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Deep learning based...
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Smit, Marloes A.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
(författare)
Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment
- Artikel/kapitelEngelska2023
Förlag, utgivningsår, omfång ...
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Elsevier B.V.2023
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LIBRIS-ID:oai:DiVA.org:liu-200781
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-200781URI
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https://doi.org/10.1016/j.jpi.2023.100191DOI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:art swepub-publicationtype
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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
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Biuppslag (personer, institutioner, konferenser, titlar ...)
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Ciompi, FrancescoDepartment of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
(författare)
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Bokhorst, John-MelleDepartment of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
(författare)
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van Pelt, Gabi W.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
(författare)
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Geessink, Oscar G.F.Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
(författare)
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Putter, HeinDepartment of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands
(författare)
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Tollenaar, Rob A.E.M.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
(författare)
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van Krieken, J. Han J.M.Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
(författare)
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Mesker, Wilma E.Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
(författare)
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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
(författare)
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Department of Surgery, Leiden University Medical Center, Leiden, The NetherlandsDepartment of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
(creator_code:org_t)
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Ingår i:Journal of Pathology Informatics: Elsevier B.V.142229-50892153-3539
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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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