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Computer aided quan...
Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
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- Geessink, Oscar G. F. (author)
- Radboud Univ Nijmegen, Netherlands; Radboud Univ Nijmegen, Netherlands; Lab Pathol East Netherlands LabPON, Netherlands
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- Baidoshvili, Alexi (author)
- Lab Pathol East Netherlands LabPON, Netherlands
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- Klaase, Joost M. (author)
- Medisch Spectrum Twente, Netherlands
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- Bejnordi, Babak Ehteshami (author)
- Radboud Univ Nijmegen, Netherlands
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- Litjens, Geert J. S. (author)
- Radboud Univ Nijmegen, Netherlands
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- van Pelt, Gabi W. (author)
- Leiden Univ, Netherlands
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- Mesker, Wilma E. (author)
- Leiden Univ, Netherlands
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- Nagtegaal, Iris D. (author)
- Radboud Univ Nijmegen, Netherlands
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- Ciompi, Francesco (author)
- Radboud Univ Nijmegen, Netherlands
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- van der Laak, Jeroen (author)
- Linköpings universitet,Avdelningen för radiologiska vetenskaper,Medicinska fakulteten,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands
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(creator_code:org_t)
- 2019-03-01
- 2019
- English.
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In: Cellular Oncology. - : SPRINGER. - 2211-3428 .- 2211-3436. ; 42:3, s. 331-341
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Abstract
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- Rectal carcinoma; Tumor-stroma ratio; Prognosis; Computational pathology; Automated analysis; Deep learning
Publication and Content Type
- ref (subject category)
- art (subject category)
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Geessink, Oscar ...
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Baidoshvili, Ale ...
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Klaase, Joost M.
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Bejnordi, Babak ...
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Litjens, Geert J ...
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van Pelt, Gabi W ...
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Mesker, Wilma E.
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Nagtegaal, Iris ...
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Ciompi, Francesc ...
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van der Laak, Je ...
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Cancer and Oncol ...
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Cellular Oncolog ...
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Linköping University