ArticleFrontiers in oncology2026
MRI-based morphological and spatial characteristics of leptomeningeal metastasis: prognostic value in non-small cell lung cancer.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Leptomeningeal metastasis (LM) represents a devastating complication of non-small cell lung cancer (NSCLC), with limited survival and poorly defined imaging-based prognostic markers. Purpose: This study evaluated the combined prognostic value of MRI-based morphological and spatial patterns in NSCLC patients with LM, whose prognostic relevance remains poorly defined. Methods: We retrospectively reviewed 71 NSCLC patients with LM confirmed by 3.0T black-blood MRI, selected from 109 initially screened after applying exclusion criteria. Patients were classified into linear (n=41) and mixed (n=30) morphological subtypes based on MRI, and stratified by the number of involved brain regions (>3 vs. ≤3). Clinical, imaging, and survival data were analyzed using Kaplan-Meier estimates and multivariate Cox regression to identify independent prognostic factors. Results: The mixed subtype exhibited a significantly higher lesion burden than the linear subtype (26.86 ± 26.72 vs. 7.25 ± 10.95, p<0.001). Involvement of more than three brain regions was associated with significantly shorter median overall survival (14 vs. 24 months, p=0.016). Multivariate analysis identified several independent adverse prognostic factors: increased lesion number (HR = 1.02, 95% CI: 1.01-1.03, p<0.01), temporal lobe invasion (HR = 1.96, 95% CI: 1.07-3.58, p=0.029), Regionsinvolved (HR = 0.52, p=0.020). Conclusion: MRI-based morphological subtyping and spatial distribution provide significant prognostic value in NSCLC-LM. The mixed morphology, extensive brain involvement (>3 regions), and invasions of specific location such as the tempoarl lobe is associated with poorer survival outcomes. These findings support the use of MRI phenotyping for risk-adapted clinical management in NSCLC-LM.
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