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

Yi-Kang Sun; Bo-Yang Zhou; Yao Miao; Yi-Lei Shi; Shi-Hao Xu; Dao-Ming Wu; Lei Zhang; Guang Xu; Ting-Fan Wu; Li-Fan Wang; Hao-Hao Yin; Xin Ye; Dan Lu; Hong Han; Li-Hua Xiang; Xiao-Xiang Zhu; Chong-Ke Zhao; Hui-Xiong Xu; China Alliance of Multi-Center Clinical Study for Ultrasound (Ultra-Chance)

doi:10.1016/j.eclinm.2023.102027

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

EClinicalMedicine. 2023 Jun 9:60:102027. doi: 10.1016/j.eclinm.2023.102027. eCollection 2023 Jun.

Authors

Yi-Kang Sun¹, Bo-Yang Zhou¹, Yao Miao^{2

3}, Yi-Lei Shi⁴, Shi-Hao Xu⁵, Dao-Ming Wu⁶, Lei Zhang⁴, Guang Xu^{2

3}, Ting-Fan Wu⁷, Li-Fan Wang¹, Hao-Hao Yin¹, Xin Ye¹, Dan Lu¹, Hong Han¹, Li-Hua Xiang^{2

3}, Xiao-Xiang Zhu⁸, Chong-Ke Zhao¹, Hui-Xiong Xu¹; China Alliance of Multi-Center Clinical Study for Ultrasound (Ultra-Chance)

Affiliations

¹ Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
² 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.
³ Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China.
⁴ MedAI Technology (Wuxi) Co., Ltd., Wuxi, China.
⁵ Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China.
⁶ Department of Ultrasound, Fujian Provincial Hospital, Fujian, China.
⁷ Bayer Healthcare, Radiology, Shanghai, China.
⁸ Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany.

Abstract

Background: 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.

Methods: 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.

Findings: 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, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 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.

Interpretation: 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.

Funding: 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).

Keywords: Clinically significant prostate cancer; Three-dimensional convolutional neural network; Two-dimensional convolutional neural network; Ultrasound video.