An AI model for prostate MRI that combines the best features of five separate algorithms helped radiologists diagnose clinically significant prostate cancer in a new study in JAMA Network Open.
The Prostate Imaging-Cancer AI consortium was formed to address a nagging problem in prostate cancer screening.
- Studies have shown that MRI can reduce biopsies and minimize workup of clinically insignificant disease, but it also has high inter-reader variability and requires a high level of expertise.
The PI-CAI challenge brought together researchers from multiple countries with a single goal: develop an AI algorithm for prostate MRI that would improve radiologists’ performance.
- Results were presented at RSNA and ECR conferences, as well as in a 2024 paper in Lancet Oncology that showed that individually the algorithms improved radiologist performance and generated fewer false positives.
But what if you combined the best of the PI-CAI algorithms into a single all-star AI model?
- Researchers did just that in the new study, combining the top five algorithms from the PI-CAI challenge into a single AI model in which each algorithm’s results were pooled to create an average detection map indicating the presence of prostate cancer.
To test the new algorithm, 61 readers from 17 countries interpreted 360 prostate MRI scans with and without the model.
- Patients in the test cohort had a median age of 65 years and a median PSA level of 7.0 ng/mL; 34% were eventually diagnosed with clinically significant prostate cancer.
Results of PI-CAI-aided prostate MRI were as follows …
- Radiologists using the algorithm had higher diagnostic performance than those who didn’t (AUROC=0.92 vs. 0.88).
- PI-CAI working on its own had the highest performance (AUROC=0.95).
- Sensitivity improved for cases rated as PI-RADS 3 or higher (97% vs. 94%).
- Specificity also improved (50% vs. 48%).
- AI assistance improved the performance of non-expert readers more than expert readers, with greater increases in sensitivity (3.7% vs. 1.5%) and specificity (4.3% vs. 2.8%).
The Takeaway
The new PC-CAI study is an important advance not only for prostate cancer diagnosis but also for the broader AI industry. It points to a future where multiple AI algorithms could be combined to tackle clinical challenges with better diagnostic performance than any model working alone.
