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Updated: Feb 27, 2026

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A Multiphase CT-Based Integrated Deep Learning Framework for Rectal Cancer Detection, Segmentation, and Staging:

Tzu-Hsueh Tsai1, Jia-Hui Lin2, Yen-Te Liu3

  • 1Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 807, Taiwan.

Journal of Imaging
|February 26, 2026
PubMed
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This summary is machine-generated.

An AI system for rectal cancer staging using CT scans shows accuracy comparable to radiologists. This AI tool aids in treatment planning by improving rectal cancer detection and staging.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Radiology

Background:

  • Accurate rectal cancer staging is vital for treatment planning but challenging with computed tomography (CT) due to radiologist dependency.
  • Current CT interpretation for rectal cancer staging requires significant expertise, highlighting a need for improved diagnostic tools.

Purpose of the Study:

  • To develop and evaluate an AI-assisted system for enhancing rectal cancer detection and staging using CT images.
  • To integrate lesion detection, segmentation, and staging into a unified AI framework for rectal cancer management.

Main Methods:

  • A three-component AI framework was developed: RCD-CNN for lesion detection, U-Net for segmentation, and RCS-3DCNN for staging.
  • The system was trained and validated on CT scans from 223 rectal cancer patients, analyzing both non-contrast and contrast-enhanced studies.
Keywords:
artificial intelligencecancer stagingcomputed tomographyconvolutional neural networksdeep learningimage segmentationrectal cancer

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Main Results:

  • The AI system demonstrated high performance in detection (accuracy 0.976) and segmentation (Dice scores 0.897 and 0.856).
  • AI-based staging (80.4% concordance) showed no significant difference compared to radiologist-based staging (82.6% concordance) against pathology.
  • The AI system achieved staging accuracy comparable to that of expert radiologists.

Conclusions:

  • The developed AI-assisted system is a feasible decision-support tool for rectal cancer management.
  • This novel AI framework offers a unified workflow for CT-based rectal cancer detection, segmentation, and staging.
  • The AI system shows potential to assist radiologists in improving the accuracy and efficiency of rectal cancer staging.