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A Novel Model for Muscle-Invasive Bladder Cancer Diagnosis Using Contrast-Enhanced Ultrasound.

Qiping Liu1, Xuwei Lu2, Chang He2

  • 1Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China.

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Summary
This summary is machine-generated.

This study developed a new model combining contrast-enhanced ultrasound (CEUS) radiomics and the Vesical Imaging Reporting and Data System (VI-RADS) score to accurately predict muscle-invasive bladder cancer (MIBC) before surgery.

Keywords:
Bladder cancerContrast-enhanced ultrasoundMuscle-invasiveRadiomics

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Accurate preoperative identification of muscle-invasive bladder cancer (MIBC) is crucial for treatment planning.
  • Distinguishing MIBC from non-muscle-invasive bladder cancer preoperatively is challenging with current methods.

Purpose of the Study:

  • To develop and validate a novel predictive model for MIBC.
  • To integrate contrast-enhanced ultrasound (CEUS) radiomics features with the Vesical Imaging Reporting and Data System (VI-RADS) score.

Main Methods:

  • Retrospective single-center study of 116 patients with bladder tumors.
  • Development of a predictive model using CEUS radiomics features and clinical variables (VI-RADS score).
  • Model validation using training and testing cohorts with XGBoost classifier.

Main Results:

  • Three CEUS radiomic features and the VI-RADS score were significant predictors of MIBC.
  • The combined model achieved an area under the curve (AUC) of 0.96, outperforming the radiomic-only model (AUC 0.79).
  • XGBoost classifier demonstrated superior performance in predicting MIBC.

Conclusions:

  • A combined model integrating CEUS radiomics and VI-RADS score shows potential for improved preoperative MIBC prediction.
  • Preliminary validation suggests the model's feasibility and clinical utility in differentiating MIBC.
  • Further prospective studies are warranted to confirm these findings.