A CT-based deep learning model to predict local recurrence-free survival in primary retroperitoneal sarcoma.

Background: Survival prediction using radiomics and deep learning (DL) has shown promise, but its utility for predicting local recurrence among patients with primary retroperitoneal sarcoma (RPS) remains unexplored. This study sought to construct a DL framework leveraging preoperative CT to predict local recurrence-free survival (LRFS) in RPS. Methods: We retrospectively enrolled 115 primary RPS patients (2013-2024), splitting into training (N = 86) and validation (N = 29) sets. An end-to-end DL model was designed to forecast LRFS using contrast-enhanced CT images. The DL-based score (DL-score) was contrasted with a conventional handcrafted radiomics model (Rad-score) and clinical model. Integrated models combining DL-score or Rad-score with clinicopathological factors were constructed (DLCM and RSCM). Model evaluation included the C-index, time-dependent ROC, calibration, decision curve analysis and survival analysis. Results: The DL-score outperformed Rad-score and clinical model, yielding higher C-index (training: 0.778 vs. 0.716 vs. 0.721; validation: 0.730 vs. 0.654 vs. 0.648). The DL-score proved to be an independent predictor of LRFS in training sets (adjusted HR = 5.950, 95% CI: 2.800-12.644; p < 0.001) and effectively categorized patients into high- and low-risk categories (p < 0.0001, p = 0.012, respectively). The combined DLCM further improved the performance, attaining C-index of 0.848 (95% CI: 0.790-0.915) and 0.749 (95% CI: 0.601-0.878) in the training and validation sets, respectively. The DLCM exhibited strong calibration and clinical utility and was an effective prognostic tool for risk classification in both cohorts. Conclusions: The CT-based DL model effectively predicts LRFS preoperatively in RPS, aiding risk stratification and guiding individualized therapeutic strategies.