Promoting the use of the PRECISE score for prostate MRI during active surveillance: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

OBJECTIVES: The PRECISE criteria for serial multiparametric magnetic resonance imaging (MRI) of the prostate during active surveillance recommend the use of a dedicated scoring system (PRECISE score) to assess the likelihood of clinically significant radiological change. This pilot study assesses the effect of an interactive teaching course on prostate MRI during active surveillance in assessing radiological change in serial imaging. METHODS: Eleven radiology fellows and registrars with different experience in prostate MRI reading participated in a dedicated teaching course where they initially evaluated radiological change (based on their previous training in prostate MRI reading) independently in fifteen patients on active surveillance (baseline and follow-up scan), and then attended a lecture on the PRECISE score. The initial scans were reviewed for teaching purposes and afterwards the participants re-assessed the degree of radiological change in a new set of images (from fifteen different patients) applying the PRECISE score. Receiver operating characteristic analysis was performed. Confirmatory biopsies and PRECISE scores given in consensus by two radiologists (involved in the original draft of the PRECISE score) were the reference standard. RESULTS: There was a significant improvement in the average area under the curve (AUC) for the assessment of radiological change from baseline (AUC: 0.60 [Confidence Intervals: 0.51-0.69] to post-teaching (AUC: 0.77 [0.70-0.84]). This was an improvement of 0.17 [0.016-0.28] (p = 0.004). CONCLUSIONS: A dedicated teaching course on the use of the PRECISE score improves the accuracy in the assessment of radiological change in serial MRI of the prostate

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia

International audienceCoronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach