Lung cancer screening via annual low-dose computed tomography has poor adoption. We conducted a prospective case–control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by a low-dose computed tomography. Changes in genome-wide cell-free DNA fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples and validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a 5-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths. Significance: Lung cancer screening has poor adoption. Our study describes the development and validation of a novel blood-based lung cancer screening test utilizing a highly affordable, low-coverage genome-wide sequencing platform to analyze cell-free DNA fragmentation patterns. The test could improve lung cancer screening rates leading to substantial public health benefits. See related commentary by Haber and Skates, p. 2025

Up to 50% of patients with non-small cell lung cancer (NSCLC) develop brain metastasis (BM), yet the study of BM genomics has been limited by tissue access, incomplete clinical data, and a lack of comparison with paired extracranial specimens. Here we report a cohort of 233 patients with resected and sequenced (MSK-IMPACT) NSCLC BM and comprehensive clinical data. With matched samples (47 primary tumor, 42 extracranial metastatic), we show CDKN2A/B deletions and cell cycle pathway alterations to be enriched in the BM samples. Meaningful clinico-genomic correlations are noted, namely EGFR alterations in leptomeningeal disease (LMD) and MYC amplifications in multifocal regional brain progression. Patients who developed early LMD frequently have had uncommon, multiple, and persistently detectable EGFR driver mutations. The distinct mutational patterns identified in BM specimens compared to other tissue sites suggest specific biologic underpinnings of intracranial progression.

PURPOSE: Newer-generation tyrosine kinase inhibitors (TKIs) for non–small cell lung cancer (NSCLC) with epidermal growth factor receptor ( EGFR ) mutations and anaplastic lymphoma kinase ( ALK ) rearrangements have demonstrated high CNS activity. The optimal use of up-front stereotactic radiosurgery (SRS) for brain metastases (BM) in patients eligible for CNS-penetrant TKIs is controversial, and data to guide patient management are limited. MATERIALS AND METHODS: Data on TKI-naïve patients with EGFR- and ALK-driven NSCLC with BM treated with CNS-penetrant TKIs with and without up-front SRS were retrospectively collected from seven academic centers in the United States. Time-to-CNS progression and overall survival (OS) were analyzed, with multivariable adjustment in Fine & Gray and Cox proportional hazards models for clinically relevant factors. RESULTS: From 2013 to 2022, 317 patients were identified (200 TKI-only and 117 TKI + SRS). Two hundred fifty (79%) and 61 (19%) patients received osimertinib and alectinib, respectively. Patients receiving TKI + SRS were more likely to have BM ≥1 cm ( P < .001) and neurologic symptoms ( P < .001) at presentation. Median OS was similar between the TKI and TKI + SRS groups (median 41 v 40 months, respectively; P = .5). On multivariable analysis, TKI + SRS was associated with a significant improvement in time-to-CNS progression (hazard ratio [HR], 0.63 [95% CI, 0.42 to 0.96]; P = .033). Local CNS control was significantly improved with TKI + SRS (HR, 0.30 [95% CI, 0.16 to 0.55]; P < .001), whereas no significant differences were observed in distant CNS control. Subgroup analyses demonstrated a greater benefit from TKI + SRS in patients with BM ≥1 cm in diameter for time-to-CNS progression and CNS progression-free survival. CONCLUSION: The addition of up-front SRS to CNS-penetrant TKI improved time-to-CNS progression and local CNS control, but not OS, in patients with BM from EGFR - and ALK-driven NSCLC. Patients with larger BM (≥1 cm) may benefit the most from up-front SRS.

Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)—a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation.

Brain metastases are a frequent and debilitating manifestation of advanced cancer. Here, we collect and analyze neuroimaging of 3,065 cancer patients with 13,067 brain metastases, representing an extensive collection for research. We find that metastases predominantly localize to high perfusion areas near the grey-white matter junction, but also identify notable differences depending on the primary cancer histology as well as brain regions which do not conform to this relationship. Lung and breast cancers, in contrast to melanoma, frequently metastasize to the cerebellum, hinting at biological pathways of spread. Additionally, the deep brain structures are relatively spared from metastasis, regardless of primary cancer type. Leveraging this data, we propose a probabilistic brain metastasis risk model to enhance the therapeutic ratio of whole-brain radiotherapy by targeting high risk areas while preserving cortical and subcortical brain regions of functional significance and low metastasis risk, potentially reducing the cognitive side effects of therapy.