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

Bokhorst JM, Nagtegaal ID, Zlobec I, Dawson H, Sheahan K, Simmer F, Kirsch R, Vieth M, Lugli A, van der Laak J, Ciompi F (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Cancers MDPI

Book Volume: 15

Article Number: 2079

Journal Issue: 7

DOI: 10.3390/cancers15072079

Abstract

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.

Authors with CRIS profile

Michael Vieth Institut für Lehre und Forschung am Medizincampus Oberfranken

Additional Organisation(s)

FAU Profilzentrum Licht.Materie.Quantentechnologien (FAU LMQ)

Involved external institutions

Radboud University Nijmegen Medical Centre / Radboudumc of voluit Radboud Universitair Medisch Centrum (UMC) NL Netherlands (NL) University of Toronto CA Canada (CA) Universität Bern CH Switzerland (CH) University College Dublin (UCD) IE Ireland (IE)

How to cite

APA:

Bokhorst, J.M., Nagtegaal, I.D., Zlobec, I., Dawson, H., Sheahan, K., Simmer, F.,... Ciompi, F. (2023). Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer. Cancers, 15(7). https://dx.doi.org/10.3390/cancers15072079

MLA:

Bokhorst, John Melle, et al. "Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer." Cancers 15.7 (2023).

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