Abstract 1775P
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.
Resources from the same session
1239P - NHS-Galleri trial enrolment approaches and participant sociodemographic diversity
Presenter: Charles Swanton
Session: Poster session 14
1241P - Decoding the glycan code: Pioneering early detection of non-small cell lung cancer through glycoproteomics
Presenter: Kai He
Session: Poster session 14
1242P - Implementing functional precision oncology in real-world patients: Translating extensive in vitro data into personalized treatment combining genetics and functional assays
Presenter: Dörthe Schaffrin-Nabe
Session: Poster session 14
1243P - Ocular surface squamous neoplasia early diagnosis using an AI-empowered autofluorescence multispectral imaging technique
Presenter: Abbas HABIBALAHI
Session: Poster session 14
1244P - AI-based accurate PD-L1 IHC assessment in non-small cell lung cancer across multiple sites and scanners
Presenter: Ramona Erber
Session: Poster session 14
1245P - A lymph nodal staging assessment model for various histologic types of small intestinal tumors
Presenter: YOUSHENG LI
Session: Poster session 14
1246P - Detection of alternative lengthening of telomeres (ALT) across cancer types based on tumor-normal multigene panel sequencing
Presenter: Juan Blanco Heredia
Session: Poster session 14
1247P - A detection model for EGFR mutations in lung adenocarcinoma patients based on volatile organic compounds
Presenter: Yunpeng Yang
Session: Poster session 14
1248P - Development of a high performance and noninvasive diagnostic model using blood cell-free microRNAs for multi-cancer early detection
Presenter: Jason Zhang
Session: Poster session 14
1249P - Whole genome sequencing-based cancer diagnostics in routine clinical practice: An interim analysis of two years of real-world data
Presenter: Jeffrey van Putten
Session: Poster session 14