ArticleQuantitative imaging in medicine and surgery2025
Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
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28 authors.
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Abstract
Background: A new diffusion-weighted imaging (DWI) technique, known as zoomed-field-of-view echo-planar DWI (z-DWI), has been developed to reduce geometric distortions and susceptibility artifacts and to achieve higher spatial resolution. However, it remains unclear whether z-DWI, compared with the traditional DWI technique, can enhance the diagnostic performance of deep-learning-based computer-aided diagnosis (DL-CAD) and radiologists using DL-CAD in detecting prostate cancer (PCa). This study aims to evaluate and compare the diagnostic performance and PI-RADS scores of DL-CAD in detecting PCa using conventional full-field-of-view single-shot echo-planar DWI (f-DWI) and advanced z-DWI and to extend this comparison to clinical practice, in which radiologists use DL-CAD. Methods: This study retrospectively included magnetic resonance imaging from 359 patients for suspected PCa. There were 496 prostate lesions included, with 253 (51%) being malignant. Using a DL-CAD system, images of f-DWI and z-DWI were uploaded separately to obtain the localizations and the prostate imaging reporting and data system (PI-RADS) scores of suspected malignant lesions. The results were compared to histopathologic results. The diagnostic performance of f-DWI and z-DWI were evaluated using the free-response receiver operating characteristics and the alternative free-response receiver operating characteristics curves. Discrepancies in PI-RADS scores were analyzed. Additionally, two radiologists participated in consensus reading images by using DL-CAD with different DWI techniques, and their performance and PI-RADS scores were compared. Lastly, the relationship between PI-RADS discrepancies and clinically significant prostate cancer (csPCa) risk was analyzed. Results: z-DWI enabled DL-CAD to exhibit better diagnostic performance [area under the curve (AUC), 0.857 Conclusions: z-DWI, in comparison to f-DWI, enhances the diagnostic performance of DL-CAD for PCa, assigning higher PI-RADS scores to malignant lesions. Despite offering limited improvement for radiologists using DL-CAD, z-DWI shows promise in enhancing the detection of csPCa.
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