AI and Cancer Research ── Lung ── 2026-10-31
Lung cancer: treatment, trials and AI
- Abstract
PURPOSE: To develop and externally test a multiparametric MRI habitat-based framework integrating intratumoral heterogeneity (ITH), peritumoral heterogeneity (PTH), and clinical characteristics for predicting intracranial progression in patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases receiving first-line third-generation (3rd-gen) EGFR-TKI monotherapy. MATERIALS AND METHODS: This retrospective multicenter study included patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases who received first-line 3rd-gen EGFR-TKI monotherapy. Patients were assigned to an internal training cohort (n = 90) and an external testing cohort (n = 51). The binary prediction outcome was intracranial progression within 20 months after treatment initiation. Pretreatment contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging were analyzed. The index brain metastasis and a 6-mm peritumoral region were segmented, followed by voxel-wise habitat clustering. Habitat-specific radiomic features were extracted and selected to construct ITH, PTH, and combined intratumoral-peritumoral habitat (PITH) signatures. Clinical predictors were evaluated using univariable and multivariable logistic regression. Support vector machine models were evaluated using receiver operating characteristic, decision curve, calibration, and Kaplan-Meier analyses. RESULTS: A total of 141 patients were analyzed, including 75 with progressive disease (PD) and 66 without PD (non-PD). Multivariable logistic regression identified EGFR mutation subtype (odds ratio [OR] = 2.390, 95% confidence interval [CI]: 1.187-4.811, P = 0.015) and the maximum diameter of the index brain metastasis as independent predictors (OR = 2.048, 95% CI: 1.204-3.483, P = 0.008). Models based on imaging heterogeneity features outperformed the clinical model overall. The clinical model achieved an AUC of 0.662, while the ITH, PTH, and PITH models achieved AUCs of 0.715, 0.724, and 0.758, respectively. The combined model showed favorable performance (AUC 0.795, accuracy 76.5%, sensitivity 70.4%, and specificity 83.3%). Patients classified by the combined model as high risk had significantly shorter intracranial progression-free survival (iPFS), than those classified as low risk (log-rank P = 0.019). CONCLUSION: The proposed framework integrating ITH, PTH, and clinical features may provide a noninvasive approach for predicting intracranial progression risk and stratifying prognosis in patients with EGFR-mutant lung adenocarcinoma with newly diagnosed BMs receiving first-line 3rd-gen EGFR-TKI monotherapy.
Journal IF-equivalent: 8.3 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Deng G, Li H, Dai Z, Fan J, Song X, Pang G, et al. MRI-based intratumoral and peritumoral heterogeneity for predicting intracranial progression in EGFR-mutant lung adenocarcinoma patients with treatment-naive brain metastases receiving first-line third-generation EGFR-TKIs. J Transl Med. 2026 Oct 17;24(1):1361. doi:10.1186/s12967-026-09109-7. PMID: 42958288.Checked: Abstract only - Abstract
Lung adenocarcinoma (LUAD) is a highly invasive malignancy associated with poor prognosis. However, reliable molecular subtypes and prognostic signatures for LUAD, particularly their associations with tumor immunity and treatment response, remain poorly characterized. This study aimed to identify distinct molecular subtypes and establish a robust prognostic signature for LUAD, while exploring potential therapeutic targets associated with cisplatin resistance. In this study, we integrated ten clustering algorithms with 101 machine learning methods to identify two distinct molecular subtypes of LUAD and established a robust consensus machine learning-derived signature (CMLS). Patients belonging to the Cluster A subtype exhibited a more favorable prognosis and an enhanced immune response. Furthermore, a CMLS model constructed from 19 genes exhibited strong predictive power. Individuals with high CMLS scores displayed reduced CD4 + T-cell infiltration, poorer survival outcomes, and diminished responses to immunotherapy, whereas those with the low CMLS scores showed improved clinical outcomes, better immunotherapy responsiveness, and features of an immunologically "hot" tumor phenotype. Among the identified genes, ribonucleotide reductase subunit M2 (RRM2) emerged as a core regulator associated with LUAD prognosis. RRM2 was markedly upregulated in lung cancer tissues and the LUAD cell lines and was associated with the cisplatin (DDP) resistance. Suppression of RRM2 expression significantly enhanced the cisplatin sensitivity of A549/DDP cells. In summary, our findings establish a molecular classifier that stratifies LUAD patients into distinct risk categories and highlight RRM2 as a promising biomarker and therapeutic target associated with cisplatin resistance.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Huang H, Tao T, Li Z, Zhai K, Cui B, Feng H, et al. Integrating Machine Learning to Identify Prognostic Subtypes and the Cisplatin Resistance Gene RRM2 in Lung Adenocarcinoma. Thorac Cancer. 2026 Oct 30;17(21):e70407. doi:10.1111/1759-7714.70407. PMID: 42958928.Checked: Abstract only