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Related Experiment Video

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Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
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A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter

Zongjie Wei1, Xuesong Bai1, Yingjie Xv1

  • 1Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Insights Into Imaging
|October 28, 2024
PubMed
Summary

This study developed a machine learning model using CT radiomics to predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer. The model offers a noninvasive method for preoperative prediction, aiding clinical decisions.

Keywords:
Bladder cancerComputed tomographyHER2Machine learningRadiomics

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Area of Science:

  • Radiology
  • Oncology
  • Machine Learning

Background:

  • Bladder cancer (BCa) diagnosis and treatment often rely on human epidermal growth factor receptor 2 (HER2) status.
  • Accurate preoperative prediction of HER2 status is crucial for guiding treatment strategies.

Purpose of the Study:

  • To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for preoperative prediction of HER2 status in BCa.
  • To assess the performance and interpretability of various ML models in predicting HER2 status.

Main Methods:

  • Retrospective analysis of 207 BCa patients with CT images.
  • Extraction and selection of radiomics features using LASSO regression.
  • Development and evaluation of five ML models (LR, SVM, KNN, XGBoost, RF) using AUC and accuracy.
  • Interpretability analysis using Shapley additive explanation (SHAP).

Main Results:

  • 11 discriminative radiomics features were identified from 1218 extracted features.
  • The Random Forest (RF) model achieved the highest AUC (0.815) and accuracy (75.5%) in the test set.
  • SHAP analysis highlighted texture features as significant predictors of HER2 status.

Conclusions:

  • A CT radiomics-based interpretable ML model can noninvasively predict HER2 status in BCa with satisfactory performance.
  • The developed model, particularly the RF classifier, demonstrates robust accuracy and interpretability, supporting its potential clinical utility.