AI mapping is more effective than MRI in assessing prostate cancer extent, study says

A recent study showed that an artificial intelligence (AI) model capable of providing three-dimensional estimates of tumor and margins in prostate cancer improved delineation of tumor margins compared to magnetic resonance and marginal negativity in index tumors. It has been suggested that the accuracy of the probabilities may be improved. Imaging (MRI).

For retrospective studies, European Urology Open ScienceThe researchers used AI-enabled mapping (Unfold AI, Avenda Health) and conventional MRI-identified regions of interest to delineate prostate cancer tumor margins in 50 consecutive patients who underwent radical prostatectomy. ROI), 10 mm ROI margin and hemigrand margin were compared. .

The study found that the margins of the AI ​​model had a sensitivity of 96.9% to identify clinically significant prostate cancer (csPCa), compared with 37.4% for conventional MRI ROI, 93.2% for 10mm ROI expansion, It was 94.1% for the hemiglandular stump.

The images above show two cases reflecting negative margins for AI model assessment of prostate index lesions, but semiglandular margins were considered positive (cases AC) and 10 mm MRI regions of interest (ROI). stump is considered positive (Case DF). (Image courtesy: European Urology Open Science. )

The study authors noted that the mean specificity of the AI ​​model (51.8 percent) was significantly lower than the conventional MRI ROI (97.9 percent) and ROI margin of 10 mm (63.4 percent). However, the researchers also found that the average extent of csPCa vacancies in the AI ​​model was 1.6 mm, compared to 3.8 mm for hemigrand margins, 3.2 mm for 10 mm ROI margins, and 12.0 mm for conventional MRI ROIs. I also found The AI ​​software margin negative rate was also 90 percent for index lesions compared to 66 percent for hemilateral margin assessment.

The study authors emphasized that multiparameter MRI contouring protocols are not sufficient for targeted treatment of prostate cancer. According to the researchers, the study results also showed a significant advantage of AI mapping software over hemigrand margins.

“In the independent test set, AI margins outperformed hemiglandular margin negative rates for both index lesions (90% vs. 66%) and csPCa (80% vs. 56%),” Geoffrey A. Sonn, M.D. writes Dr. Associate professor of urology at Stanford University School of Medicine and his colleagues. “The combination of index lesion underestimation and accompanying lesions with csPCa caused the hemiglandular margin to miss csPCa in almost half of the cases.”

(Editor’s Note: For related content, seeCould emerging AI platforms replace traditional MRI in prostate cancer assessment?“”Study Shows Benefits of AI in Prostate Cancer Detection on Multiparametric MRI” and “FDA Approves RadNet’s Latest AI Software for Prostate MRI. ”)

Researchers believe that combining the benefits of AI software with the prognostic accuracy of the Encapsulated Confidence Score (ECS) for negative margins may improve risk assessment and treatment option selection for prostate cancer patients. Stated.

“This approach may help improve and standardize local treatment margins and reduce cancer recurrence rates,” said Sonn et al. “Furthermore, accurate assessment of the likelihood of negative margins and residual tumor risk by ECS may help facilitate informed decision-making for both patients and physicians.”

Beyond the limitations inherent in single-center studies, the investigators acknowledged that the study cohort consisted of patients who underwent radical prostatectomy. Therefore, these patients were more likely to have advanced prostate cancer than the average patient receiving local therapy, the study authors said. The researchers also acknowledged that the study algorithm did not include diffusion-weighted imaging, which has been shown to be strongly correlated with the presence of prostate tumors.

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