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Updated: Jan 9, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Texture analysis enhances diagnostic accuracy of lesions scored as 5 in the Prostate Imaging Reporting and Data
Yan Bai1,2,3, Xin Ru Xie3, Ying Hou1,3
1The First Affi liated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, China.
Purpose:
Prostatitis is frequently observed in false-positive lesions scored as 5 in the Prostate Imaging Reporting and Data System (PI-RADS), necessitating improved diagnostic tools. This study investigated the potential of magnetic resonance imaging (MRI) texture analysis of apparent diffusion coefficient (ADC) sequences to enhance the differentiation of prostatitis from prostate cancer (PCa) in PI-RADS 5 lesions.
Methods:
This retrospective study enrolled patients undergoing 3.0-T MRI with lesions scored as PI-RADS 5. Lesions were manually delineated on ADC maps, and texture features were extracted using FireVoxel. Clinical data and ADC texture parameters were collected. The diagnostic performance [area under the curve (AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV)] of the clinical data, ADC texture, and a combined model were calculated and compared using the DeLong test.
Results:
The final cohort included 189 patients with 189 PI-RADS 5 lesions (164 PCa, 25 prostatitis). The combined model, incorporating clinical indicators (age, prostate-specific antigen density) and ADC texture parameters (signal coefficient of variation, ADC percentile), revealed the optimal diagnostic performance: SEN 98.7%, SPE 60.0%, PPV 97.9%, NPV 71.6%, and AUC 93.1%. Bootstrap resampling verified the robustness of the model. Decision curve analysis indicated an improved net benefit with the combined model for guiding biopsy decisions.
Conclusion:
ADC imaging texture parameters are valuable for the differential diagnosis of prostatitis from lesions scored as PI-RADS 5. Their combination with clinical indicators substantially improves diagnostic performance, providing valuable information to facilitate surgical decision-making and potentially reduce unnecessary biopsies.
Clinical Significance:
This study addresses a critical limitation of the current PI-RADS system, which exhibits a notable rate of false positives in high-risk PI-RADS 5 lesions. By demonstrating the added value of quantitative ADC texture analysis in this specific diagnostic challenge, this research offers a practical and potentially translatable approach to reducing the number of unnecessary biopsies for PI-RADS 5 lesions.
Insights
Magnetic resonance imaging (MRI) texture analysis of apparent diffusion coefficient (ADC) sequences can help differentiate prostatitis from prostate cancer (PCa) in PI-RADS 5 lesions. Combining ADC texture with clinical data significantly improves diagnostic accuracy, potentially reducing unnecessary biopsies.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions often include prostatitis, leading to false positives.
- Improved diagnostic tools are needed to differentiate prostatitis from prostate cancer (PCa) in PI-RADS 5 lesions.
Purpose of the Study:
- To investigate the utility of MRI apparent diffusion coefficient (ADC) texture analysis for distinguishing prostatitis from PCa in PI-RADS 5 lesions.
- To assess the diagnostic performance of ADC texture parameters, clinical data, and a combined model.
Main Methods:
- Retrospective analysis of 189 PI-RADS 5 lesions (164 PCa, 25 prostatitis) from 3.0-T MRI scans.
- Manual delineation of lesions on ADC maps and extraction of texture features using FireVoxel.
- Calculation and comparison of diagnostic performance metrics (AUC, sensitivity, specificity, PPV, NPV) for clinical data, ADC texture, and a combined model.
Main Results:
- The combined model, integrating clinical indicators (age, PSA density) and ADC texture parameters (signal coefficient of variation, ADC percentile), achieved high diagnostic performance: AUC 93.1%, sensitivity 98.7%, specificity 60.0%, PPV 97.9%, NPV 71.6%.
- Bootstrap resampling confirmed the model's robustness.
- Decision curve analysis demonstrated improved net benefit for guiding biopsy decisions with the combined model.
Conclusions:
- ADC imaging texture parameters are valuable for differentiating prostatitis from PI-RADS 5 lesions.
- Combining ADC texture analysis with clinical indicators significantly enhances diagnostic performance.
- This approach offers a practical method to reduce unnecessary biopsies for PI-RADS 5 lesions.
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