Lung cancer is the leading cause of cancer death in the United States. More than 80% of these deaths are attributed to tobacco use, and primary prevention can effectively reduce the cancer burden. The National Lung Screening Trial showed that low-dose computed tomography (LDCT) screening could reduce lung cancer mortality in high-risk patients by 20% compared with chest radiography. The US Preventive Services Task Force recommends annual LDCT screening for persons aged 55 to 80 years with a 30-pack-year smoking history, either currently smoking or having quit within 15 years.

Background: Histoplasmosis pulmonary nodules often present in computed tomography (CT) imaging with characteristics suspicious for lung cancer. This presents a work-up decision issue for clinicians in regions where histoplasmosis is an endemic fungal infection, when a nodule suspicious for lung cancer is detected.We hypothesize the application of radiomic features extracted from pulmonary nodules and perinodular parenchyma could accurately distinguish between suspicious histoplasmosis lung nodules and non-small cell lung cancer (NSCLC).Methods: A retrospective clinical cohort of pulmonary nodules with a confirmed diagnosis of histoplasmosis or NSCLC was collected from the University of Iowa Hospitals and Clincs. Radiomic features were extracted describing characteristics of the nodule and perinodular parenchyma regions and used to build a machine learning tool. These cases were assessed by four expert clinicians who gave a blinded risk prediction for NSCLC. Tool and observer performance were assessed by calculating the area under the curve for the receiver operating characteristic (AUC-ROC) and interclass correlation coefficient (ICC).Results: A cohort of 71 subjects with confirmed histopathology (40 NSCLC, 31 histoplasmosis) were case-matched based on age, sex, and smoking history. Superior performance (AUC-ROC =0.89) was demonstrated using leave-one-subject out validation in the tool that incorporated radiomics from the nodule and perinodular parenchyma region extended to 100% nodule diameter. Observers had perfect intrarepeatability (ICC =1.0) and demonstrated fair inter-reader variability (ICC =0.52). Conclusions:Radiomics have potential utility in the challenging task of differentiation between lung cancer and histoplasmosis. Expert clinician readers have high intra-repeatability but demonstrated interreader variability which could provide context for a supplemental radiomics-based tool.

A malignant pleural effusion (MPE) from lung cancer represents stage IV disease and portends a poor prognosis. Routine mutational analysis of tissue samples is the standard of care in advanced lung cancer management because it has treatment implications. Sampling of MPE is minimally invasive, safe, repeatable, and provides both diagnostic and therapeutic value. Mutational analysis on MPE has been shown to be feasible and correlates with a response to targeted therapy with tyrosine kinase inhibitors (TKIs). Guidelines recommend mutational testing in MPE, however there is no one standardized method for testing. There are several testing methods available for mutational analysis in pleural fluid including PCR, mutant-enriched PCR, DNA & RNA sequencing, and immunohistochemistry the sensitivity of which are dependent upon tumor cell heterogeneity. The advantages and disadvantages of each will be reviewed here. Keywords Epidermal growth factor receptor (EGFR). Malignant pleural effusion. Non-small cell lung cancer (NSCLC). Tyrosine kinase inhibitors (TKIs). Molecular testing [9]. This article will review the pathophysiology of MPE, the role of targeted TKIs, methods for EGFR mutational analysis in MPEs, guidelines for mutational analysis testing, and will discuss future directions. Pathophysiology A pleural effusion develops when the production of pleural fluid exceeds its removal. One of the most common causes of effusion associated with impaired removal of pleural fluid

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Purpose To evaluate the association between rurality and lung cancer stage at diagnosis. Methods We conducted a cross‐sectional study using Veterans Health Administration (VHA) data to identify veterans newly diagnosed with lung cancer between October 1, 2011 and September 30, 2015. We defined rurality, based on place of residence, using Rural‐Urban Commuting Area (RUCA) codes with the subcategories of urban, large rural, small rural, and isolated. We used multivariable logistic regression models to determine associations between rurality and stage at diagnosis, adjusting for sociodemographic and clinical characteristics. We also analyzed data using the RUCA code for patients’ assigned primary care sites and driving distances to primary care clinics and medical centers. Findings We identified 4,220 veterans with small cell lung cancer (SCLC) and 25,978 with non‐small cell lung cancer (NSCLC). Large rural residence (compared to urban) was associated with early‐stage diagnosis of NSCLC (OR = 1.12; 95% CI: 1.00‐1.24) and SCLC (OR = 1.73; 95% CI: 1.18‐1.55). However, the finding was significant only in the southern and western regions of the country. White race, female sex, chronic lung disease, higher comorbidity, receiving primary care, being a former tobacco user, and more recent year of diagnosis were also associated with diagnosing early‐stage NSCLC. Driving distance to medical centers was inversely associated with late‐stage NSCLC diagnoses, particularly for large rural areas. Conclusions We did not find clear associations between rurality and lung cancer stage at diagnosis. These findings highlight the complex relationship between rurality and lung cancer within VHA, suggesting access to care cannot be fully captured by current rurality codes.