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Study Finds No Impact for Deep Learning Software in PI-RADS Scoring or Diagnosis of Prostate Cancer


In a recent study of a deep learning software in patients with suspected prostate cancer, researchers noted no difference in the detection of clinically significant prostate cancer nor PI-RADS scoring consistency.

The use of an adjunctive deep learning (DL) software made no difference in the diagnosis of clinically significant prostate cancer (csPCa) with bi-parametric magnetic resonance imaging (MRI), according to newly published research. The study authors also noted the DL software showed no difference in the consistency of scoring with the Prostate Imaging-Reporting and Data System (PI-RADS) for radiologists of varying experience levels.

For the retrospective study, published in Insights into Imaging, researchers assessed the use of deep learning (DL) software (Prostate AI, version Syngo.Via VB60, Siemens Healthineers) in 153 men (mean age of 63.59) who had bi-parametric MRI due to suspicion of prostate cancer (PCa). Four radiologists of varying experience, ranging from two years of experience to more than 20 years of experience, performed 3T MRI exams with and without the DL software, according to the study.

After utilizing the DL software, the researchers found that the four reviewing radiologists changed their initial PI-RADS score assessment a total of six times.

In assessing the area under the receiver operating curve (AUROC) for the detection of csPA, the study authors said the DL software showed no difference in the AUROC for any of the radiologists. They also noted significantly higher AUROCs for the radiologist with more than 20 years of experience (92 percent) and a radiologist with five years of experience (85 percent) in comparison to standalone use of the DL software (74 percent).

“Overall, the radiologists changed their initial PI-RADS scores in ~ 1% of the scans with the DL software, and radiologists with > 5 years of experience provided a statistically higher performance in identifying csPCa than the DL software. Furthermore, the DL software did not improve radiologists’ performance in identifying csPCa,” wrote study co-author Ercan Karaarslan, M.D., a professor of radiology at the School of Medicine at Acibadem Mehmet Ali Aydinlar University and director of radiology at the Acibadem Maslak Hospital in Istanbul, Turkey, and colleagues.

(Editor’s note: For related content, see “Study Says Targeted Biopsy with MRI Reduces Overdiagnosis of Prostate Cancer by Half” and “Could a New PSMA PET Agent Improve Detection of Distant Metastatic Lesions in Patients with Prostate Cancer?”)

The study authors also pointed out that the DL software rarely assigned a PI-RADS 3 score whereas the most experienced reviewing radiologist assigned a PI-RADS 3 score in approximately 25 percent of the cases.

“We suggest that the potential underlying factors that lead the DL software to assign PI-RADS 3 to only a minority of the patients and whether this tendency is beneficial (e.g., sparing patients from unnecessary biopsy or identifying clinically insignificant cancers) should be investigated in future work,” noted Karaarslan and colleagues.

The researchers acknowledged the limitations of a small sample size with the study data derived from a single tertiary facility. Pointing out that the DL software assessed in the study was not compatible with contrast-enhanced images, the authors employed bi-parametric MRI despite multiple guidelines recommending multiparametric MRI over bi-parametric MRI. Accordingly, Karaarslan and colleagues said general extrapolation of their results may be limited with multiparametric MRI being routinely utilized in patient care.

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