Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study.

INTRODUCTION/RATIONALE: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT. METHODS: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.05). To predict incident interstitial features, we trained four random forest classifiers in a two-thirds random subset of COPDGene: (1) imaging and demographic information, (2) univariate screen biomarkers, (3) multivariable confirmation biomarkers and (4) multivariable confirmation biomarkers available in a separate testing cohort (Pittsburgh Lung Screening Study (PLuSS)). We evaluated classifier performance in the remaining one-third of COPDGene, and, for the final model, also in PLuSS. RESULTS: In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features. CONCLUSIONS: Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

BACKGROUND: Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen. METHODS: Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set. RESULTS: The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95). CONCLUSIONS: We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers.

OBJECTIVE: Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer. METHODS: A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach. RESULTS: A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16). CONCLUSIONS: The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.

Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.

BACKGROUND: Local governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA. METHODS: The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. RESULTS: The ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates. CONCLUSIONS: These SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.

Quantitative Interstitial Abnormality Progression and Outcomes in the Genetic Epidemiology of COPD and Pittsburgh Lung Screening Study Cohorts.

BACKGROUND: The risk factors and clinical outcomes of quantitative interstitial abnormality progression over time have not been characterized. RESEARCH QUESTIONS: What are the associations of quantitative interstitial abnormality progression with lung function, exercise capacity, and mortality? What are the demographic and genetic risk factors for quantitative interstitial abnormality progression? STUDY DESIGN AND METHODS: Quantitative interstitial abnormality progression between visits 1 and 2 was assessed from 4,635 participants in the Genetic Epidemiology of COPD (COPDGene) cohort and 1,307 participants in the Pittsburgh Lung Screening Study (PLuSS) cohort. We used multivariable linear regression to determine the risk factors for progression and the longitudinal associations between progression and FVC and 6-min walk distance, and Cox regression models for the association with mortality. RESULTS: Age at enrollment, female sex, current smoking status, and the MUC5B minor allele were associated with quantitative interstitial abnormality progression. Each percent annual increase in quantitative interstitial abnormalities was associated with annual declines in FVC (COPDGene: 8.5 mL/y; 95% CI, 4.7-12.4 mL/y; P < .001; PLuSS: 9.5 mL/y; 95% CI, 3.7-15.4 mL/y; P = .001) and 6-min walk distance, and increased mortality (COPDGene: hazard ratio, 1.69; 95% CI, 1.34-2.12; P < .001; PLuSS: hazard ratio, 1.28; 95% CI, 1.10-1.49; P = .001). INTERPRETATION: The objective, longitudinal measurement of quantitative interstitial abnormalities may help identify people at greatest risk for adverse events and most likely to benefit from early intervention.

Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing.

BACKGROUND: Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer. METHODS: Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model. RESULTS: We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules. CONCLUSIONS: Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers. IMPACT: Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.

Early natural menopause is associated with poor lung health and increased mortality among female smokers.

