• Shiyu Li, Xiuqin Ye, Hongtian Tian, Zhimin Ding, Chen Cui, Siyuan Shi, Yang Yang, Guoqiu Li, Jing Chen, Ziwei Lin, Zhipeng Ni, Jinfeng Xu, and Fajin Dong.
• Department of Ultrasound, The Second Clinical Medical College of Jinan University, China.
• Postgrad Med J. 2024 Mar 18; 100 (1182): 228236228-236.

PurposeWe 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.MethodsA 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.ResultsIn 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).ConclusionThe 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.© The Author(s) 2023. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine.

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