The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort

BACKGROUND

Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical prediction models were developed to inform physician assessment of pretest probability of nodule malignancy but have not been validated in a high-risk cohort of nodules for which biopsy was ultimately performed.

RESEARCH QUESTION

Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy?

STUDY DESIGN AND METHODS

We assembled a prospective cohort of 322 indeterminate pulmonary nodules in 282 patients referred to a tertiary medical center for diagnostic navigational bronchoscopy between 2017 and 2019. We calculated the probability of malignancy for each nodule using the Brock model, Mayo Clinic model, and Veterans Affairs (VA) model. On a subset of 168 patients who also had PET-CT scans before biopsy, we also calculated the probability of malignancy using the Herder model. The performance of the models was evaluated by calculating the area under the receiver operating characteristic curves (AUCs) for each model.

RESULTS

The study cohort contained 185 malignant and 137 benign nodules (57% prevalence of malignancy). The malignant and benign cohorts were similar in terms of size, with a median longest diameter for benign and malignant nodules of 15 and 16 mm, respectively. The Brock model, Mayo Clinic model, and VA model showed similar performance in the entire cohort (Brock AUC, 0.70; 95% CI, 0.64-0.76; Mayo Clinic AUC, 0.70; 95% CI, 0.64-0.76; VA AUC, 0.67; 95% CI, 0.62-0.74). For 168 nodules with available PET-CT scans, the Herder model had an AUC of 0.77 (95% CI, 0.68-0.85).

INTERPRETATION

Currently available clinical models provide insufficient discrimination between benign and malignant nodules in the common clinical scenario in which a patient is being referred for biopsy, especially when PET-CT scan information is not available.

Age-related Muscle Fat Infiltration in Lung Screening Participants: Impact of Smoking Cessation

Rationale

Skeletal muscle fat infiltration progresses with aging and is worsened among individuals with a history of cigarette smoking. Many negative impacts of smoking on muscles are likely reversible with smoking cessation.

Objectives

To determine if the progression of skeletal muscle fat infiltration with aging is altered by smoking cessation among lung cancer screening participants.

Methods

This was a secondary analysis based on the National Lung Screening Trial. Skeletal muscle attenuation in Hounsfield unit (HU) was derived from the baseline and follow-up low-dose CT scans using a previously validated artificial intelligence algorithm. Lower attenuation indicates greater fatty infiltration. Linear mixed-effects models were constructed to evaluate the associations between smoking status and the muscle attenuation trajectory.

Measurements and Main Results

Of 19,019 included participants (age: 61 years, 5 [SD]; 11,290 males), 8,971 (47.2%) were actively smoking cigarettes. Accounting for body mass index, pack-years, percent emphysema, and other confounding factors, actively smoking predicted a lower attenuation in both males (β0 =-0.88 HU, P<.001) and females (β0 =-0.69 HU, P<.001), and an accelerated muscle attenuation decline-rate in males (β1=-0.08 HU/y, P<.05). Age-stratified analyses indicated that the accelerated muscle attenuation decline associated with smoking likely occurred at younger age, especially in females.

Conclusions

Among lung cancer screening participants, active cigarette smoking was associated with greater skeletal muscle fat infiltration in both males and females, and accelerated muscle adipose accumulation rate in males. These findings support the important role of smoking cessation in preserving muscle health.

The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation

BACKGROUND

Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation.

RESEARCH QUESTION

Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models?

STUDY DESIGN AND METHODS

Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots.

RESULTS

Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23.

INTERPRETATION

The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.

Assessing the clinical utility of biomarkers using the intervention probability curve (IPC)

BACKGROUND

Assessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease.

OBJECTIVE

To assess the potential impact of a new biomarker for lung cancer using the IPC.

METHODS

The IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker.

RESULTS

The IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds.

CONCLUSIONS

The IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation.

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.

Short-term outcomes of robotic-assisted transthoracic diaphragmatic plication

BACKGROUND

Patients who are symptomatic from diaphragmatic dysfunction may benefit from diaphragmatic plication. We recently modified our plication approach from open thoracotomy to robotic transthoracic. We report our short-term outcomes.

METHODS

We conducted a single-institution retrospective review of all patients who underwent transthoracic plications from 2018, when we began using the robotic approach, to 2022. The primary outcome was short-term recurrence of diaphragm elevation with symptoms noted before or during the first planned postoperative visit. We also compared proportions of short-term recurrences in patients that underwent plication with extracorporeal knot-tying device alone versus those that used intracorporeal instrument tying (alone or supplemental). Secondary outcomes included subjective postoperative improvement of dyspnea at follow-up visit and by postoperative patient questionnaire, chest tube duration, length of stay (LOS), 30-day readmission, operative time, estimated blood loss (EBL), intraoperative complications, and perioperative complications.

