AI and Cancer Research (Top Journals) ── Lung ── 2026-09-30

3 Lung papers. Lead: Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC.

New papers on cancer and AI from 26 medical, oncology and general journals with an impact factor of 10 or more, collected from the journals' own websites and selected by Jev (an AI that makes language judgements): is it original research, is AI central, which of six themes, and does it concern treatment or trials. Summaries are written from each abstract only and make no claim about efficacy or safety.

Lung cancer: treatment, trials and AI

Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC.
Nature medicine · 2026-09-13 · Evidence: international real-world observational study with a clinical usability evaluation; prospective validation ongoing
I3LUNG (NCT05537922) enrolled 2,396 patients and combined real-world clinical and blood data, CT, digital pathology and genomics in early- and intermediate-fusion models. Clinical-blood models reached AUC up to 0.77 in the test set and 0.55-0.72 in external validation, exceeding PD-L1, ECOG PS, NLR, LDH and LIPI. Physicians' predictions improved with the explainable tool; prospective validation is ongoing.
Multi-task deep learning model for predicting EGFR mutation status in NSCLC.
NPJ digital medicine · 2026-07-20 · Evidence: observational development and validation study of an imaging-based prediction model
A multi-task deep learning model using CT images was developed to predict EGFR mutation status in NSCLC (ChiCTR2400083082). The abstract reports accurate prediction and an association of the MTDL score with survival under EGFR-targeted treatment, gene expression patterns and the tumor microenvironment, without reporting performance values.
Radiogenomic modeling of EGFR mutation status in brain metastases from lung adenocarcinoma: a multicenter study with biological interpretability.
NPJ digital medicine · 2026-07-06 · Evidence: multicenter observational model development and validation study
Across three institutions, 1303 brain metastases from 421 lung adenocarcinoma patients were modeled using 3435 radiomic features from T1, T2 and contrast-enhanced T1 to classify EGFR mutation status. Internal AUCs reached 0.95, and 94 pathologically confirmed lesions gave 83.0% accuracy. Sphericity was the leading feature and correlated negatively with RNF125 and SLC37A2.

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How papers are chosen: (1) collected from each journal's RSS; (2) editorials, corrections and items without cancer/AI terms are removed; (3) Jev judges four points (original research ≥ 0.6, AI ≥ 0.6, one of six themes, treatment/trials ≥ 0.5 for the disease themes). Adenoid cystic carcinoma is a rare cancer with almost no AI papers, so AI is not required for that theme. Papers Jev cannot judge are not listed.