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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
Radboud Univ Nijmegen, Netherlands; Radboud Univ Nijmegen, Netherlands; Lab Pathol East Netherlands LabPON, Netherlands.
Lab Pathol East Netherlands LabPON, Netherlands.
Medisch Spectrum Twente, Netherlands.
Radboud Univ Nijmegen, Netherlands.
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2019 (English)In: Cellular Oncology, ISSN 2211-3428, E-ISSN 2211-3436, Vol. 42, no 3, p. 331-341Article in journal (Refereed) Published
Abstract [en]

PurposeTumor-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.MethodsHistological 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.ResultsWith 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.ConclusionsThis 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.

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 42, no 3, p. 331-341
Keywords [en]
Rectal carcinoma; Tumor-stroma ratio; Prognosis; Computational pathology; Automated analysis; Deep learning
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:liu:diva-158049DOI: 10.1007/s13402-019-00429-zISI: 000469354700007PubMedID: 30825182OAI: oai:DiVA.org:liu-158049DiVA, id: diva2:1330166
Note

Funding Agencies|Dutch Cancer Society (KWF) / Alpe dHuZes fund [KUN 2014-7032]

Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-11-04

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