RESULTS

Forty-one patients underwent robotic-assisted transthoracic plication. Four patients experienced recurrent diaphragm elevation with symptoms before or during their first routine postoperative visit, occurring on POD 6, 10, 37, and 38. All four recurrences occurred in patients whose plications were performed with the extracorporeal knot-tying device without supplemental intracorporeal instrument tying. Proportion of recurrences in the group that used extracorporeal knot-tying device alone was significantly greater than the recurrences in the group that used intracorporeal instrument tying (alone or supplemental) (P=0.016). The majority (36/41) reported clinical improvement postoperatively and 85% of questionnaire respondents also agreed they would recommend the surgery to others with similar condition. The median LOS and of chest tube duration were 3 days and 2 days, respectively. There were two patients with 30-day readmissions. Three patients developed postoperative pleural effusion necessitating thoracenteses and 8 patients (20%) had postoperative complications. No mortalities were observed.

CONCLUSIONS

While our study shows the overall acceptable safety and favorable outcomes in patients undergoing robotic-assisted transthoracic diaphragmatic plications, the incidence of short-term recurrences and its association with the use of extracorporeally knot-tying device alone in diaphragm plication warrant further investigation.

Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules

A deep learning model (LCP CNN) for the stratification of indeterminate pulmonary nodules (IPNs) demonstrated better discrimination than commonly used clinical prediction models. However, the LCP CNN score is based on a single timepoint that ignores longitudinal information when prior imaging studies are available. Clinically, IPNs are often followed over time and temporal trends in nodule size or morphology inform management. In this study we investigated whether the change in LCP CNN scores over time was different between benign and malignant nodules. This study used a prospective-specimen collection, retrospective-blinded-evaluation (PRoBE) design. Subjects with incidentally or screening detected IPNs 6-30 mm in diameter with at least 3 consecutive CT scans prior to diagnosis (slice thickness ≤ 1.5 mm) with the same nodule present were included. Disease outcome was adjudicated by biopsy-proven malignancy, biopsy-proven benign disease and absence of growth on at least 2-year imaging follow-up. Lung nodules were analyzed using the Optellum LCP CNN model. Investigators performing image analysis were blinded to all clinical data. The LCP CNN score was determined for 48 benign and 32 malignant nodules. There was no significant difference in the initial LCP CNN score between benign and malignant nodules. Overall, the LCP CNN scores of benign nodules remained relatively stable over time while that of malignant nodules continued to increase over time. The difference in these two trends was statistically significant. We also developed a joint model that incorporates longitudinal LCP CNN scores to predict future probability of cancer. Malignant and benign nodules appear to have distinctive trends in LCP CNN score over time. This suggests that longitudinal modeling may improve radiomic prediction of lung cancer over current models. Additional studies are needed to validate these early findings.

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.

Biomarkers in Lung Cancer Screening: a Narrative Review

Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

Risk factors for SARS-CoV-2 related mortality and hospitalization before vaccination: A meta-analysis

The literature remains scarce regarding the varying point estimates of risk factors for COVID-19 associated mortality and hospitalization. This meta-analysis investigates risk factors for mortality and hospitalization, estimates individual risk factor contribution, and determines drivers of published estimate variances. We conducted a systematic review and meta-analysis of COVID-19 related mortality and hospitalization risk factors using PRISMA guidelines. Random effects models estimated pooled risks and meta-regression analyses estimated the impact of geographic region and study type. Studies conducted in North America and Europe were more likely to have lower effect sizes of mortality attributed to chronic kidney disease (OR: 0.21, 95% CI: 0.09-0.52 and OR: 0.25, 95% CI: 0.10-0.63, respectively). Retrospective studies were more likely to have decreased effect sizes of mortality attributed to chronic heart failure compared to prospective studies (OR: 0.65, 95% CI: 0.44-0.95). Studies from Europe and Asia (OR: 0.42, 95% CI: 0.30-0.57 and OR: 0.49, 95% CI: 0.28-0.84, respectively) and retrospective studies (OR: 0.58, 95% CI: 0.47-0.73) reported lower hospitalization risk attributed to male sex. Significant geographic population-based variation was observed in published comorbidity related mortality risks while male sex had less of an impact on hospitalization among European and Asian populations or in retrospective studies.

