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Fully Automated Tum...
Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer
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- Bokhorst, John-Melle (författare)
- Radboud Univ Nijmegen, Netherlands
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- Ciompi, Francesco (författare)
- Radboud Univ Nijmegen, Netherlands
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- Ozturk, Sonay Kus (författare)
- Radboud Univ Nijmegen, Netherlands
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- Erdogan, Ayse Selcen Oguz (författare)
- Radboud Univ Nijmegen, Netherlands
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- Vieth, Michael (författare)
- Bayreuth Univ, Germany
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- Dawson, Heather (författare)
- Univ Bern, Switzerland
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- Kirsch, Richard (författare)
- Univ Toronto, Canada
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- Simmer, Femke (författare)
- Radboud Univ Nijmegen, Netherlands
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- Sheahan, Kieran (författare)
- St Vincents Hosp, Ireland
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- Lugli, Alessandro (författare)
- Bayreuth Univ, Germany
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- Zlobec, Inti (författare)
- Bayreuth Univ, Germany
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- van der Laak, Jeroen (författare)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands
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- Nagtegaal, Iris D. (författare)
- Radboud Univ Nijmegen, Netherlands
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(creator_code:org_t)
- ELSEVIER SCIENCE INC, 2023
- 2023
- Engelska.
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Ingår i: Modern Pathology. - : ELSEVIER SCIENCE INC. - 0893-3952 .- 1530-0285. ; 36:9
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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/).
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- automated assessment; colorectal cancer; computational pathology; prognosis; tumor budding
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Bokhorst, John-M ...
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Ciompi, Francesc ...
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Ozturk, Sonay Ku ...
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Erdogan, Ayse Se ...
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Vieth, Michael
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Dawson, Heather
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visa fler...
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Kirsch, Richard
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Simmer, Femke
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Sheahan, Kieran
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Lugli, Alessandr ...
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Zlobec, Inti
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van der Laak, Je ...
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Nagtegaal, Iris ...
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Modern Pathology
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