Utilizing a deep learning-based AI algorithm to differentiate between diagnostic and non-diagnostic quality of prostate MRI facilitated a 10 percent higher specificity rate for diagnosing extraprostatic extension on multiparametric MRI, according to research presented at the recent RSNA conference.
Differentiating between diagnostic and non-diagnostic prostate magnetic resonance imaging (MRI) with artificial intelligence (AI) may significantly improve the detection of extraprostatic extension (EPE),
In a retrospective study, recently presented at the Radiological Society of North America (RSNA) conference, researchers reviewed data from 812 patients (median age of 62) who had multiparametric MRI (mpMRI) and a subsequent radical prostatectomy. Using a validated grading system for EPE on MRI, the study authors considered an EPE grade of 1 or above to be positive for EPE.
The researchers found that only 41 percent of positive EPE diagnoses on mpMRI led to confirmed EPE, according to the study.
When utilizing an AI algorithm to help discern image quality for T2 weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps, the researchers determined that 35 percent of the T2WI sequences (284 out of 812) and 32 percent of the ADC maps (260 out of 812) were non-diagnostic.
After comparing non-diagnostic and diagnostic T2WI scans, the researchers found that diagnostic quality T2WI scans had a 10 percent higher specificity rate (73 percent vs. 63 percent) and a seven percent lower false positive rate (21 percent vs. 28 percent) for EPE.
“Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that preoperative T2WI quality is crucial for accurate EPE evaluation,” wrote lead study author Yue Lin, BA, a MRSP scholar at the National Institutes of Health (NIH), and colleagues.
The study authors noted that utilizing AI to ascertain T2WI MRI quality may facilitate enhanced decision-making for patients with prostate cancer.
“Image quality assessment AI could be integrated into radiologist clinical workflow to improve prostate cancer imaging,” suggested Lin and colleagues.