Associations of Dietary Intakes of Carotenoids and Vitamin A with Lung Cancer Risk in a Low-Income Population in the Southeastern United States

Observational studies found inverse associations of dietary carotenoids and vitamin A intakes with lung cancer risk. However, interventional trials among high-risk individuals showed that β-carotene supplements increased lung cancer risk. Most of the previous studies were conducted among European descendants or Asians. We prospectively examined the associations of lung cancer risk with dietary intakes of carotenoids and vitamin A in the Southern Community Cohort Study, including 65,550 participants with 1204 incident lung cancer cases. Multivariate Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Lung cancer cases had lower energy-adjusted dietary intakes of all carotenoids and vitamin A than non-cases. However, dietary intakes of carotenoids and vitamin A were not associated with overall lung cancer risk. A significant positive association of dietary vitamin A intake with lung cancer risk was observed among current smokers (HR_{Q4 vs. Q1} = 1.23; 95% CI: 1.02-1.49; P_trend = 0.01). In addition, vitamin A intake was associated with an increased risk of adenocarcinoma among African Americans (HR_{Q4 vs. Q1} = 1.55; 95%CI: 1.08-2.21; P_trend = 0.03). Dietary lycopene intake was associated with an increased risk of lung cancer among former smokers (HR_{Q4 vs. Q1} = 1.50; 95% CI: 1.04-2.17; P_trend = 0.03). There are positive associations of dietary β-cryptoxanthin intake with squamous carcinoma risk (HR_{Q4 vs. Q1} = 1.49; 95% CI: 1.03-2.15; P_trend = 0.03). Further studies are warranted to confirm our findings.

Deep Breaths: A Systematic Review of the Potential Effects of Employment in the Nuclear Industry on Mortality from Non-Malignant Respiratory Disease

Ionizing radiation is an established carcinogen, but its effects on non-malignant respiratory disease (NMRD) are less clear. Cohorts exposed to multiple risk factors including radiation and toxic dusts conflate these relationships, and there is a need for clarity in previous findings. This systematic review was conducted to survey the body of existing evidence for radiation effects on NMRD in global nuclear worker cohorts. A PubMed search was conducted for studies with terms relating to radiation or uranium and noncancer respiratory outcomes. Papers were limited to the most recent report within a single cohort published between January 2000 and December 2020. Publication quality was assessed based upon UNSCEAR 2017 criteria. In total, 31 papers were reviewed. Studies included 29 retrospective cohorts, one prospective cohort, and one longitudinal cohort primarily comprising White men from the U.S., Canada and Western Europe. Ten studies contained subpopulations of uranium miners or millers. Papers reported standardized mortality ratio (SMR) analyses, regression analyses, or both. Neither SMR nor regression analyses consistently showed a relationship between radiation exposure and NMRD. A meta-analysis of excess relative risks (ERRs) for NMRD did not present evidence for a dose-response (overall ERR/Sv: 0.07; 95% CI: -0.07, 0.21), and results for more specific outcomes were inconsistent. Significantly elevated SMRs for NMRD overall were observed in two studies among the subpopulation of uranium miners and millers (combined n = 4229; SMR 1.42-1.43), indicating this association may be limited to mining and milling populations and may not extend to other nuclear workers. A quality review showed limited capacity of 17 out of 31 studies conducted to provide evidence for a causal relationship between radiation and NMRD; the higher-quality studies showed no consistent relationship. All elevated NMRD SMRs were among mining and milling cohorts, indicating different exposure profiles between mining and non-mining cohorts; future pooled cohorts should adjust for mining exposures or address mining cohorts separately.

The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers

BACKGROUND

Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect.

METHODS

Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets.

RESULTS

Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 &gt; 0.97 for each) with fitted parameters closely aligned with professional society guidelines.

CONCLUSIONS

The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice.

IMPACT

We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers

BACKGROUND

Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.

METHODS

Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.

RESULTS

Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.

CONCLUSIONS

A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

Abstract 9: Will our biomarkers have an impact? Estimating the utility of adding biomarkers to diagnostic risk models for lung cancer using intervention probability curves

Introduction: Assessing the potential clinical value of adding a new biomarker to a diagnostic risk model is a necessary step to improve clinical outcomes. The reclassification of patients across clinically relevant subgroups, such as low risk/intermediate risk/high risk, is considered the best metric to estimate potential utility. However, data from clinical decisions demonstrates that the “all-or-nothing” approach to threshold-based decisions is, in practice, incorrect. Here we present a novel approach to assessing biomarker utility, the change in intervention probability (IP). The intervention probability curve (IPC) models the likelihood that a provider will choose the intervention as a continuous function of the risk of disease. To assess the impact of adding a new biomarker, the change in likelihood of intervention is calculated for each patient based upon their change in risk. The summary statistics of the change in likelihood of intervention over the population can be descriptive of the expected clinical impact of the added biomarker.

