Abstract #4809

Improving PI-RADS rating with Zoomed Diffusion-Weighted Imaging in Deep Learning CAD Systems

Haining Long¹, Wangshu Zhu¹, Lei Hu^2,3, Liming Wei¹, Lisong Dai¹, Caixia Fu⁴, Yichen Lu⁵, Cancan Xu⁶, Zhonghua Hu⁷, Zhihan Xu⁸, Robert Grimm⁹, Heinrich von Busch¹⁰, Thomas Benkert⁹, Pengyi Xing¹¹, and Jungong Zhao¹

¹Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, ²Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University,, Guangzhou, China, ³Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China, ⁴MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, ⁵Digital Health, Siemens Healthineers Digital Technology (Shanghai) Co., Ltd., Shanghai, China, ⁶Digital Development, Siemens Digital Medical Technology (Shanghai) Co., Ltd., Shanghai, China, ⁷Digital & Automation, Siemens Shanghai Medical Equipment Ltd., Shanghai, China, ⁸DI CT Collaboration, Siemens Healthineers, Shanghai, China, ⁹MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany, ¹⁰Digital & Automation Innovation, Siemens Healthcare GmbH, Erlangen, Germany, ¹¹Department of Radiology, 989th Hospital of The Joint Logistic Support Force of the Chinese People's Liberation Army, Henan Province, China

Synopsis

Keywords: DWI/DTI/DKI, Prostate, Cancer; PI-RADS rating; DL-CAD; Zoom-DWI

Motivation: The clinical accuracy of the Prostate Imaging Reporting and Data System (PI-RADS) rating by deep-learning-based computer-aided diagnosis (DL-CAD) models need further enhancement for improved prostate cancer (PCa) detection and fewer unnecessary biopsies.

Goal(s): This study aimed to achieve more precise PI-RADS rating for PCa lesions by using zoomed diffusion-weighted imaging (z-DWI) in DL-CAD models.

Approach: We compared the diagnostic performance and PI-RADS rating of DL-CAD using advanced z-DWI vs. conventional DWI and extended this analysis to radiological practice.

Results: z-DWI improved the PI-RADS rating of PCa lesions by DL-CAD based on superior diagnostic performance compared with conventional DWI.

Impact: Deep-learning-based computer-aided diagnosis using zoomed diffusion-weighted imaging provides more accurate PI-RADS rating than conventional DWI, correlating MRI-detected lesions with prostate cancer (PCa) from biopsy. This can help minimize unnecessary biopsies for benign lesions while facilitating timely PCa treatment.

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