دورية أكاديمية
Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer.
| العنوان: | Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer. |
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| المؤلفون: | Bokhorst JM; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: john-melle.bokhorst@radboudumc.nl., Ciompi F; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Öztürk SK; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Oguz Erdogan AS; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Vieth M; Klinikum of Pathology, Bayreuth University, Bayreuth, Germany., Dawson H; Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland., Kirsch R; University of Toronto, Mount Sinai Hospital, Toronto, Canada., Simmer F; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Sheahan K; Department of Pathology, St Vincent's Hospital, Dublin, Ireland., Lugli A; Klinikum of Pathology, Bayreuth University, Bayreuth, Germany., Zlobec I; Klinikum of Pathology, Bayreuth University, Bayreuth, Germany., van der Laak J; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden., Nagtegaal ID; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands. |
| المصدر: | Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc [Mod Pathol] 2023 Sep; Vol. 36 (9), pp. 100233. Date of Electronic Publication: 2023 May 30. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 8806605 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-0285 (Electronic) Linking ISSN: 08933952 NLM ISO Abbreviation: Mod Pathol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2023- : [New York] : Elsevier Inc. Original Publication: Baltimore, MD : Williams & Wilkins, c1988- |
| مواضيع طبية MeSH: | Artificial Intelligence* , Colorectal Neoplasms*/diagnosis , Colorectal Neoplasms*/pathology, Humans ; Hematoxylin ; Eosine Yellowish-(YS) ; Diagnosis, Computer-Assisted |
| مستخلص: | 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 = 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. (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.) |
| فهرسة مساهمة: | Keywords: artificial intelligence; automated assessment; colorectal cancer; computational pathology; prognosis; tumor budding |
| المشرفين على المادة: | YKM8PY2Z55 (Hematoxylin) TDQ283MPCW (Eosine Yellowish-(YS)) |
| تواريخ الأحداث: | Date Created: 20230531 Date Completed: 20230925 Latest Revision: 20230925 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.modpat.2023.100233 |
| PMID: | 37257824 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1530-0285 |
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| DOI: | 10.1016/j.modpat.2023.100233 |