology, 42, 3, pp. 331-341 |
| ISSN: | 1570-5870 |
| Popis: | Contains fulltext : 204300.pdf (Publisher’s version ) (Open Access) PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images. METHODS: Histological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a 'stroma-high' or 'stroma-low' group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times. RESULTS: With stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio = 2.48 (95% confidence interval 1.29-4.78)) and for disease-free survival (hazard ratio = 2.05 (95% confidence interval 1.11-3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis. CONCLUSIONS: This work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics. |
| Databáze: | OpenAIRE |
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