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Characteristics of Abdominal Fat Based on CT Measurements to Predict Early Recurrence After Initial Surgery of NMIBC in Stage Ta/T1

  • 0Department of Urology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China.
Clinical Genitourinary Cancer +

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September 12, 2024

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September 12, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Abdominal fat measurements, particularly bilateral perirenal fat thickness, can predict early bladder cancer recurrence after surgery. A new model integrating fat and clinical factors shows improved accuracy for personalized treatment.

Area Of Science

  • Urology
  • Oncology
  • Radiology

Background

  • Nonmuscle-invasive bladder cancer (NMIBC) recurrence post-transurethral resection of bladder tumor (TURBT) is a clinical challenge.
  • Early identification of recurrence is crucial for timely intervention and improved patient outcomes.

Purpose Of The Study

  • To evaluate the predictive value of abdominal fat characteristics using computed tomography (CT) for early NMIBC recurrence within one year post-TURBT.
  • To develop and validate a predictive model integrating CT-measured fat features and clinical factors.

Main Methods

  • Retrospective analysis of 203 NMIBC patients.
  • Abdominal CT image analysis using 3D Slicer software.
  • Statistical analysis including logistic regression and Lasso algorithm; predictive efficacy assessed by ROC curve analysis and DCA.

Main Results

  • Significant differences in abdominal fat characteristics observed between recurrence and nonrecurrence groups.
  • Bilateral perirenal fat thickness (PrFT) demonstrated superior predictive performance.
  • A Lasso-based model integrating fat features (e.g., PrFT, visceral fat area) and clinical factors achieved an AUC of 0.904, outperforming existing models.

Conclusions

  • Abdominal fat characteristics, especially bilateral PrFT, are significant predictors of early recurrence in NMIBC patients.
  • The developed Lasso-based model offers enhanced predictive efficacy for early recurrence.
  • This model can aid in developing individualized treatment strategies for NMIBC management.

Keywords: