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Semi-Supervised Lea...
Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer
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- Bokhorst, John-Melle (author)
- Radboud Univ Nijmegen, Netherlands
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- Nagtegaal, Iris D. (author)
- Radboud Univ Nijmegen, Netherlands
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- Zlobec, Inti (author)
- Univ Bern, Switzerland
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- Dawson, Heather (author)
- Univ Bern, Switzerland
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- Sheahan, Kieran (author)
- St Vincents Univ Hosp, Ireland
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- Simmer, Femke (author)
- Radboud Univ Nijmegen, Netherlands
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- Kirsch, Richard (author)
- Univ Toronto, Canada
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- Vieth, Michael (author)
- Friedrich Alexander Univ Erlangen Nuremberg, Germany
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- Lugli, Alessandro (author)
- Univ Bern, Switzerland
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- van der Laak, Jeroen (author)
- 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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- Ciompi, Francesco (author)
- Radboud Univ Nijmegen, Netherlands
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(creator_code:org_t)
- 2023-03-30
- 2023
- English.
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In: Cancers. - : MDPI. - 2072-6694. ; 15:7
- Related links:
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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/10.3...
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Abstract
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- Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- deep learning; computational pathology; colorectal carcinoma; tumor budding; object detection
Publication and Content Type
- ref (subject category)
- art (subject category)
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Cancers
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- By the author/editor
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Bokhorst, John-M ...
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Nagtegaal, Iris ...
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Zlobec, Inti
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Dawson, Heather
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Sheahan, Kieran
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Simmer, Femke
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show more...
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Kirsch, Richard
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Vieth, Michael
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Lugli, Alessandr ...
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van der Laak, Je ...
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Ciompi, Francesc ...
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Cancer and Oncol ...
- Articles in the publication
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Cancers
- By the university
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Linköping University