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TRUVACE RECORD VERSION
record: TRV-2026-1589
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T18:12:33.694378Z
status: published
lens: g_space
sector: health
headline: Multimodal Transformer Integrating CT and Pathology for Postoperative Risk Stratification in Stage I Non-Small Cell Lung Cancer
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: outperforming the guideline-based risk stratification (concordance index, 0.60-0.65) | adjuvant therapy improved disease-free survival in high-risk patients (HR, 0.30; 95% CI: 0.11, 0.84; P = .02)
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built the Deep Learning-Radiomic-Text Transformer, which combines CT-derived deep learning features, radiomics, clinical variables, and pathology report embeddings from a large language model to predict disease-free survival after surgery for stage I non-small cell lung cancer. It was developed and tested in a retrospective study of 1073 patients from five centers.

On independent external datasets the model scored a concordance index of 0.77-0.78 versus 0.60-0.65 for guideline-based stratification, and it separated patients who gained from adjuvant therapy from those who did not. This could help target treatment in early-stage disease, but the evidence is retrospective, so prospective confirmation is still needed.
limitation: The study is retrospective, and the treatment-benefit findings come from propensity score-matched subcohorts rather than a randomized trial.
tag: Evidence-backed gain
key_points: The model fuses multiview 2.5-dimensional CT deep learning features, radiomics, clinical variables, and LLM-extracted pathology report embeddings using a central transformer. | External concordance index was 0.77-0.78, compared with 0.60-0.65 for guideline-based risk stratification. | Risk scores were linked to tumor microenvironment features, including increased neoplastic cellularity and reduced tertiary lymphoid structures. | A treatment-by-risk interaction was observed (interaction HR 0.14; 95% CI 0.06, 0.32; P < .01).
rundown: The cohort comprised 1073 patients enrolled from five centers, with a mean age of 60.8 years and 550 men. The study was multicenter and retrospective, and performance was assessed on independent external datasets.

In propensity score-matched subcohorts, adjuvant therapy improved disease-free survival in high-risk patients (HR 0.30; 95% CI 0.11, 0.84; P = .02). In low-risk patients no clear benefit was seen (HR 1.70; 95% CI 0.97, 2.99; P = .06).

The authors analyzed associations between model-derived risk scores and tumor microenvironment phenotypes to assess biologic interpretability, finding links to higher neoplastic cellularity and fewer tertiary lymphoid structures.
sources:
- peer_reviewed | Radiology: Imaging Cancer | https://doi.org/10.1148/rycan.250617 | 2026-11-01
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