An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer

Abstract

Purpose: We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. Methods: A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. Results: In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). Conclusion: The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy. Key messages: What is already known on this topic The application of artificial intelligence in transrectal ultrasound can assist in the diagnosis of prostate cancer (PCa). What this study adds Differing from previous studies, our study combined magnetic resonance imaging fusion targeted biopsy and systematic biopsy, in analyzing segmented needle tract ultrasound images of prostate biopsies. Excluding the interference of inaccurate region of interest outlining, a deep learning PCa diagnostic model with better diagnostic efficacy was constructed. How this study might affect research, practice, or policy Through our model screening, a greater number of suspected PCa patients who need further biopsy can receive the proper diagnosis and treatment, avoiding unnecessary biopsy in patients without PCa.