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Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation

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Abstract

The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients’ data from Siemens (Vendor 1), and the inner test set was 70 patients’ data from the same vendor. The external test set was 221 patients’ data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.

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Funding

This work was supported by National Natural Science Foundation of China [Grant Numbers: 61801474, 61801475]; CAS-VPST Slik Road Science Fund 2018 [Grant Number: GJHZ1857]; Science and Technology Plan Project of Tianjin [grant number: 19YDYGHZ00030]; Suzhou science and technology plan project [Grant Number: SYG201908, SS201863].

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Author notes

  1. Xuefu Ji and Jiayi Zhang are primary authors and contributed equally.

  2. Yuguo Tang and Wei Xia are the joint corresponding authors.

Authors and Affiliations

  1. The School of Electro-Optical Engineering, Changchun University of Science and Technology, No.7089 Weixing Road, Changchun, 130013, Jilin, China

    Xuefu Ji

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou, 215163, Jiangsu, China

    Xuefu Ji, Jiayi Zhang, Wei Shi, Yangchuan Liu, Xin Gao, Yuguo Tang & Wei Xia

  3. Department of Urology, The First Affiliated Hospital of SooChow University, No. 188 Shizi Street, Suzhou, 215006, Jiangsu, China

    Dong He, Xuedong Wei & Yuhua Huang

  4. Department of Radiology, The First Affiliated Hospital of SooChow University, No.188 Shizi Street, Suzhou, 215006, Jiangsu, China

    Jie Bao

  5. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No.155 Linong Street, Taipei, 100101, Taiwan, China

    Jyh-Cheng Chen

  6. School of Medical Imaging, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, 221004, Jiangsu, China

    Jyh-Cheng Chen

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  1. Xuefu Ji
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  11. Yuguo Tang
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Corresponding authors

Correspondence to Yuguo Tang or Wei Xia.

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Conflict of interest

The authors declared that they have no conflict of interest.

Ethical approval

Institutional Review Board approval was obtained in concordance with the standards of the First Affiliated Hospital of Soochow University Ethics Committee (2020; approval no. 064). All procedures performed in this study, which involved human participants, were in accordance with the ethical standards of the institutional and/or nation research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Approved by the ethics committee of the First Affiliated Hospital of Soochow University Ethics Committee, this retrospective analysis was involving MRI of patients in and informed consent was waived for all patients.

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Ji, X., Zhang, J., Shi, W. et al. Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation. Phys Eng Sci Med 44, 745–754 (2021). https://doi.org/10.1007/s13246-021-01022-1

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