Wang Kai, Chen Peizhe, Feng Bojian, Tu Jing, Hu Zhengbiao, Zhang Maoliang, Yang Jie, Zhan Ying, Yao Jincao, Xu Dong
Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China.
College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
Front Oncol. 2022 Aug 26;12:948662. doi: 10.3389/fonc.2022.948662. eCollection 2022.
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).
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.
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 (< 0.05).
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.
利用整个前列腺的经直肠超声视频片段构建前列腺癌（PCa）的机器学习（ML）预测模型，并将其诊断性能与磁共振成像（MRI）进行比较。
我们系统整理了501例患者的数据，其中276例患有前列腺癌，225例患有良性病变。从最终筛选出的231例患者（118例患有前列腺癌，113例患有良性病变）中，我们随机选择170例用于训练和验证机器学习模型，同时使用其余61例来测试派生模型。我们从超声视频片段中提取了851个特征。在用最小绝对收缩和选择算子（LASSO）回归进行降维后，最终选择了14个特征，并使用支持向量机（SVM）和随机森林（RF）算法基于这些特征建立了放射组学模型。此外，我们创造性地提出了一种由SVM、RF和基于MRI的放射科医生诊断组成的机器学习模型辅助诊断算法（MLAD），以评估ML模型在计算机辅助诊断（CAD）中的性能。我们通过采用受试者工作特征曲线（ROC）分析，评估了ML模型以及基于MRI的放射科医生诊断的曲线下面积（AUC）、敏感性、特异性和准确性。
验证集和测试集中SVM诊断PCa的AUC、敏感性、特异性和准确性分别为0.78、63%、80%；0.75、65%和67%。此外，发现SVM模型优于基于MRI的资深放射科医生（SR，超过10年经验）的诊断（验证集中AUC为0.78对0.75，测试集中为0.75对0.72），差异具有统计学意义（<0.05）。
由ML算法构建的预测模型对前列腺癌具有良好的诊断效率。SVM模型的诊断效率优于MRI，具有更具针对性的应用价值。总体而言，这些预测模型可以帮助放射科医生做出更好的诊断。
