MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. KEY POINTS: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively.
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http://dx.doi.org/10.1007/s00330-024-10792-7 | DOI Listing |
Neurogastroenterol Motil
January 2025
Division of Gastroenterology, Rabin Medical Center, Beilinson Campus, Petah Tikva, Israel.
Background: Proton pump inhibitors (PPI) for gastroesophageal reflux disease (GERD) are associated with a high failure rate. Our uncontrolled feasibility study aimed determining the effect of a transcutaneous electrical stimulation system (TESS) on GERD symptoms and acid exposure time (AET).
Methods: Recruited patients with heartburn and regurgitation.
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
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January 2025
Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.
BJU Int
January 2025
Department of Urology, University of Alabama, Birmingham, AL, USA.
Objectives: To identify associations between 24-h urine abnormalities and clinical risk factors for recurrent stone formers.
Patients And Methods: The Registry for Stones of the Kidney and Ureter was queried for all patients who underwent 24-h urine studies. Patients were categorised by the number of clinical risk factors for recurrent stone disease.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
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