Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
SourceCellular Oncology, 42, 3, (2019), pp. 331-341
Article / Letter to editor
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SubjectAll institutes and research themes of the Radboud University Medical Center; Radboudumc 14: Tumours of the digestive tract RIHS: Radboud Institute for Health Sciences; Radboudumc 14: Tumours of the digestive tract RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences
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.
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