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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Interpretable MRI-based Multiparametric Radiomics for Preoperative Prediction of CMS4 Colorectal Cancer.

Zonglin Liu1,2, Wenchao Gu3, Liheng Liu4

  • 1Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, Shanghai 200032, China.

Radiology
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

A novel machine learning radiomics model accurately predicts CMS4 status in colorectal cancer (CRC) using MRI, outperforming deep learning models and identifying patients at higher risk for metastasis.

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Area of Science:

  • Radiomics and Machine Learning in Oncology
  • Molecular Subtyping of Colorectal Cancer
  • Biomarker Discovery using Medical Imaging

Background:

  • Consensus Molecular Subtypes (CMSs) are crucial for colorectal cancer (CRC) prognosis and treatment.
  • Effective noninvasive methods for identifying CMS4, a subtype associated with poor outcomes, are limited.
  • Accurate CMS4 identification is essential for personalized therapeutic strategies in CRC.

Purpose of the Study:

  • To develop and validate a radiomics-based machine learning model for predicting CMS4 status in CRC noninvasively.
  • To assess the biologic relevance and interpretability of radiomics features identified by the model.
  • To compare the performance of the radiomics model against deep learning approaches for CMS4 prediction.

Main Methods:

  • Retrospective analysis of multiparametric MRI (T2WI and CE-T1WI) data from 253 CRC patients across multiple centers.
  • Development of an MRI radiomics CMS4 score (MRC4s) using a machine learning model trained on a subgroup of patients.
  • Validation of MRC4s performance using internal and external test sets and comparison with deep learning models (ResNet50, VGG16, DenseNet201).

Main Results:

  • The merged MRC4s model, integrating T2WI and CE-T1WI features, achieved high predictive performance (AUCs of 0.85 and 0.84 in internal and external test sets, respectively).
  • MRC4s significantly outperformed deep learning models (AUC range, 0.70-0.75; P < .01), demonstrating superior accuracy in CMS4 prediction.
  • Higher MRC4s scores were associated with an increased risk of recurrent metastasis (HR, 5.96; P < .001) and linked to TGF-β and EMT pathways via transcriptomic analysis.

Conclusions:

  • A preoperative multiparametric MRI radiomics model (MRC4s) can accurately predict CMS4 status in colorectal cancer with strong performance.
  • The model offers biologic interpretability, correlating with known aggressive cancer pathways like TGF-β and epithelial-mesenchymal transition.
  • This radiomics approach provides a promising noninvasive tool for CMS4 identification, potentially guiding treatment decisions and improving patient outcomes in CRC.