Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study.

OBJECTIVES: To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives. METHODS: One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions. RESULTS: csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only. CONCLUSION: Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

MRI-informed prostate biopsy: What the radiologist should know on quality in biopsy planning and biopsy acquisition.

Quality is currently recognized as the pre-requisite for delivering the clinical benefits expected by magnetic resonance imaging (MRI)-informed prostate biopsy (MRI-i-PB) in patients with a suspicion for clinically significant prostate cancer (csPCa). The "quality chain" underlying MRI-i-PB is multidisciplinary in nature, and depends on several factors related to the patient, imaging technique, image interpretation and biopsy procedure. This review aims at making the radiologist aware of biopsy-related factors impacting on MRI-i-PB quality, both in terms of biopsy planning (threshold for biopsy decisions, association with systematic biopsy and number of targeted cores) and biopsy acquisition (biopsy route, targeting technique, and operator's experience). While there is still space for improvement and better standardization of several biopsy-related procedures, current evidence suggests that high-quality MRI-i-PB can be delivered by acquiring and increased the number of biopsy cores targeted to suspicious imaging findings and perilesional area ("focal saturation biopsy"). On the other hand, uncertainty still exists as to whether software-assisted fusion of MRI and transrectal ultrasound images can outperform cognitive fusion strategy. The role for operator's experience and quality assurance/quality control procedures are also discussed.

Unveiling the Potential of Venn Diagrams as a Helpful Tool for Clinical Reasoning: An Illustrative Case-based Discussion.

Venn diagrams graphically represent a cognitive approach that can assist in highlighting information shared by different data sets while eliminating nonoverlapping conditions. When applied to clinical reasoning, such an approach helps physicians visually focus on data pertaining to differential diagnoses. We present and discuss a 3-step reasoning pathway derived from a real-life case in which we used Venn diagrams to diagnose drug-related pneumonitis in a 67-year-old man with advanced bladder cancer and nodular lung findings at chest CT. This education paper supports using Venn diagrams in Radiology.