Point-based risk prediction model reduces prostate biopsies by up to 20%
A point-based model for predicting clinically significant prostate cancer (csPCA) could significantly reduce unnecessary biopsies in men with elevated PSA levels.
The model incorporates data from MRI exams with patient risk factors to predict whether a patient is likely to develop csPCA. In doing so, it can reduce unnecessary biopsies by as much as 20%, according to the authors of a new study published in the Journal of the American College of Radiology.
“This novel approach provides a clinically transparent risk calculator that can provide ancillary support for providers and patients undergoing evaluation to determine the need for a biopsy,” corresponding author Ronilda Lacson, MD, PhD, from the department of radiology at Brigham and Women's Hospital in Boston, and colleagues noted.
The risk model combines data derived from imaging, such as PI-RADS score and PSA density (PSAD), with known factors like race, age and PSA level. The team defined csPCA as having a Gleason Sum of seven or greater.
The model was applied to the cases of 960 men who had undergone MRI between 2015 and 2019 and biopsy either six months prior or six months after their initial imaging. Using the patients’ medical records and imaging reports, the team calculated Gleason scores to determine which thresholds were most predictive of csPCA.
PI-RADS 4 and 5, the presence of extraprostatic extension and elevated PSAD were all observed to be indicative of csPCA. A threshold of two points was accurate in predicting csPCA, but cut unnecessary biopsies by just over 4%. Increasing the threshold to five points or higher, however, maintained sensitivity while also reducing potential biopsies by over 20%
“Once prospectively validated, availability of PI-RADS score, PSA density and extra-prostatic extension of the tumor in MRI reports can potentially be useful for patients and clinicians in performing csPCA risk estimation using a point-based system,” the authors wrote.
This model is much simpler than other risk prediction processes, the authors suggested, and can be a valuable tool that could contribute to shared decision-making between patients and providers in the future.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She began covering the medical imaging industry for Innovate Healthcare in 2021.