1775P - A newly-developed deep-learning algorithm: NAFNet outperforms ResNet50 for predicting adverse pathology events and biochemical recurrence time using MRI from prostate cancer patients

Background

Although deep learning algorithms have shown promising results in medical imaging, their application in predicting adverse pathology events (AP) and biochemical recurrence-free survival (bRFS) in prostate cancer patients is relatively lagging. Here we aimed to evaluate the performance of a novel deep learning network, NAFNet, in predicting AP and bRFS based on pre-treatment multiparametric MRI imaging.

Methods

This multicentre study enrolled 514 prostate cancer patients from six tertiary hospitals throughout China from 2017 and 2021. A total of 367 patients from Fudan University Shanghai Cancer Centre with whole-mount histopathology of radical prostatectomy specimens were assigned to the internal set, and cancer lesions were delineated with whole-mount pathology as the reference. The external test set included 147 patients with BCR data from five other institutes. The prediction model (NAFNet-classifier) and integrated nomogram (DL-nomogram) were constructed based on NAFNet. To evaluate the predictive ability of DL-nomogram for AP, we compared DL-nomogram with radiology score (PI-RADS), and clinical score (Cancer of the Prostate Risk Assessment score (CAPRA)). ROC curves and DCA analyses were performed to assess the AP prediction ability of various models, and survival analyses were also made for bRFS.

Results

After training and validation in the internal set, ROC curves in the external test set showed that NAFNet-classifier alone outperformed ResNet50 in predicting AP (AUC:0.799, 95%CI:0.724-0.873 vs. AUC:0.703, 95%CI:0.618-0.787, P=0.013). The DL-nomogram, including the NAFNet-classifier, clinical T stage and biopsy results, showed the highest AUC (0.915, 95% CI: 0.871-0.959) and accuracy (0.850) compared with the PI-RADS and CAPRA scores. Additionally, the DL-nomogram outperformed the CAPRA score with a higher C-index (0.732, P<0.001) in predicting bRFS.

Conclusions

Our newly-developed deep learning network, NAFNet, combined with clinical factors, accurately predicted AP and poor prognosis in prostate cancer patients from preoperative MRI imaging, providing a potential AI tools in medical imaging risk stratification.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

B. Dai.

Funding

The Medical Innovation Research Project of the Science and Technology Commission of Shanghai Municipality (20Y11905000) and the Discipline Leader of Shanghai Municipal Health Commission (2022XD01).

Disclosure

All authors have declared no conflicts of interest.