ArticleLa Radiologia medica2022
Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial.European radiology · 2026Trial
- Design of a magnetic resonance imaging-based screening program for early diagnosis of prostate cancer: preliminary results of a randomized controlled trial-Prostate Cancer Secondary Screening in Sapienza (PROSA).European radiology · 2024Trial
- Contrast-free MRI-first screening in young asymptomatic men: Prostate characteristics and cancer detection rates.Prostate cancer and prostatic diseases · 2026Article
- AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between Novice-Level and Experienced Readers.Journal of magnetic resonance imaging : JMRI · 2026Article
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Concurrent AI-human interaction in prostate cancer MRI interpretation: More hype than help?European radiology experimental · 2026Article
- Improving Early Prostate Cancer Detection Through Artificial Intelligence: Evidence from a Systematic Review.Cancers · 2025Review
- Diagnostic performance of a fully automated AI algorithm for lesion detection and PI-RADS classification in patients with suspected prostate cancer.La Radiologia medica · 2025Article
- The added value of artificial intelligence using Quantib Prostate for the detection of prostate cancer at multiparametric magnetic resonance imaging.La Radiologia medica · 2025Observational
- Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.American journal of clinical and experimental urology · 2025Review
- What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning.La Radiologia medica · 2024Review
- Ruptured Ovarian Cystic Teratoma: A Rare Diagnosis, Easily to Be Confused with Peritoneal Carcinomatosis.Medicina (Kaunas, Lithuania) · 2024Article
- The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS).Magma (New York, N.Y.) · 2024Review
- Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer.Gland surgery · 2023Review
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Low cancer yield in PI-RADS 3 upgraded to 4 by dynamic contrast-enhanced MRI: is it time to reconsider scoring categorization?European radiology · 2023Observational
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Dose Reduction Strategies for Pregnant Women in Emergency Settings.Journal of clinical medicine · 2023Review
- Is Artificial Intelligence Replacing Our Radiology Stars in Prostate Magnetic Resonance Imaging? The Stars Do Not Look Big, But They Can Look Brighter.European urology open science · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
objectiveTo investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. MATERIALS AND
methodsA total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group.
resultsInter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively).
conclusionsAI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
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