Machine learning prediction of prostate cancer from transrectal ultrasound video clips

作者：Kai Wang, Peizhe Chen, Bojian Feng, Jing Tu, Zhengbiao Hu, Maoliang Zhang, Jie Yang, Ying Zhan, Jincao Yao, Dong Xu · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.948662 · 被引用次数：21 · 研究领域：Prostate Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

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 ...