Attention-guided framework for integrative omics and temporal dynamics in predicting major pathological response in neoadjuvant immunochemotherapy for NSCLC.
This study developed a multiomics model combining radiomics, pathomics, and temporal imaging to predict major pathological response in patients with locally advanced non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy.
A retrospective, multicenter study was conducted, enrolling 271 patients with stage IB-III NSCLC who received neoadjuvant immunochemotherapy. High-resolution CT images were enhanced using a generative adversarial network-based super-resolution technique. Radiomics features were extracted from multi-sequence CT scans at multiple time points, while pathomics features were derived from whole-slide imaging of surgical specimens. A transformer-based attention mechanism was used to integrate radiomics, pathomics, and temporal imaging data. The model was trained and validated on data from one center and tested on external cohorts. Performance was evaluated using area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, and decision curve analysis.
The Trans-Model demonstrated superior predictive performance, achieving an AUC of 0.858 (95% CI 0.783 to 0.933) in the external test cohort. It outperformed Rad-Model (AUC: 0.839) and Patho-Model (AUC: 0.753). The Trans-Model effectively stratified patients by survival outcomes, with major pathological response (MPR)-positive patients exhibiting significantly improved 3-year overall survival (87.3% vs 76.1%, p=0.034) and 5-year progression-free survival (45.8% vs 34.7%, p=0.033) compared with MPR-negative patients. Decision curve analysis confirmed the model's clinical utility across a wide range of threshold probabilities.
The multiomics model, integrating multi-temporal, multi-sequence data with attention-based feature fusion, improves MPR prediction in patients with NSCLC receiving neoadjuvant immunochemotherapy, enabling personalized treatment by identifying responders and optimizing outcomes.
A retrospective, multicenter study was conducted, enrolling 271 patients with stage IB-III NSCLC who received neoadjuvant immunochemotherapy. High-resolution CT images were enhanced using a generative adversarial network-based super-resolution technique. Radiomics features were extracted from multi-sequence CT scans at multiple time points, while pathomics features were derived from whole-slide imaging of surgical specimens. A transformer-based attention mechanism was used to integrate radiomics, pathomics, and temporal imaging data. The model was trained and validated on data from one center and tested on external cohorts. Performance was evaluated using area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, and decision curve analysis.
The Trans-Model demonstrated superior predictive performance, achieving an AUC of 0.858 (95% CI 0.783 to 0.933) in the external test cohort. It outperformed Rad-Model (AUC: 0.839) and Patho-Model (AUC: 0.753). The Trans-Model effectively stratified patients by survival outcomes, with major pathological response (MPR)-positive patients exhibiting significantly improved 3-year overall survival (87.3% vs 76.1%, p=0.034) and 5-year progression-free survival (45.8% vs 34.7%, p=0.033) compared with MPR-negative patients. Decision curve analysis confirmed the model's clinical utility across a wide range of threshold probabilities.
The multiomics model, integrating multi-temporal, multi-sequence data with attention-based feature fusion, improves MPR prediction in patients with NSCLC receiving neoadjuvant immunochemotherapy, enabling personalized treatment by identifying responders and optimizing outcomes.
Authors
Gan Gan, He He, Zhang Zhang, Chen Chen, Liu Liu, Li Li, Duan Duan, Lv Lv, Liang Liang, Cao Cao, Chen Chen
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