Machine learning prediction of prostate cancer from transrectal ultrasound video clips

Abstract

ObjectiveTo 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).MethodsWe 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.ResultsThe 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).ConclusionThe 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.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer

“…Wang conducted a machine-learning prediction study of prostate cancer, using transmittal ultrasound video clips, and found that the AUCs of the SVM model in the validation set and test set were 0.78 and 0.75, respectively, and the diagnostic efficiency of the SVM model was higher than that of MRA-based diagnosis (AUC was 0.78 vs. 0.65/0.75 and 0.75 vs. 0.65/0.72, respectively). Li found that the MRI-based SVM model had high diagnostic efficiency and stability ( 46 ). In this study, the accuracy of the SVM model for the diagnosis of clinically significant prostate cancer was 95%, and the AUC was 0.845, which is similar to the effect in Li’s study.…”

Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study

“…used a machine learning-based method to extract features from TRUS video clips to predict csPCa and achieved an AUC of 0.78. 41 However, these approaches required time-consuming manual preparation, which may not be suitable for routine clinical use. Our method requires the least amount of manual pre-processing and may be more advantageous for practical applications.…”

Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer

“…The ML-based imaging radiomics transforms visual image information into in-depth quantitative indicators, extracts a large amount of image feature information from medical images, and constructs predictive models based on feature information ( 16 , 17 ). In our previous study, prostate features were extracted from TRUS videos, and the SVM model constructed using ML algorithms was found to outperform MRI-based advanced radiologist diagnosis for PCa (AUC = 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set) ( 18 ). Techniques such as quantitative MRI analysis and computer-aided diagnosis have expanded the scope to analyze prostate MRI, and have been shown to improve diagnostic accuracy and reproducibility ( 19 – 21 ) and reduce inter-diagnostician variability by highlighting suspicious areas on MRI ( 22 ).…”