AI and Cancer Research ── Lung ── 2026-11-07
Lung cancer: treatment, trials and AI
- Abstract
Introduction: The 2020 International Association for the Study of Lung Cancer (IASLC) grading system integrates the predominant histologic pattern and the proportion of high-grade patterns, but preoperative CT assessment is limited by spatial averaging of heterogeneous tumor components. We aimed to develop a CT-derived nested habitats score (NHS) that recursively identifies grade-associated intratumoral CT subregions, evaluate its cross-center performance for preoperative IASLC grading, and explore its transcriptomic correlates.
Methods: This retrospective two-center study included 650 patients with surgically resected lung adenocarcinoma. Center 1 formed the development cohort (n = 390); Center 2 was stratified 1:1 into validation (n = 130) and locked test (n = 130) cohorts, both from the same external institution. The prespecified primary task was pathologic IASLC Grade 1 versus Grade 2/3. NHS was generated using five-fold out-of-fold estimation in development data. Clinical-radiological, whole-tumor radiomics, NHS, and integrated models were compared, with algorithm selection confined to validation before one-time test evaluation. An independent TCGA-LUAD/TCIA CT-RNA cohort (n = 88) underwent exploratory WGCNA and Hallmark gene-set enrichment analysis.
Results: The development/validation/test cohorts contained 41/14/14 Grade 1, 293/101/96 Grade 2, and 56/15/20 Grade 3 tumors. The selected random-forest integrated model yielded AUCs of 0.871 (95% CI, 0.784–0.936) in validation and 0.866 (95% CI, 0.784–0.935) in testing. NHS alone yielded AUCs of 0.828 and 0.854, compared with 0.620 and 0.497 for the clinical-radiological model and 0.703 and 0.775 for whole-tumor radiomics. NHS accounted for 76.3% of mean absolute SHAP contribution; removing NHS reduced AUC by 0.251 (95% CI, 0.079–0.439) in validation and 0.369 (95% CI, 0.200–0.553) in testing. Brier scores for NHS and the integrated model (0.074–0.077) were below the non-informative reference (0.096), but calibration slopes exceeded 1, and decision curves showed no net benefit over treating all patients as Grade 2/3 at low-to-intermediate thresholds. Exploratory radiogenomics showed nominal NHS-associated coexpression modules and enrichment of WNT/beta-catenin, E2F, G2M-checkpoint, and immune-related programs. Discussion NHS discriminated IASLC Grade 1 from Grade 2/3 tumors, retained stable performance in two held-out cohorts from a second institution, and contributed most of the integrated model’s discrimination. The transcriptomic associations are hypothesis-generating, and recalibration and prospective multicenter evaluation are required before clinical application.
Journal IF-equivalent: 4.8 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Peng W, Liu J, Li L, Qi W, Zhao J, Li D. CT-derived nested habitats score for preoperative IASLC grading in lung adenocarcinoma: cross-center validation and radiogenomic analysis. Front Mol Biosci. 2026 Nov 4;13:2006861. doi:10.3389/fmolb.2026.2006861.Checked: Abstract only - Abstract
Phage display can identify ligands that target distinctive cell surface phenotypes, but selection outputs are often complex and difficult to interpret. Here, we developed an integrated workflow combining phage display biopanning, next-generation sequencing (NGS), machine learning (ML), and flow cytometry to discover short peptide binders associated with genotype-defined cancer contexts. Phage display was performed on lung cancer cell lines representing KRAS wild-type (WT), G12C, G12D, and G12V backgrounds, and experimental outputs were used to train a multimodal probabilistic model. From the resulting model-generated candidates, 98 nonredundant heptapeptides were selected for synthesis, and 88 were successfully solubilized and tested experimentally. Flow cytometry validation revealed heterogeneous binding behaviors, including broad pan-cellular binders and candidates with mutant-vs-WT binding preference. Extended dose–response profiling and normal-cell counter-screening enabled prioritization based on binding magnitude, curve behavior, normal-cell reactivity, and sequence developability. Overall, this study shows how whole-cell selection data sets can be analyzed computationally and experimentally to identify and prioritize peptide candidates for targeting strategies.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Uguzzoni G, Belisario DC, Pagnani A, Marchiò S. Integrated Whole-Cell Phage Display and Machine Learning Identify Candidate Targeting Ligands in KRAS-Defined Lung Cancer Contexts. ACS Omega. 2026 Nov 4 [Epub ahead of print]. doi:10.1021/acsomega.6c10403.Checked: Abstract only