AI and Cancer Research ── Lung ── 2026-10-18
Lung cancer: treatment, trials and AI
- Abstract
Chemoimmunotherapy (CIT) is the standard first-line regimen for advanced lung squamous cell carcinoma (LUSC), yet clinical benefit varies substantially across patients, and available biomarkers remain inadequate for treatment selection. We aimed to establish and externally validate a whole-slide image (WSI)-based graph convolutional network (GCN) pathomics model integrated with clinical variables for objective response prediction and clinically relevant stratification of CIT benefit.
Journal IF-equivalent: 1.0 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Wang D, Mu S, Zhang M, Tao G, Wang S, Shen Y, et al. P3.062: GCN Pathomics-Clinical Model Predicts Response to First-Line Chemoimmunotherapy in Advanced LUSC: A Multicenter Study. Journal of Thoracic Oncology. 2026 Oct;21(10):105231. doi:10.1016/j.jtho.2026.105231.Checked: Abstract only - Abstract
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, and prognostic embeddings extracted from pathology reports using a large language model. A central transformer architecture fuses these features to predict disease-free survival. Model performance was evaluated on independent external datasets and compared with guideline-based risk stratification. Associations between model-derived risk scores and tumor microenvironment features were analyzed to assess biologic interpretability.
Results: A total of 1073 patients (mean age ± SD, 60.8 years ± 9.8; 550 male) were enrolled from five centers. On external datasets, DLRTransformer demonstrated robust prognostic performance (concordance index, 0.77-0.78), outperforming the guideline-based risk stratification (concordance index, 0.60-0.65). Risk scores were associated with tumor microenvironment phenotypes, including increased neoplastic cellularity and reduced tertiary lymphoid structures. A treatment-by-risk interaction was observed (interaction hazard ratio [HR], 0.14; 95% CI: 0.06, 0.32; P P = .02) but not in low-risk patients (HR, 1.70; 95% CI: 0.97, 2.99; P = .06).
Conclusion: The DLRTransformer improved postoperative risk stratification compared with guideline-based risk stratification and identified patients with stage I NSCLC who benefited from adjuvant therapy. Keywords: Deep Learning, Large Language Models LLMs, CT, Prognosis & Prediction, Non-Small Cell Lung Cancer (NSCLC), Multimodal Deep Learning, Risk Stratification, Adjuvant Therapy, Tumor Microenvironment (TME) Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.
Journal IF-equivalent: 2.2 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)Reference: Wang L, Zong Y, Li X, Tang H, Yao J, Cao Y, et al. Multimodal Transformer Integrating CT and Pathology for Postoperative Risk Stratification in Stage I Non–Small Cell Lung Cancer. Radiology: Imaging Cancer. 2026 Nov 1;8(6):e250617. doi:10.1148/rycan.250617.Checked: Abstract only