Work

Texture analysis enhances diagnostic accuracy of lesions scored as 5 in the Prostate Imaging Reporting and Data System in magnetic resonance imaging

Year: 2025

Type: article

Abstract: 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.

Source: Diagnostic and Interventional Radiology

DOI: 10.4274/dir.2025.253640

Authors: Yan Bai, Xin Ru Xie, Ying Hou, Yu Dong Zhang, Hai Bin Shi, Chen Jiang Wu

Institutions: Inner Mongolia University of Science and Technology, Jiangsu Province Hospital, Baotou Medical College, Nanjing Medical University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: National Natural Science Foundation of China