Matthew B. Schabath

The paper identifies lung cancer susceptibility copy number variants (CNVs) and shows their potential role in genomic instability through association analyses and functional validation.

This study identifies three distinct evolutionary trajectories in lung adenocarcinomas using whole-genome sequencing, revealing that some never-smoking tumors follow smoking-like paths driven by KRAS mutations.

This paper develops an automated breast density detection technique that improves risk prediction by synthesizing a signal-dependent noise stochastic process across different mammographic technologies.

This study uses a 3D computational model to show that tumor growth rates depend on tissue density and metabolic strategies like glycolysis or angiogenesis in lung cancer.

The paper identifies three subtypes of NS-LUAD using RNA-seq data and develops a 60-gene signature that predicts survival across subgroups.

This study analyzes 1024 lung adenocarcinomas to identify evolutionary patterns and mutational mechanisms, revealing distinct trajectories linked to smoking, ancestry, and retrotransposition.

This study develops a clinical-radiomics model to predict immunotherapy response in NSCLC patients, identifying a radiomic feature linked to hypoxia and poor outcomes.

The paper identifies genetic links between impaired pulmonary function and increased lung cancer risk, showing causal relationships through Mendelian randomization and highlighting immune-related pathways.