Genes associated with genetic and rare lung diseases and the risk of lung cancer.

Background: We investigated whether markers, genes or terms of the associated with genetic or rare diseases (GARDs) that affect airway or lung function are associated with lung cancer.

Methods: Genes of interest were extracted from , , and Monarch Initiative. Individual SNP, gene level and gene-set analyses were performed for 52,207 SNPs, 1,677 genes or for 620 terms of the .

Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers-gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression-along with the clinical variables, that learns a unified representation robust to missing modalities.

Genome-wide association study of early-stage non-small cell lung cancer prognosis: a pooled analysis in the International Lung Cancer Consortium.

Lung cancer is the leading cause of cancer mortality. To investigate genetic determinants for prognosis among patients diagnosed with early-stage non-small cell lung cancer (NSCLC), we conducted the first large-scale genome-wide association prognostic study using data from the International Lung Cancer Consortium (ILCCO) through a two-phase analysis. Phase 1 includes the discovery of genome-wide association studies analysis using a multivariable Cox PH model on 3428 NSCLC patients of European ancestry from 10 ILCCO participating studies to identify genetic variants associated with overall survival and validation analysis for genome-wide significant variants (P-value ≤5 × 10-8) using the Cancer Genome Atlas (TCGA).

Sexual orientation and gender identity data reveals real-world cancer disparities among sexual and gender minorities at an NCI-Designated Comprehensive Cancer Center.

Background: Lack of sexual orientation and gender identity (SOGI) collection hinders the ability to identify cancer disparities, create opportunities for improvement, and reveal the burden of cancer among sexual and gender minority (SGM) populations. Our institution is one of the first NCI-Designated Comprehensive Cancer Centers to collect SOGI as standard-of-care demographics.

Methods: This analysis includes 118 320 patients who came to the H.

The mutagenic forces shaping the genomes of lung cancer in never smokers.

Lung cancer in never smokers (LCINS) accounts for around 25% of all lung cancers and has been associated with exposure to second-hand tobacco smoke and air pollution in observational studies. Here we use data from the Sherlock-Lung study to evaluate mutagenic exposures in LCINS by examining the cancer genomes of 871 treatment-naive individuals with lung cancer who had never smoked, from 28 geographical locations. KRAS mutations were 3.

The Association Between Alcohol Consumption and Anal Human Papillomavirus in the HPV Infection in Men Study.

Background: Human papillomavirus (HPV) can lead to anal cancer in men and women. The aim of this analysis was to assess the association between alcohol consumption and the prevalence and incidence of anal HPV among 1,919 men.

Methods: The HPV infection in Men Study recruited men without HIV.

Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence.

Rationale And Objectives: To predict recurrence risk in patients with surgically resected non-small cell lung cancer (NSCLC) using radiomic analysis and clinicopathological factors.

Materials And Methods: 293 patients with surgically resected stage IA-IIIA NSCLC were analyzed. Patients were randomly stratified into development and test cohorts.

Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.

Artificial intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patient data remain a major barrier to the widespread adoption of AI in clinical practice, because large and diverse training datasets are essential for developing accurate, robust, and generalizable AI models. Federated learning offers a privacy-preserving solution by enabling collaborative model training across institutions without sharing sensitive data.

Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology Reports.

Surgical pathology reports contain essential diagnostic information, in free-text form, required for cancer staging, treatment planning, and cancer registry documentation. However, their unstructured nature and variability across tumor types and institutions pose challenges for automated data extraction. We present a consensus-driven, reasoning-based framework that uses multiple locally deployed large language models (LLMs) to extract six key diagnostic variables: site, laterality, histology, stage, grade, and behavior.

Genome-wide association study for lung cancer in 6531 African Americans reveals new susceptibility loci.

Despite lung cancer affecting all races and ethnicities, disparities are observed in incidence and mortality rates among different ethnic groups in the United States. Non-Hispanic African Americans had a high incidence rate of lung cancer at 55.8 per 100 000 people, as well as the highest death rate at 37.

Reliable Radiologic Skeletal Muscle Area Assessment - A Biomarker for Cancer Cachexia Diagnosis.

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (SMA) longitudinally through computed tomography (CT) scans, an imaging modality routinely acquired in cancer care, is an effective way to identify and track this condition. However, existing tools often lack full automation and exhibit inconsistent accuracy, limiting their potential for integration into clinical workflows.

REASONING BEYOND ACCURACY: EXPERT EVALUATION OF LARGE LANGUAGE MODELS IN DIAGNOSTIC PATHOLOGY.

Background: Diagnostic pathology depends on complex, structured reasoning to interpret clinical, histologic, and molecular data. Replicating this cognitive process algorithmically remains a significant challenge. As large language models (LLMs) gain traction in medicine, it is critical to determine whether they have clinical utility by providing reasoning in highly specialized domains such as pathology.

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition.

Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features.

Correction: Park et al. Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration. 2024, , 2364.

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Real-world comparative outcomes of EGFR-TKIs for first-line treatment of EGFR+ metastatic non-small-cell lung cancer.

Purpose: Osimertinib is a third-generation EGFR-TKI and preferred first-line (1L) treatment for EGFR positive (EGFR+) metastatic non-small cell lung cancer (mNSCLC). This study compared real-world clinical outcomes of 1L osimertinib versus 1st or 2nd generation EGFR-TKIs (1/2G-TKIs) in patients with EGFR+ mNSCLC.

Methods: Nine academic cancer centers in the US participated in the retrospective cohort study.

Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.

Background: We use real-world data to develop a lung cancer screening (LCS) eligibility mechanism that is both accurate and free from racial bias.

Methods: Our data came from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial. We built a systematic fairness-aware machine learning framework by integrating a Group and Intersectional Fairness and Threshold (GIFT) strategy with an easy ensemble classifier-(EEC-) or logistic regression-(LR-) based model.

Difference-Makers for Collecting Sexual Orientation and Gender Identity Data in Oncology Settings.

Purpose: The purpose of this analysis was to identify key difference-making conditions that distinguish oncology institutions that collect sexual orientation and gender identity (SOGI) data across a sample of American Society of Clinical Oncology (ASCO) members.

Methods: From October to November 2020, an anonymous 54-item web-based survey was distributed to ASCO members. Coincidence analysis was used to identify difference-making conditions for the collection of SOGI data.

NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2025.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Lung Cancer Screening provide criteria for selecting individuals for screening and offer recommendations for evaluating and managing lung nodules detected during initial and subsequent annual screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.

Fourier analysis of signal dependent noise images.

An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD).

Rethinking how mobile units can catalyze progress on lung cancer screening: a scoping review of what we have learned.

Background: Despite United States Preventive Services Task Force (USPSTF) recommendations, low uptake of lung cancer screening (LCS) highlights the need for measures to promote adoption. This scoping review aims to outline the global landscape of mobile low-dose computed tomography (LDCT) platforms, summarizing research and evaluating efficacy in screening at-risk populations.

Methods: We comprehensively searched Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, Embase, Scopus, and Web of Science for articles published between 2017 and 2023.

Medical student clinical cultural awareness in cancer care of sexual gender minority patients.

Objective: Health disparities in lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+), or sexual and gender minority (SGM) people are known. SGM people have higher cancer risk, but lower rates of screenings, resulting in a higher likelihood of late-stage disease. This study evaluates medical students' clinical cultural awareness in cancer care of SGM patients to identify gaps in education.

Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration.

For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects.

Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection.

Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high-risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence.