Cancer medicine
Transformer-Based Deep Learning Integrating Multi-Regional MRI and Clinical Features for Predicting Biochemical Recurrence After Radical Prostatectomy: A Multicenter Study
By Zhou M, Lu K, Liu Y and 6 others.
Abstract
Background
Existing deep learning models for predicting biochemical recurrence (BCR) after radical prostatectomy often ignore the biological context beyond the tumor. This study aims to develop and validate a transformer-based model combining multi-regional magnetic resonance imaging (MRI) features and clinical data to improve BCR prediction after radical prostatectomy in prostate cancer (PCa).
Methods
We enrolled 232 Pca patients from three medical centers between 2013 and 2020. Multi-parametric MRI sequences, including T2-weighted fat-suppressed and diffusion-weighted imaging, were collected before surgery. Patients from centers 1 and 2 were assigned to a discovery cohort (training: n = 146; internal validation: n = 36), and patients from center 3 (n = 50) formed an external test cohort. A transformer-based deep learning (DL) model was developed using imaging features from intratumoral, peritumoral, and periprostatic regions, combined with clinical variables. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity with 95% confidence intervals (CI), and prognostic value was assessed via Kaplan-Meier (K-M) survival analysis.