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Deep learning based...
Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment
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- Smit, Marloes A. (author)
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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- Ciompi, Francesco (author)
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
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- Bokhorst, John-Melle (author)
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
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- van Pelt, Gabi W. (author)
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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- Geessink, Oscar G.F. (author)
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
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- Putter, Hein (author)
- Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands
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- Tollenaar, Rob A.E.M. (author)
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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- van Krieken, J. Han J.M. (author)
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
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- Mesker, Wilma E. (author)
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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- van der Laak, Jeroen, 1967- (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi,Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
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(creator_code:org_t)
- Elsevier B.V. 2023
- 2023
- English.
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In: Journal of Pathology Informatics. - : Elsevier B.V.. - 2229-5089 .- 2153-3539. ; 14
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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- Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor–stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. Methods: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. Results: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23–0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. Conclusion: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists. © 2023 The Authors
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
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- ref (subject category)
- art (subject category)
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Smit, Marloes A.
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Ciompi, Francesc ...
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Bokhorst, John-M ...
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van Pelt, Gabi W ...
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Geessink, Oscar ...
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Putter, Hein
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Tollenaar, Rob A ...
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van Krieken, J. ...
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Mesker, Wilma E.
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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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Journal of Patho ...
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Linköping University