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  • Smit, Marloes A.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands (author)

Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • Elsevier B.V.2023
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:liu-200781
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-200781URI
  • https://doi.org/10.1016/j.jpi.2023.100191DOI

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  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • 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

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  • Ciompi, FrancescoDepartment of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands (author)
  • Bokhorst, John-MelleDepartment of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands (author)
  • van Pelt, Gabi W.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands (author)
  • Geessink, Oscar G.F.Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands (author)
  • Putter, HeinDepartment of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands (author)
  • Tollenaar, Rob A.E.M.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands (author)
  • van Krieken, J. Han J.M.Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands (author)
  • Mesker, Wilma E.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands (author)
  • van der Laak, Jeroen,1967-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(Swepub:liu)jerva26 (author)
  • Department of Surgery, Leiden University Medical Center, Leiden, The NetherlandsDepartment of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands (creator_code:org_t)

Related titles

  • In:Journal of Pathology Informatics: Elsevier B.V.142229-50892153-3539

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