Lou Wenjie, Chen Peizhe, Wu Chengyi, Liu Qinghua, Zhou Lingyan, Zhang Maoliang, Tu Jing, Hu Zhengbiao, Lv Cheng, Yang Jie, Qi Xiaoyang, Sun Xingbo, Du Yanhong, Liu Xueping, Zhou Yuwang, Liu Yuanzhen, Chen Chen, Wang Zhengping, Yao Jincao, Wang Kai
Department of Intervention, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
Front Oncol. 2025 Jun 27;15:1582035. doi: 10.3389/fonc.2025.1582035. eCollection 2025.
This study aimed to evaluate the effectiveness of deep-learning models using transrectal ultrasound (TRUS) video clips in predicting prostate cancer.
We manually segmented TRUS video clips from consecutive men who underwent examination with EsaoteMyLab™ Class C ultrasonic diagnostic machines between January 2021 and October 2022. The deep learning-inflated 3D ConvNet (I3D) model was internally validated using split-sample validation on the development set through cross-validation. The final performance was evaluated on two external test sets using geographic validation. We compared the results obtained from a ResNet 50 model, four ML models, and the diagnosis provided by five senior sonologists.
A total of 815 men (median age: 71 years; IQR: 67-77 years) were included. The development set comprised 552 men (median age: 71 years; IQR: 67-77 years), the internal test set included 93 men (median age: 71 years; IQR: 67-77 years), external test set 1 consisted of 96 men (median age: 70 years; IQR: 65-77 years), and external test set 2 had 74 men (median age: 72 years; IQR: 68-78 years). The I3D model achieved diagnostic classification AUCs greater than 0.86 in the internal test set as well as in the independent external test sets 1 and 2. Moreover, it demonstrated greater consistency in sensitivity, specificity, and accuracy compared to pathological diagnosis (kappa > 0.62, p < 0.05). It exhibited a statistically significant superior ability to classify and predict prostate cancer when compared to other AI models, and the diagnoses provided by sonologists (p<0.05).
The I3D model, utilizing TRUS prostate video clips, proved to be valuable for classifying and predicting prostate cancer.
本研究旨在评估使用经直肠超声（TRUS）视频片段的深度学习模型在预测前列腺癌方面的有效性。
我们手动分割了2021年1月至2022年10月期间使用百胜MyLab™ C类超声诊断仪进行检查的连续男性的TRUS视频片段。通过交叉验证，在开发集上使用拆分样本验证对深度学习增强的3D卷积神经网络（I3D）模型进行内部验证。使用地理验证在两个外部测试集上评估最终性能。我们比较了从ResNet 50模型、四个机器学习模型以及五位资深超声科医生提供的诊断中获得的结果。
共纳入815名男性（中位年龄：71岁；四分位间距：67 - 77岁）。开发集包括552名男性（中位年龄：71岁；四分位间距：67 - 77岁），内部测试集包括93名男性（中位年龄：71岁；四分位间距：67 - 77岁），外部测试集1由96名男性（中位年龄：70岁；四分位间距：65 - 77岁）组成，外部测试集2有74名男性（中位年龄：72岁；四分位间距：68 - 78岁）。I3D模型在内部测试集以及独立的外部测试集1和2中实现了大于0.86的诊断分类曲线下面积（AUC）。此外，与病理诊断相比，它在敏感性、特异性和准确性方面表现出更高的一致性（kappa>0.62，p<0.05）。与其他人工智能模型以及超声科医生提供的诊断相比，它在分类和预测前列腺癌方面表现出统计学上显著的优越能力（p<0.05）。
利用TRUS前列腺视频片段的I3D模型被证明在前列腺癌的分类和预测方面具有价值。
