ArticleCancers2023
Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 15 citations in OpenAlex.
- Digital Pathology and the AI-Based Quantification of the Tumor Microenvironment in Gastrointestinal Cancer: From Tumor Budding and Tumor-Infiltrating Lymphocytes to Tertiary Lymphoid Structures.International journal of molecular sciences · 2026Review
- Tumor Budding in Colorectal Carcinoma-From Incidental Observation to Prognostic Marker: Lessons Learned from Colorectal Cancer Assessment.Medicina (Kaunas, Lithuania) · 2026Review
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Assessing Tumour Budding in Lung Squamous Cell Carcinoma: A Comparative Analysis of Digital Whole-Slide Imaging and Light Microscopy.Pathobiology : journal of immunopathology, molecular and cellular biology · 2026Article
- Advances in Hereditary Colorectal Cancer: How Precision Medicine Is Changing the Game.Cancers · 2025Review
- Article
- Immunohistochemical analysis of tumor budding in stage II colon cancer: exploring zero budding as a prognostic marker.Virchows Archiv : an international journal of pathology · 2024Article
- Lymph node metastases and recurrence in pT1 colorectal cancer: Prediction with the International Budding Consortium Score-A retrospective, multi-centric study.United European gastroenterology journal · 2024Article
- Cutting-edge technology and automation in the pathology laboratory.Virchows Archiv : an international journal of pathology · 2024Review
- Enhancing Colorectal Cancer Tumor Bud Detection Using Deep Learning from Routine H&E-Stained Slides.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Establish a novel tumor budding-related signature to predict prognosis and guide clinical therapy in colorectal cancer.Scientific reports · 2024Article
- Predictive value of tumor budding in head and neck squamous cell carcinoma: an update.Virchows Archiv : an international journal of pathology · 2023Review
- Beyond H&E: CDX2 immunohistochemistry in tumor budding assessment of colorectal carcinoma.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 7 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.