BACKGROUND: Early natural menopause has been regarded as a biomarker of reproductive and somatic aging. Cigarette smoking is the most harmful factor for lung health and also an established risk factor for early menopause. Understanding the effect of early menopause on health outcomes in middle-aged and older female smokers is important to develop preventive strategies. OBJECTIVE: This study aimed to examine the associations of early menopause with multiple lung health and aging biomarkers, lung cancer risk, and all-cause and cause-specific mortality in postmenopausal women who were moderate or heavy smokers. STUDY DESIGN: This study was conducted on postmenopausal women with natural (n=1038) or surgical (n=628) menopause from the Pittsburgh Lung Screening Study. The Pittsburgh Lung Screening Study is a community-based research cohort of current and former smokers, screened with low-dose computed tomography and followed up for lung cancer. Early menopause was defined as occurring before 45 years of age. The analyses were stratified by menopause types because of the different biological and medical causes of natural and surgical menopause. Statistical methods included linear model, generalized linear model, linear mixed-effects model, and time-to-event analysis. RESULTS: The average age of the 1666 female smokers was 59.4±6.7 years, with 1519 (91.2%) of the population as non-Hispanic Whites and 1064 (63.9%) of the population as current smokers at baseline. Overall, 646 (39%) women reported early menopause, including 198 (19.1%) women with natural menopause and 448 (71.3%) women with surgical menopause (P<.001). Demographic variables did not differ between early and nonearly menopause groups, regardless of menopause type. Significant associations were identified between early natural menopause and higher risk of wheezing (odds ratio, 1.65; P<.01), chronic bronchitis (odds ratio, 1.73; P<.01), and radiographic emphysema (odds ratio, 1.70; P<.001) and lower baseline lung spirometry in an obstructive pattern (-104.8 mL/s for forced expiratory volume in the first second with P<.01, -78.6 mL for forced vital capacity with P=.04, and -2.1% for forced expiratory volume in the first second-to-forced vital capacity ratio with P=.01). In addition, early natural menopause was associated with a more rapid decline of forced expiratory volume in the first second-to-forced vital capacity ratio (-0.16% per year; P=.01) and incident airway obstruction (odds ratio, 2.02; P=.04). Furthermore, women early natural menopause had a 40% increased risk of death (P=.023), which was mainly driven by respiratory diseases (hazard ratio, 2.32; P<.001). Mediation analyses further identified that more than 33.3% of the magnitude of the associations between early natural menopause and all-cause and respiratory mortality were explained by baseline forced expiratory volume in the first second. Additional analyses in women with natural menopause identified that the associations between continuous smoking and subsequent lung cancer risk and cancer mortality were moderated by early menopause status, and females with early natural menopause who continued smoking had the worst outcomes (hazard ratio, >4.6; P<.001). This study did not find associations reported above in female smokers with surgical menopause. CONCLUSION: Early natural menopause was found to be a risk factor for malignant and nonmalignant lung diseases and mortality in middle-aged and older female smokers. These findings have strong public health relevance as preventive strategies, including smoking cessation and chest computed tomography screening, should target this population (ie, female smokers with early natural menopause) to improve their postmenopausal health and well-being.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Plasma Based Protein Signatures Associated with Small Cell Lung Cancer.

Small-cell-lung cancer (SCLC) is associated with overexpression of oncogenes including Myc family genes and YAP1 and inactivation of tumor suppressor genes. We performed in-depth proteomic profiling of plasmas collected from 15 individuals with newly diagnosed early stage SCLC and from 15 individuals before the diagnosis of SCLC and compared findings with plasma proteomic profiles of 30 matched controls to determine the occurrence of signatures that reflect disease pathogenesis. A total of 272 proteins were elevated (area under the receiver operating characteristic curve (AUC) ≥ 0.60) among newly diagnosed cases compared to matched controls of which 31 proteins were also elevated (AUC ≥ 0.60) in case plasmas collected within one year prior to diagnosis. Ingenuity Pathway analyses of SCLC-associated proteins revealed enrichment of signatures of oncogenic MYC and YAP1. Intersection of proteins elevated in case plasmas with proteomic profiles of conditioned medium from 17 SCLC cell lines yielded 52 overlapping proteins characterized by YAP1-associated signatures of cytoskeletal re-arrangement and epithelial-to-mesenchymal transition. Among samples collected more than one year prior to diagnosis there was a predominance of inflammatory markers. Our integrated analyses identified novel circulating protein features in early stage SCLC associated with oncogenic drivers.

Exploring the Impact of Lung Cancer Screening on Lung Cancer Mortality of Smokers With Obstructive Lung Disease: Analysis of the NLST-ACRIN Cohort.

