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September 17, 2025Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

Improving prediction of pathological downstaging in rectal cancer using deep learning with preoperative MRI and clinicopathological data

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Authors

SHSuk Hee HeoCMChung Man MoonChonnam National University Hwasun HospitalKPKyung Hwa ParkDokkyo Medical University

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Overview

Deep learning models enhance prediction of pathological downstaging in rectal cancer, suggesting improved assessment tools.

Key Points

  • Deep learning models achieve area-under-curve values between 0.800 and 0.817, outperforming traditional assessments.
  • Combining T2-weighted MRI with clinicopathological data enhances predictive accuracy for rectal cancer treatment response.
  • The approach includes data from 318 patients, validated through statistical tests and operating characteristic curves.
  • These models could significantly improve preoperative assessment for patients undergoing chemoradiotherapy in rectal cancer.

Cite This Study

Heo et al. (2025) studied this question.

synapsesocial.com/papers/68d45b0b31b076d99fa5d094https://doi.org/10.58530/2025/1391
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  1. 1Predicting treatment outcome and prognosis in locally advanced rectal cancer using pretreatment MRI and pathology based machine learning models2025
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  3. 3Automated deep learning model for predicting pathological complete response in rectal cancer: A tool to organ-preserving strategies2026
  4. 4Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T32024 · 6 citations
  5. 5Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning2024 · 2 citations