Evidence map›Paper›PMID 41524989›Full record

ArticleAbdominal radiology (New York)2026

Deep learning in enhanced CT imaging: predicting invasion depth of rectal adenocarcinoma.

Wenya Lin, Chengwei Liao, Huang Su, Qilong Wu

Abstract read
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wenya LinDepartment of Ultrasonography, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Chengwei LiaoDepartment of Gastroenterology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Huang SuDepartment of Gastroenterology, Wenzhou Central Hospital, Wenzhou, China.
Qilong WuDepartment of Ultrasonography, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. wql1993ssr@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aims to develop and evaluate a deep learning model, RectoDepthAI, that leverages enhanced CT images to accurately assess the tumor invasion depth in rectal adenocarcinoma, distinguishing between early-stage (submucosal or muscularis propria invasion) and advanced-stage (pararectal tissue or adjacent structure invasion) tumors.

methodUtilizing a dataset of 934 patients, RectoDepthAI integrates ResNet-18 for spatial feature extraction and Long Short-Term Memory (LSTM) for sequential processing of venous phase CT slices. Data management involved preprocessing, augmentation, and 5-fold cross-validation; evaluation metrics included AUC, accuracy, sensitivity, and specificity; interpretability was enhanced via Gradient-weighted Class Activation Mapping (Grad-CAM).

resultsIn its evaluation phase, RectoDepthAI was assessed over five rounds of training and validation, demonstrating robust performance with an average AUC of 0.883, accuracy of 84.1%, sensitivity of 87.0%, and specificity of 80.0%. Grad-CAM enhanced the interpretability of diagnostic predictions.

conclusionRectoDepthAI emerges as a promising tool for non-invasive staging of rectal adenocarcinoma, improving diagnostic precision and supporting clinicians in treatment decision-making.

Indexed as

AdenocarcinomaDeep LearningRadiographic Image Interpretation, Computer-AssistedRectal NeoplasmsTomography, X-Ray ComputedFemaleHumansMaleNeoplasm InvasivenessSensitivity and SpecificityCT imagingDeep learningInvasion depthPredictRectal adenocarcinoma

Identifiers

PMID41524989

What OpenQuestion holds

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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.