BACKGROUND: Lung Cancer (LC) screening with low dose chest computed tomography (LDCT) in smokers reduces LC mortality. Patients with Obstructive Lung Disease (OLD) are at high risk for LC. The potential effect of LC screening in this population is unknown. OBJECTIVE: To determine if screening with LDCT reduces LC mortality in smokers with spirometrically defined OLD. METHODS: The National Lung Screening Trial-American College of Radiology Imaging Network (NLST-ACRIN) study included 13,831 subjects (55-74 years of age with ≥30 pack-year history of smoking) that had a baseline spirometry. Randomly assigned to LDCT or Chest X-ray, all had 3 annual rounds of screening. LC mortality was compared between the LDCT and chest X-ray arms during the 1st year and at 6 years of follow up. Landmark analysis explored LC mortality differences between arms after the first year. RESULTS: From the 4584 subjects with OLD (FEV1/FVC <0.7), 152 (3.3%) died from LC. Multivariable analysis showed that screening trended to decrease LC mortality at 6 years (HR, 95%CI: 0.75, 0.55-1.04, p=0.09). During the 1st year no differences were found between arms (p=0.65). However, after this year, LDCT significantly decreased LC mortality (HR, 95%CI: 0.63, 0.44-0.91, p=0.01). The number needed to screen to avoid one LC death in these subjects was 108 while in those without OLD was 218. CONCLUSIONS: LC screening with LDCT in smokers with spirometrically diagnosed OLD, showed a trend to reduce lung cancer mortality but a study with a larger number of patients and with a more robust design would be needed to confirm these findings.

Contribution of a Blood-Based Protein Biomarker Panel to the Classification of Indeterminate Pulmonary Nodules.

RATIONALE: The workup and longitudinal monitoring for subjects presenting with pulmonary nodules is a pressing clinical problem. A blood-based biomarker panel potentially has utility for identifying subjects at higher risk for harboring a malignant nodule for whom additional workup would be indicated or subjects at reduced risk for whom imaging-based follow-up would be indicated. OBJECTIVES: To assess whether a previously described four-protein biomarker panel, reported to improve assessment of lung cancer risk compared with a smoking-based lung cancer risk model, can provide discrimination between benign and malignant indeterminate pulmonary nodules. METHODS: A previously validated multiplex enzyme-linked immunoassay was performed on matched case and control samples from each cohort. MEASUREMENTS: The biomarker panel was tested in two case-control cohorts of patients presenting with indeterminate pulmonary nodules at the University of Pittsburgh Medical Center and the University of Texas Southwestern. MAIN RESULTS: In both cohorts, the biomarker panel resulted in improved prediction of lung cancer risk over a model on the basis of nodule size alone. Of particular note, the addition of the marker panel to nodule size greatly improved sensitivity at a high specificity in both cohorts. CONCLUSIONS: A four-marker biomarker panel, previously validated to improve lung cancer risk prediction, was found to also have utility in distinguishing benign from malignant indeterminate pulmonary nodules. Its performance in improving sensitivity at a high specificity indicates potential utility of the marker panel in assessing likelihood of malignancy in otherwise indeterminate nodules.

Validation of Histoplasmosis Enzyme Immunoassay to Evaluate Suspicious Lung Nodules.

BACKGROUND: Granulomas caused by infectious lung diseases can present as indeterminate pulmonary nodules (IPN). This study aims to validate an enzyme immunoassay (EIA) for Histoplasma immunoglobulin G (IgG) and immunoglobulin M (IgM) for diagnosing benign IPN in areas with endemic histoplasmosis. METHODS: Prospectively collected serum samples from patients at Vanderbilt University Medical Center (VUMC [n = 204]), University of Pittsburgh Medical Center (n = 71), and University of Cincinnati (n = 51) with IPN measuring 6 to 30 mm were analyzed for Histoplasma IgG and IgM with EIA. Diagnostic test characteristics were compared with results from the VUMC pilot cohort (n = 127). A multivariable logistic regression model was developed to predict granuloma in IPN. RESULTS: Cancer prevalence varied by cohort: VUMC pilot 60%, VUMC validation 65%, University of Pittsburgh Medical Center 35%, and University of Cincinnati 75%. Across all cohorts, 19% of patients had positive IgG titers, 5% had positive IgM, and 3% had positive both IgG and IgM. Of patients with benign disease, 33% were positive for at least one antibody. All patients positive for both IgG and IgM antibodies at acute infection levels had benign disease (n = 13), with a positive predictive value of 100%. The prediction model for granuloma in IPN demonstrated an area under the receiver-operating characteristics curve of 0.84 and Brier score of 0.10. CONCLUSIONS: This study confirmed that Histoplasma EIA testing can be useful for diagnosing benign IPN in areas with endemic histoplasmosis in a population at high risk for lung cancer. Integrating Histoplasma EIA testing into the current diagnostic algorithm where histoplasmosis is endemic could improve management of IPN and potentially decrease unnecessary invasive biopsies.

