A transformer-based deep learning model for early prediction of biochemical recurrence after radical prostatectomy using pretreatment mpMRI
Author Block: F. Li1, L. Zhuo1, L. Yue1, L. Juan1, L. Wang1, R. Liu1, F. Wang2, Y. Xiang3; 1Mianyang City/CN, 2Luzhou/CN, 3Leshan/CN
Purpose: The purpose of this study is to develop and verify a deep learning model using preoperative multi-parameter MRI images to predict BCR risk after radical prostatectomy.
Methods or Background: Patients after radical surgery at 4 centers between August 2013 and September 2021 were retrospectively included with the endpoint outcome of 3-year BCR (two consecutive specific antigen [PSA] levels > 0.2 ng/mL [0.2µg/L]). A transformer-based DL model was used to predict BCR after radical surgery using 3D tumor images, a clinical model was constructed by multivariate logistic regression, Kaplan-Meier plots were used for estimating recurrence-free survival, and finally, pre- and post-surgical Capra models, a clinical model, a multi-instance model, and a transformer model, Multimodal Combine model were compared to assess the performance of predicting BCR.
Results or Findings: A total of 582 patients (median age 70 years, (IQR 44-89 years) with a median follow-up of 43 months (IQR, 29-71 months) were randomized into a training group (n=249 ), an internal test set (n=107), an external test set 1 (n=189), and an external test set 2 (n=37).The AUC of the Transformer model in the 0.92 in the internal test set, 0.84 in the external test set 1, and 0.82 in the external test set 2, and the multimodal Combine model further improves the performance, respectively, with 0.94 (95% CI. 0.885 - 0.992), 0.94 (95% CI, 0.900 - 0.969), and 0.83 (95% CI, 0.693 - 0.965), and early recurrence-free survival and overall survival could be better risk-stratified and predicted using the Combine model.
Conclusion: A transformer-based DL model for predicting BCR after radical surgery was developed and internally and externally validated, and the joint model is better and expected to guide individualized treatment.
Limitations: Not applicable.
Funding for this study: No funding was provided for this study
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: Ethics (2024)014-1