ArticleFrontiers in medicine2023
Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.
Article in Frontiers in medicine, 2023. 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, 11 citations in OpenAlex.
- Artificial intelligence-enabled MRI surveillance of prostate cancer: integrating PRECISE v2, PI-QUAL, and longitudinal biomarkers-a narrative review.Abdominal radiology (New York) · 2026Review
- Improving rectal tumor segmentation with anomaly fusion derived from anatomical inpainting: a multicenter study.Scientific reports · 2026Article
- Evaluation of Model Performance and Clinical Usefulness in Automated Rectal Segmentation in CT for Prostate and Cervical Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- A novel structural modeling magnitude and orientation radiomic descriptor for evaluating response to neoadjuvant therapy in rectal cancers via MRI.NPJ precision oncology · 2025Article
- How much data do you need? An analysis of pelvic multi-organ segmentation in a limited data context.Physical and engineering sciences in medicine · 2025Article
- Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review.Annals of coloproctology · 2024Review
- Deep learning for MRI lesion segmentation in rectal cancer.Frontiers in medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 1 country.
Funding
Abstract
Introduction: For locally advanced rectal cancers, Methods: This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T Results: In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T Discussion: Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T
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.