A new Radiology Journal study showed that VERDICT MRI-based analysis could significantly improve prostate cancer lesion characterization, and might solve PCa screening’s unnecessary biopsy problem.
Before we jump into the study… VERDICT MRI (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor) is a novel diffusion MRI modeling technique that estimates microstructural tissue properties, and has shown promise for cancer diagnosis and assessments. It can also be performed using standard 3T MRI exams.
The UK-based researchers had 165 men with suspected prostate cancer undergo mpMRI and VERDICT MRI (73 later confirmed w/ significant PCa). Over the 3.5yr study, they found that VERDICT MRI-based ‘lesion fractional intracellular’ volumes (FICs) have significant characterization advantages versus mpMRI-based apparent diffusion coefficient and PSA density measurements (ADC & PSAD):
- VERDICT MRI-based FICs classified clinically significant prostate cancer lesions far more accurately than ADC and PSAD (AUCs: 0.96 vs. 0.85 & 0.74).
- VERDICT-based FICs also clearly differentiated clinically insignificant and significant prostate cancer among the study’s Likert 3 lesions (median FICs: 0.53 & 0.18) and Likert 4 lesions (median FICs: 0.60 & 0.28), while ADC and PSAD measurements couldn’t be used to show which of these lesions would be cancerous.
Noting that up to 50% of men with positive PI-RADS scores or >3 Likert scores end up with negative biopsy results, these findings suggest that VERDICT MRI could reduce unnecessary prostate biopsies by a whopping 90%.
That makes this study a “massive leap forward” for prostate cancer diagnostics, and provides enough evidence to make VERDICT MRI just one successful large multi-center trial away from clinical adoption.
A new European Radiology study showed that Siemens Healthineers’ AI-RAD Companion Prostate MR solution can improve radiologists’ lesion assessment accuracy (especially less-experienced rads), while reducing reading times and lesion grading variability.
The researchers had four radiologists (two experienced, two inexperienced) assess lesions in 172 prostate MRI exams, with and without AI support, finding that AI-RAD Companion Prostate MR improved:
- The less-experienced radiologists’ performance, significantly (AUCs: 0.66 to 0.80 & 0.68 to 0.80)
- The experienced rads’ performance, modestly (AUCs: 0.81 to 0.86 & 0.81 to 0.84)
- Overall PI-RADS category and Gleason score correlations (r = 0.45 to 0.57)
- Median reading times (157 to 150 seconds)
The study also highlights Siemens Healthineers’ emergence as an AI research leader, leveraging its relationship / funding advantages over AI-only vendors and its (potentially) greater focus on AI research than its OEM peers to become one of imaging AI’s most-published vendors (here are some of its other recent studies).
Given the role that experience plays in radiologists’ prostate MRI accuracy, and noting prostate MRI’s historical challenges with variability, this study makes a solid case for AI-RAD Companion Prostate MR’s ability to improve rads’ diagnostic performance (without slowing them down). It’s also a reminder that Siemens Healthineers is serious about supporting its homegrown AI portfolio through academic research.
University of Chicago researchers provided solid evidence that hybrid multidimensional MRI (HM-MRI) might be superior to multiparametric MRI (mpMRI) for diagnosing clinically significant prostate cancer.
That’s a big statement after nearly two decades of prostate MRI exams, but mpMRI’s continued variability challenges still leave room for improvement, and some believe HM-MRI’s quantitative approach could help add objectivity.
To test that theory, the researchers had four radiologists with different career experience (1 to 20yrs) interpret HM-MRI and mpMRI exams from 61 men with biopsy-confirmed prostate cancer, finding that the HM-MRI exams produced:
- Higher AUCs among three of the four readers (0.61 vs. 0.66; 0.71 vs. 0.60; 0.59 vs. 0.50; 0.64 vs. 0.46), with the least experienced rad achieving the greatest AUC improvement
- Higher specificity among all four readers (48% vs. 37%; 78% vs. 26%; 48% vs. 0%; 46% vs. 7%)
- Significantly greater interobserver agreement rates (Cronbach alpha: 0.88 vs. 0.26; >0.60 indicates reliability)
- Far shorter average interpretation times (73 vs. 254 seconds)
As the study’s editorial put it, HM-MRI appears to be a “quantitative step in the right direction” for prostate MRI, and has the potential to address mpMRI’s variability, accuracy, and efficiency challenges.