Quantifying the incremental value of deep learning: Application to lung nodule detection.

We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some domains; however, implementation of these methods is in early development. Often these methods are only directly compared to existing methods; here we present a framework for assessing the value of a machine learning model by assessing the incremental value. We developed a machine learning model to identify and classify lung nodules and assessed the incremental value added to existing risk prediction models. Multiple external datasets were used for validation. We found that our image model, trained on a dataset from The Cancer Imaging Archive (TCIA), improves upon existing models that are restricted to patient characteristics, but it was inconclusive about whether it improves on models that consider nodule features. Another interesting finding is the variable performance on different datasets, suggesting population generalization with machine learning models may be more challenging than is often considered.

Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models.

INTRODUCTION: Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives. METHODS: In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort. RESULTS: Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p<0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules. DISCUSSION: LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.

Factors influencing recurrence following anatomic lung resection for clinical stage I non-small cell lung cancer.

OBJECTIVES: Anatomic lung resection provides the best opportunity for long-term survival in the setting of early-stage non-small cell lung cancer (NSCLC). However, 20-30% of patients develop recurrent disease following complete (R0) resection for Stage I disease. In the current study, we analyze the impact of patient, surgical and pathologic variables upon recurrence patterns following anatomic lung resection for clinical stage I NSCLC. PATIENTS AND METHODS: A total of 1132 patients (384 segmentectomies, 748 lobectomies) with clinical stage I NSCLC were evaluated. Predictors of recurrence were identified by proportional hazards regression. Differences in recurrence patterns between groups are illustrated by log rank tests applied to Kaplan-Maier estimates. RESULTS: A total of 227 recurrences (20.0%) were recorded at a median follow-up of 36.8 months (65 locoregional, 155 distant). There was no significant difference in recurrence patterns when comparing segmentectomy and lobectomy. Multivariate analysis demonstrated that angiolymphatic invasion, tumor size, tumor grade and the presence of only mild-moderate tumor inflammation were independent predictors of recurrence risk. CONCLUSIONS: Recurrence following anatomic lung resection is influenced predominantly by pathological variables (tumor size, tumor grade, angiolymphatic invasion, tumor inflammation). Optimization of surgical margin in relation to tumor size may improve outcomes. Extent of resection (segmentectomy vs. lobectomy) does not appear to have an impact on recurrence-free survival when adequate margins are obtained.

Preoperative brain MRI for clinical stage IA lung cancer: is routine scanning rational?

PURPOSE: Early detection and control of lung cancer brain metastases (BMs) are important. However, several guideline recommendations are inconsistent with regard to routine preoperative brain MRI, especially in patients with clinical stage IA lung cancer. Our study evaluated the value of preoperative brain MRI in patients with clinical stage IA lung cancer. METHODS: A retrospective analysis of patients with lung cancer was performed using a prospectively collected database. Clinical data and the results of brain MRI were collected and analyzed. RESULTS: Patients with pathologically proved primary lung cancer who underwent an MRI at initial diagnosis were identified (3392 patients). In total, 170 patients (5.0%) were diagnosed with BMs. The increased frequency of BMs was significantly associated with advanced clinical stage (P = 0.000) and pathological type (P = 0.011). BMs were detected in 11 out of 1595 patients with clinical stage IA lung cancer (0.7%). BMs were more common in patients with clinical stage cT1c lung cancer (1.9%) than those with clinical stage cT1a or cT1b (0.1%, odds ratio = 21.30, 95% confidence interval: 2.7-166.9, P = 0.000). All patients with stage IA lung cancer and BMs had solid lung lesions (P = 0.002). CONCLUSIONS: Preoperative brain MRI might help identify BMs in patients with lung cancer that has progressed beyond stage IA. In patients with clinical stage IA lung cancer, we do not recommend preoperative brain MRI, but it may potentially be beneficial in those with solid T1c cancers.