ArticleProceedings of SPIE--the International Society for Optical Engineering2024
Enhancing Colorectal Cancer Tumor Bud Detection Using Deep Learning from Routine H&E-Stained Slides.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- 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
- Article
- A full-automated tumor budding annotation approach in hematoxylin and eosin-stained whole slide images of colorectal cancer.NPJ precision oncology · 2025Article
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- Article
- 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Tumor budding refers to a cluster of one to four tumor cells located at the tumor-invasive front. While tumor budding is a prognostic factor for colorectal cancer, counting and grading tumor budding are time consuming and not highly reproducible. There could be high inter- and intra-reader disagreement on H&E evaluation. This leads to the noisy training (imperfect ground truth) of deep learning algorithms, resulting in high variability and losing their ability to generalize on unseen datasets. Pan-cytokeratin staining is one of the potential solutions to enhance the agreement, but it is not routinely used to identify tumor buds and can lead to false positives. Therefore, we aim to develop a weakly-supervised deep learning method for tumor bud detection from routine H&E-stained images that does not require strict tissue-level annotations. We also propose
Indexed as
Identifiers
What OpenQuestion holds
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.