aceMultimodal Transformer Integrating CT and Pathology for Postoperative Risk Stratification in Stage I Non-Small Cell Lung Cancer
Purpose To develop and evaluate an interpretable, multimodal deep learning model to improve postoperative risk stratification and predict adjuvant therapy benefit in stage I non-small cell lung cancer (NSCLC). Materials and Methods In this multicenter retrospective study of patients with stage I NSCLC, a multimodal model called the Deep Learning-Radiomic-Text Transformer (DLRTransformer) was developed by integrating multiview 2.5-dimensional deep learning features from CT scans, radiomics, clinical variables, an…
On external datasets, the DLRTransformer predicted disease-free survival better than guideline-based risk stratification and flagged high-risk stage I NSCLC patients who benefited from adjuvant therapy.
The study is retrospective, and the treatment-benefit findings come from propensity score-matched subcohorts rather than a randomized trial.
Evidence
- Peer-reviewedRadiology: Imaging Cancer2026-11-01
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Truvace Impact Record TRV-2026-1589, v1: “Multimodal Transformer Integrating CT and Pathology for Postoperative Risk Stratification in Stage I Non-Small Cell Lung Cancer.” Truvace, 2026-10-16. /record/TRV-2026-1589 (accessed at citation time). sha256 46f312f2bc56bf5a…
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