Sun Yi-Kang, Zhou Bo-Yang, Miao Yao, Shi Yi-Lei, Xu Shi-Hao, Wu Dao-Ming, Zhang Lei, Xu Guang, Wu Ting-Fan, Wang Li-Fan, Yin Hao-Hao, Ye Xin, Lu Dan, Han Hong, Xiang Li-Hua, Zhu Xiao-Xiang, Zhao Chong-Ke, Xu Hui-Xiong
Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumour, Shanghai Tenth People's Hospital, Ultrasound Institute of Research and Education, School of Medicine, Tongji University, Shanghai, China.
EClinicalMedicine. 2023 Jun 9;60:102027. doi: 10.1016/j.eclinm.2023.102027. eCollection 2023 Jun.
Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa.
Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545.
The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs.
3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted.
The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).
在活检前识别出具有临床意义的前列腺癌（csPCa）患者有助于减少不必要的活检并改善患者预后。传统经直肠超声（TRUS）对csPCa的诊断性能相对有限。本研究旨在基于整个前列腺的TRUS视频开发一种高性能卷积神经网络（CNN）模型（P-Net），并研究其在识别csPCa方面的有效性。
2021年1月至2022年12月期间，本研究前瞻性评估了来自四个中心的832例接受前列腺活检和/或根治性前列腺切除术的患者。所有患者均有标准化的全前列腺TRUS视频。使用训练队列（559例患者）构建二维CNN（2D P-Net）和三维CNN（3D P-Net），并在内部验证队列（140例患者）以及外部验证队列（133例患者）上进行测试。从受试者操作特征曲线（AUC）下面积、活检率和不必要活检率方面评估2D P-Net和3D P-Net在预测csPCa方面的性能，并与TRUS 5分制李克特评分系统以及多参数磁共振成像（mp-MRI）前列腺成像报告和数据系统（PI-RADS）v2.1进行比较。采用决策曲线分析（DCA）来确定使用它们的净效益。该研究已在https://www.chictr.org.cn注册，唯一标识符为ChiCTR2200064545。
在内部和外部验证队列中，3D P-Net的诊断性能（AUC：0.85 - 0.89）优于TRUS 5分制李克特评分系统（AUC：0.71 - 0.78，P = 0.003 - 0.040），与经验丰富的放射科医生解读的mp-MRI PI-RADS v2.1评分系统（AUC：0.83 - 0.86，P = 0.460 - 0.732）以及2D P-Net（AUC：0.79 - 0.86，P = 0.066 - 0.678）相似。活检率从40.3%（TRUS 5分制李克特评分系统）和47.6%（mp-MRI PI-RADS v2.1评分系统）降至35.5%（2D P-Net）和34.0%（3D P-Net）。不必要活检率从38.1%（TRUS 5分制李克特评分系统）和35.2%（mp-MRI PI-RADS v2.1评分系统）降至32.0%（2D P-Net）和25.8%（3D P-Net）。根据DCA，3D P-Net产生的净效益最高。
基于前列腺灰阶TRUS视频的3D P-Net在识别csPCa以及潜在减少不必要活检方面取得了令人满意的性能。有必要开展更多研究以确定人工智能模型如何更好地融入常规实践，并通过随机对照试验来展示这些模型在实际临床应用中的价值。
国家自然科学基金（项目编号82202174和82202153）、上海市科学技术委员会（项目编号18441905500和19DZ2251100）、上海市卫生健康委员会（项目编号2019LJ21和SHSLCZDZK03502）、上海市科技创新行动计划（项目编号21Y11911200）、中央高校基本科研业务费（项目编号ZD-11-202151）、复旦大学附属中山医院科研发展基金（项目编号2022ZSQD07）。
