98%
921
2 minutes
20
Purpose: This study evaluated inter-/intra-reader agreement with the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 to improve the detection rate of prostate cancer.
Materials And Methods: We enrolled 210 patients who underwent multiparametric magnetic resonance imaging (mpMRI) for clinically suspected or diagnosed prostate cancer. Four readers, including two urologists, viewed patients' mpMRI and scored PI-RADS between two sessions, including the time for feedback and training after the first reading session. Inter- and intra-reader agreements were evaluated using Fleiss' kappa coefficient (κ), agreement coefficient 1 (AC1), and percentage of agreement (PA).
Results: The overall inter-reader agreement between all readers was moderate (κ=0.466, AC1=0.522, and PA=0.610). The overall inter-reader agreement improved in the second session. The agreement for peripheral zone (PZ) lesions was higher than that for transitional zone (TZ) lesions. At a PI-RADS cut-off of 4, the agreement for PZ lesions was almost perfect (PA=0.888) and higher than that for TZ lesions. The inter-reader agreement for lesions with a PI-RADS ≥4 and Gleason score ≥7 was almost perfect (AC1=0.960 and PA=0.964). The intra-reader agreement for lesions overall and PI-RADS ≥4 lesions were substantial (AC1=0.601) and almost perfect (PA=0.876), respectively.
Conclusions: Readers achieved moderate agreement for PI-RADS version 2.1 and benefitted from training sessions. Feedback, training, and multidisciplinary discussions also improved inter-reader agreement. Our study can provide guidance, updates, and further steps for the standardization and improvement of PI-RADS scoring.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.4111/icu.20250208 | DOI Listing |
Skeletal Radiol
September 2025
Department of Radiology, Federal University of Sao Paulo (UNIFESP), Napoleão de Barros St, 800, São Paulo, SP, 04024-000, Brazil.
Objective: To evaluate multiparametric MRI features of pediatric soft-tissue sarcomas, comparing pre-treatment and post-treatment features, and assessing correlation with clinical outcomes.
Materials And Methods: Retrospective cohort study, including pediatric patients (≤ 18 years) with histologically-confirmed soft-tissue sarcomas who underwent MRI with anatomic and functional sequences in consecutive series. Post-treatment MRI was available for a subset, and features were recorded by two readers.
Introduction: Precise prediction of pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT) in rectal cancer may identify candidates for non-operative management. The optimal selection of diagnostic tools is therefore of major clinical importance.
Methods: Clinical, laboratory, endoscopic and radiological data of patients with rectal cancer treated with nCRT and surgery at an academic medical center from 2010 to 2020 were retrospectively collected.
Eur J Radiol
August 2025
Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Beomeo-ri, Mulgeum-eup, Yangsan-si 626-770 Gyeongsangnam-do, Republic of Korea. Electronic address: kschoo061
Objectives: This study externally tests the performance of an artificial intelligence algorithm (AI) for diagnosing ascending aortic dilatation (AAD) using PA view chest radiography (PA CXR).
Materials And Methods: Two retrospectively collected cohorts with paired CXR/CT within 30 days (Group 1) and 90 days (Group 2) were gathered as external test sets. The performance of AI (DeepCatch X Aorta v1.
Investig Clin Urol
September 2025
Department of Urology, Pusan National University School of Medicine, Yangsan, Korea.
Purpose: This study evaluated inter-/intra-reader agreement with the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 to improve the detection rate of prostate cancer.
Materials And Methods: We enrolled 210 patients who underwent multiparametric magnetic resonance imaging (mpMRI) for clinically suspected or diagnosed prostate cancer.
J Cardiovasc Comput Tomogr
September 2025
Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Italy.
Objectives: To evaluate a deep-learning algorithm for automated coronary artery analysis on ultrahigh-resolution photon-counting detector coronary computed tomography (CT) angiography and compared its performance to expert readers using invasive coronary angiography as reference.
Methods: Thirty-two patients (mean age 68.6 years; 81 % male) underwent both energy-integrating detector and ultrahigh-resolution photon-counting detector CT within 30 days.