1
|
Sun YK, Zhou BY, Miao Y, Shi YL, Xu SH, Wu DM, Zhang L, Xu G, Wu TF, Wang LF, Yin HH, Ye X, Lu D, Han H, Xiang LH, Zhu XX, Zhao CK, Xu HX. Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study. EClinicalMedicine 2023; 60:102027. [PMID: 37333662 PMCID: PMC10276260 DOI: 10.1016/j.eclinm.2023.102027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/22/2023] [Accepted: 05/12/2023] [Indexed: 06/20/2023] Open
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).
Collapse
Affiliation(s)
- Yi-Kang Sun
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Bo-Yang Zhou
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Yao Miao
- 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
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Yi-Lei Shi
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Shi-Hao Xu
- Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Dao-Ming Wu
- Department of Ultrasound, Fujian Provincial Hospital, Fujian, China
| | - Lei Zhang
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Guang Xu
- 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
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Ting-Fan Wu
- Bayer Healthcare, Radiology, Shanghai, China
| | - Li-Fan Wang
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hao-Hao Yin
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Xin Ye
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Dan Lu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hong Han
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Li-Hua Xiang
- 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
- Shanghai Engineering Research Center of Ultrasound in Diagnosis and Treatment, Shanghai, China
| | - Xiao-Xiang Zhu
- Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany
| | - Chong-Ke Zhao
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | | |
Collapse
|