Incorporating functional soft tissue deformations in AI model training for spatially accurate prostate cancer detection.

PURPOSE: To increase performance and generalization ability of artificial intelligence prostate cancer detection systems by simulating physiological size changes of the bladder and rectum and, thereby, associated deformations of the prostate and its lesions.

MATERIALS AND

METHODS

This retrospective study included 1028 bi-parametric MRI examinations of men (age range: 40-90 years) performed between 2014 and 2019, divided into training/test sets (771/257). We integrated an 'anatomy-informed' transformation into the training of nnU-Net, by simulating soft-tissue deformations of the prostate resulting from size changes of the rectum and bladder. The effects of these strategies were evaluated using free-response receiver operating characteristic (FROC) to assess lesion-level performance, along with a variant: weighted alternative FROC (wAFROC), which prioritizes patient-level effects with localization criteria. Change in sensitivity was tested using a clustered McNemar test. Patient-level performance was assessed with standard and localized receiver operating characteristics (ROC/LROC) analysis.

RESULTS

On the independent test set, the anatomy-informed model simulating changes of both rectum and bladder significantly increased lesion-level detection of true positive lesions by 18.8% (from 48 to 57, p = 0.01) and demonstrated significantly higher performance in the wAFROC analysis (from 0.597 to 0.639, p < 0.01). Patient-level ROC increased slightly (from 0.779 to 0.782, p = 0.89), while LROC analysis demonstrated increased performance (from 0.471 to 0.546).

CONCLUSION

Simulation of rectum and bladder size variations during model training led to significant improvement in lesion detection performance, which may be crucial for diagnostics and therapeutic measures depending on correct lesion localization, e.g. MRI-guided biopsies or focal therapy regimes.