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Purpose <p>Predicting clinical complete response (CR) to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC) remains a clinical challenge. Existing CT-based radiomics studies have shown promise, but MRI-derived radiomics using machine learning (ML) has not been systematically explored. This study aimed to develop and validate ML-based radiomics models using multiparametric MRI and clinical data to predict CR in MIBC patients receiving NAC.</p> Materials and methods <p>MIBC patients eligible for platinum-based NAC were prospectively included. Tumor regions were manually segmented from pre-treatment MRI sequences (CE-T1WI, T2WI, DWI, ADC maps). Radiomics features and clinical variables were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection, and multiple ML classifiers were trained using stratified fivefold cross-validation. The&#xa0;area under the receiver operating characteristic curve&#xa0;(AUC-ROC), sensitivity, specificity, precision, and F1 scores were calculated.</p> Results <p>Among 52 patients, 19 (36.5%) achieved CR. Of 177 extracted features, CE-T1WI-derived models achieved the best performance. The support vector machine (SVM) yielded the highest AUC-ROC of 0.88, with sensitivity, specificity, and precision of 0.82, 0.79, and 0.79, respectively. The K-Nearest Neighbors (KNN) model performed comparably (AUC = 0.87). Clinical feature-based models also performed strongly (RF, AUC = 0.86).</p> Conclusions <p>ML-based radiomics models derived from multiparametric MRI sequences and clinical features hold substantial potential for predicting clinical CR to the NAC in MIBC patients. These results suggest that MR images can provide reliable insights into treatment response, offering a noninvasive and effective tool for clinical decision-making. This is the first prospective ML-based MRI radiomics study in this domain. We present this work as a proof-of-concept requiring external multicenter validation in a larger dataset to confirm these findings.</p>

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Machine learning-based radiomics using magnetic resonance images for prediction of clinical complete response to neoadjuvant chemotherapy in patients with muscle-invasive bladder cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Bahram Mofid,
  • Hamid Abdollahi,
  • Benyamin Khajetash,
  • Anya Jafari,
  • Abbas Basiri,
  • Mohammad Sajjad Zabihi,
  • Arash Mahdavi,
  • Abolfazl Razzaghdoust,
  • Bahareh Hatami

摘要

Purpose

Predicting clinical complete response (CR) to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC) remains a clinical challenge. Existing CT-based radiomics studies have shown promise, but MRI-derived radiomics using machine learning (ML) has not been systematically explored. This study aimed to develop and validate ML-based radiomics models using multiparametric MRI and clinical data to predict CR in MIBC patients receiving NAC.

Materials and methods

MIBC patients eligible for platinum-based NAC were prospectively included. Tumor regions were manually segmented from pre-treatment MRI sequences (CE-T1WI, T2WI, DWI, ADC maps). Radiomics features and clinical variables were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection, and multiple ML classifiers were trained using stratified fivefold cross-validation. The area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 scores were calculated.

Results

Among 52 patients, 19 (36.5%) achieved CR. Of 177 extracted features, CE-T1WI-derived models achieved the best performance. The support vector machine (SVM) yielded the highest AUC-ROC of 0.88, with sensitivity, specificity, and precision of 0.82, 0.79, and 0.79, respectively. The K-Nearest Neighbors (KNN) model performed comparably (AUC = 0.87). Clinical feature-based models also performed strongly (RF, AUC = 0.86).

Conclusions

ML-based radiomics models derived from multiparametric MRI sequences and clinical features hold substantial potential for predicting clinical CR to the NAC in MIBC patients. These results suggest that MR images can provide reliable insights into treatment response, offering a noninvasive and effective tool for clinical decision-making. This is the first prospective ML-based MRI radiomics study in this domain. We present this work as a proof-of-concept requiring external multicenter validation in a larger dataset to confirm these findings.