ArticleCancers2021
Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 21 citations in OpenAlex.
- Impact of stromal maturity and proportion on prognosis and immune landscape in colorectal cancer.Annals of medicine · 2026Article
- Establishment of a Model to Predict the Prognosis of Endometrial Carcinoma Using Tumor-Infiltrating Lymphocytes Evaluated With Artificial Intelligence: A Retrospective Analysis.Cancer reports (Hoboken, N.J.) · 2026Article
- Tumor-Associated Neutrophils and Desmoplastic Reaction in the Breast Cancer Tumor Microenvironment: A Comprehensive Review.Cancers · 2026Review
- Prognostic scoring system based on indicators reflecting the tumor glandular differentiation and microenvironment for patients with colorectal cancer.Scientific reports · 2024Article
- Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers.Digestion · 2024Review
- Desmoplastic Reaction Associates with Prognosis and Adjuvant Chemotherapy Response in Colorectal Cancer: A Multicenter Retrospective Study.Cancer research communications · 2023Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- The relationship between primary colorectal cancer histology and the histopathological growth patterns of corresponding liver metastases.BMC cancer · 2022Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Desmoplastic Reaction, Immune Cell Response, and Prognosis in Colorectal Cancer.Frontiers in immunology · 2022Article
- Stromal composition predicts recurrence of early rectal cancer after local excision.Histopathology · 2021Article
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 at 3 institutions in 2 countries.
Funding
Abstract
The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (
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.