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Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Bokhorst, John-Melle (author)
Radboud Univ Nijmegen, Netherlands
Nagtegaal, Iris D. (author)
Radboud Univ Nijmegen, Netherlands
Zlobec, Inti (author)
Univ Bern, Switzerland
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Dawson, Heather (author)
Univ Bern, Switzerland
Sheahan, Kieran (author)
St Vincents Univ Hosp, Ireland
Simmer, Femke (author)
Radboud Univ Nijmegen, Netherlands
Kirsch, Richard (author)
Univ Toronto, Canada
Vieth, Michael (author)
Friedrich Alexander Univ Erlangen Nuremberg, Germany
Lugli, Alessandro (author)
Univ Bern, Switzerland
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
Ciompi, Francesco (author)
Radboud Univ Nijmegen, Netherlands
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 (creator_code:org_t)
2023-03-30
2023
English.
In: Cancers. - : MDPI. - 2072-6694. ; 15:7
Related links:
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  • Journal article (peer-reviewed)
Abstract Subject headings
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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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