Machine learning prediction of prostate cancer from transrectal ultrasound video clips

Kai Wang; Peizhe Chen; Bojian Feng; Jing Tu; Zhengbiao Hu; Maoliang Zhang; Jie Yang; Ying Zhan; Jincao Yao; Dong Xu

doi:10.3389/fonc.2022.948662

Machine learning prediction of prostate cancer from transrectal ultrasound video clips

Front Oncol. 2022 Aug 26:12:948662. doi: 10.3389/fonc.2022.948662. eCollection 2022.

Authors

Kai Wang¹, Peizhe Chen², Bojian Feng^{3

4}, Jing Tu¹, Zhengbiao Hu¹, Maoliang Zhang¹, Jie Yang¹, Ying Zhan¹, Jincao Yao^{3

4

5}, Dong Xu^{3

4

5

6}

Affiliations

¹ Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China.
² College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
³ Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China.
⁴ Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
⁵ Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China.
⁶ Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China.

Abstract

Objective: To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).

Methods: We systematically collated data from 501 patients-276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists' diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists' diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.

Results: The AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists' (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).

Conclusion: The prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model's diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

Keywords: artificial intelligence; machine learning; prostate cancer; support vector machine; ultrasound.