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Fully Automated Tum...
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Bokhorst, John-MelleRadboud Univ Nijmegen, Netherlands
(author)
Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer
- Article/chapterEnglish2023
Publisher, publication year, extent ...
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ELSEVIER SCIENCE INC,2023
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electronicrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:liu-196786
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-196786URI
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https://doi.org/10.1016/j.modpat.2023.100233DOI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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Funding Agencies|European Union [825292]; Dutch Cancer Society [10602/2016-2]; Swiss Cancer Research Foundation [KFS-4427-02-2018]; Alpe dHuZes/Dutch Cancer Society Fund [KUN 2014-7032]
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Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
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Ciompi, FrancescoRadboud Univ Nijmegen, Netherlands
(author)
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Ozturk, Sonay KusRadboud Univ Nijmegen, Netherlands
(author)
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Erdogan, Ayse Selcen OguzRadboud Univ Nijmegen, Netherlands
(author)
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Vieth, MichaelBayreuth Univ, Germany
(author)
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Dawson, HeatherUniv Bern, Switzerland
(author)
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Kirsch, RichardUniv Toronto, Canada
(author)
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Simmer, FemkeRadboud Univ Nijmegen, Netherlands
(author)
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Sheahan, KieranSt Vincents Hosp, Ireland
(author)
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Lugli, AlessandroBayreuth Univ, Germany
(author)
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Zlobec, IntiBayreuth Univ, Germany
(author)
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van der Laak, JeroenLinköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands(Swepub:liu)jerva26
(author)
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Nagtegaal, Iris D.Radboud Univ Nijmegen, Netherlands
(author)
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Radboud Univ Nijmegen, NetherlandsBayreuth Univ, Germany
(creator_code:org_t)
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In:Modern Pathology: ELSEVIER SCIENCE INC36:90893-39521530-0285
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Vieth, Michael
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Kirsch, Richard
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Simmer, Femke
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Modern Pathology
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