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A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer.

Methods

A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images. Two feature selection methods were performed and compared, the key of which are the Least Absolute Shrinkage and Selection Operator (LASSO) and the Max Relevance Min Redundancy (mRMR). The classifier of the prediction model was selected from six advanced machine-learning techniques. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were applied to assess the efficiency of the models.

Results

The models with the best performance for pathological invasion prediction and muscular invasion prediction consisted of LASSO as the feature selection method and random forest as the classifier. In the training set, the AUC of the pathological invasion model and muscular invasion model were 0.808 and 0.828. Furthermore, with the mRMR as the feature selection method, the external invasion model based on random forest achieved excellent discrimination (AUC, 0.857).

Conclusions

The full-sequence models demonstrated excellent accuracy for preoperatively predicting the bladder cancer invasion status.

Clinical relevance statement

This study introduces a full-sequence MRI model for preoperative prediction of the depth of bladder cancer infiltration, which could help clinicians to recognise pathological features associated with tumour infiltration prior to invasive procedures.

Key Points

• Full-sequence MRI prediction model performed better than Vesicle Imaging-Reporting and Data System (VI-RADS) for preoperatively evaluating the invasion status of bladder cancer.

• Machine learning methods can extract information from T1-weighted image (T1WI) sequences and benefit bladder cancer invasion prediction.

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Abbreviations

AUC:

Area under the curve

DCA:

Decision curve analysis

DCE:

DyGnamic contrast-enhanced

DWI:

DW images

GBDT:

Gradient boosting decision tree;

ICC:

Interclass correlation coefficients

KNN:

K-nearest neighbor

LASSO:

Least Absolute Shrinkage and Selection Operator

MIBC:

Muscle-invasive bladder cancer

MP-MRI:

Multiparametric magnetic resonance imaging

MRI:

Magnetic resonance imaging

mRMR:

Max Relevance Min Redundancy

NMIBC:

Non-muscle-invasive bladder cancer

RF:

Random forest

RFE:

Recursive feature elimination

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM:

Support vector machine

T1WI:

T1-weighted images

T2WI:

T2-weighted images

TUR:

Transurethral resection

VI-RADS:

Vesicle Imaging-Reporting and Data System

VOI:

Volume of interest

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Funding

This work was supported by the National Natural Science Foundation of China (81570607). The study sponsor had no roles in the study design, in the collection, analysis, and interpretation of data.

Author information

Authors and Affiliations

  1. Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China

    Guihua Chen, Tao Wang, Encheng Zhang, Jialiang Shao, Dongliang Zhang, Jian Zhang, Tuanjie Guo, Zhihao Yuan, Heting Tang, Yaoyu Yu, Jinyuan Chen & Xiang Wang

  2. Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Xuhui Fan

  3. Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200135, China

    Siteng Chen

Authors
  1. Guihua Chen
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  2. Xuhui Fan
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  3. Tao Wang
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  14. Xiang Wang
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Corresponding author

Correspondence to Xiang Wang.

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Guarantor

The scientific guarantor of this publication is Xiang Wang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Ethical permission for this study was granted by the Research Ethics Committee of Shanghai General Hospital (2022KY077). Written informed consent was obtained from each patient included. This study was performed in accordance with the Declaration of Helsinki.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

  • retrospective

  • observational

  • performed at one institution

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Chen, G., Fan, X., Wang, T. et al. A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth. Eur Radiol 33, 8821–8832 (2023). https://doi.org/10.1007/s00330-023-09960-y

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  • DOI: https://doi.org/10.1007/s00330-023-09960-y

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