Non-small cell lung cancer (NSCLC) recurrence after microwave ablation (MWA) remains a critical clinical challenge, with existing risk stratification tools limited by static feature analysis and poor generalizability. This study aimed to develop a deep learning-radiomics (DLR) fusion model incorporating dynamic spatiotemporal patterns from longitudinal imaging to enable robust recurrence prediction. A single-center cohort comprising 184 patients with pre- and post-MWA CT sequences (baseline to 12 months) was analyzed. We implemented a spatiotemporal modeling framework where 3D-ResNet convolutional networks and a PyRadiomics-based pipeline were applied in parallel to the same longitudinal CT series to simultaneously extract deep learning embeddings and radiomic signatures from the ablation zones. These spatiotemporal features were subsequently processed through a Transformer architecture with temporal self-attention mechanisms to capture dynamic lesion evolution patterns. The model integrated imaging-derived characteristics with radiomics features and deep learning features through adaptive multimodal fusion modules, utilizing gated cross-attention mechanisms to establish feature inter-dependencies. The DLR model achieved superior performance (AUC = 0.92, 95% CI: 0.89–0.95) compared to standalone deep learning (AUC = 0.85) or radiomics models (AUC = 0.78), outperforming radiologists’ visual assessments (AUC = 0.76, Kappa = 0.68). This study establishes the DLR framework for dynamic NSCLC recurrence risk profiling, demonstrating that spatiotemporal feature fusion significantly enhances predictive accuracy.
Li et al. (Thu,) studied this question.
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