Methods: An IPC for lung cancer diagnosis based upon the Mayo Clinic Model was derived from the National Lung Screening Trial. Each patient’s baseline intervention probability to undergo a diagnostic bronchoscopy for an indeterminate pulmonary nodule (IPN) was calculated based upon their Mayo risk. Their post-test IP was calculated using a new biomarker strategy incorporating a blood test (CYFRA 21-1), a quantitative radiomics signature, and patient clinical history assessed in 457 patients with IPNs between 6-30 mm). The bias-corrected clinical net reclassification index (cNRI) was calculated as the comparator method for estimating biomarker utility.

Results: Based upon ACCP risk thresholds of 0.05 and 0.65, the cNRI was 0.08 for the control population and -0.003 for the case population. Interestingly, despite greatly improved diagnostic accuracy (AUC of 0.754 improved to 0.855), the cNRI shows a very modest improvement in the control population and no improvement in the case population. However, the results of the IPC analysis show that over the entire population, there would be a net decrease in interventions among the control population of 8.3%, and a net increase in interventions among the case population of 0.8%.

Conclusions: Analysis of the change in probability of intervention provides a more informative perspective of which patients would benefit from the addition of the combined biomarker method.

Citation Format: Michael Nolan Kammer, Dianna J. Rowe, Stephen A. Deppen, Eric Grogan, Fabien Maldonado. Will our biomarkers have an impact? Estimating the utility of adding biomarkers to diagnostic risk models for lung cancer using intervention probability curves [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 9.

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Intrinsic Evaluation of Contextual and Non-contextual Word Embeddings using Radiology Reports

Many clinical natural language processing methods rely on non-contextual word embedding (NCWE) or contextual word embedding (CWE) models. Yet, few, if any, intrinsic evaluation benchmarks exist comparing embedding representations against clinician judgment. We developed intrinsic evaluation tasks for embedding models using a corpus of radiology reports: term pair similarity for NCWEs and cloze task accuracy for CWEs. Using surveys, we quantified the agreement between clinician judgment and embedding model representations. We compare embedding models trained on a custom radiology report corpus (RRC), a general corpus, and PubMed and MIMIC-III corpora (P&MC). Cloze task accuracy was equivalent for RRC and P&MC models. For term pair similarity, P&MC-trained NCWEs outperformed all other NCWE models (ρ_spearman 0.61 vs. 0.27-0.44). Among models trained on RRC, fastText models often outperformed other NCWE models and spherical embeddings provided overly optimistic representations of term pair similarity.

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.

Modeling the impact of delaying surgery for early esophageal cancer in the era of COVID-19

BACKGROUND

Surgical society guidelines have recommended changing the treatment strategy for early esophageal cancer during the novel coronavirus (COVID-19) pandemic. Delaying resection can allow for interim disease progression, but the impact of this delay on mortality is unknown. The COVID-19 infection rate at which immediate operative risk exceeds benefit is unknown. We sought to model immediate versus delayed surgical resection in a T1b esophageal adenocarcinoma.

METHODS

A decision analysis model was developed, and sensitivity analyses performed. The base case was a 65-year-old male smoker presenting with cT1b esophageal adenocarcinoma scheduled for esophagectomy during the COVID-19 pandemic. We compared immediate surgical resection to delayed resection after 3 months. The likelihood of key outcomes was derived from the literature where available. The outcome was 5-year overall survival.

RESULTS

Proceeding with immediate esophagectomy for the base case scenario resulted in slightly improved 5-year overall survival when compared to delaying surgery by 3 months (5-year overall survival 0.74 for immediate and 0.73 for delayed resection). In sensitivity analyses, a delayed approach became preferred when the probability of perioperative COVID-19 infection increased above 7%.

CONCLUSIONS

Immediate resection of early esophageal cancer during the COVID-19 pandemic did not decrease 5-year survival when compared to resection after 3 months for the base case scenario. However, as the risk of perioperative COVID-19 infection increases above 7%, a delayed approach has improved 5-year survival. This balance should be frequently re-examined by surgeons as infection risk changes in each hospital and community throughout the COVID-19 pandemic.

Women screened for breast cancer are dying from lung cancer: An opportunity to improve lung cancer screening in a mammography population

OBJECTIVE

Lung cancer is the leading cancer killer in women, resulting in more deaths than breast, cervical and ovarian cancer combined. Screening for lung cancer has been shown to significantly reduce mortality, with some evidence that women may have a greater benefit. This study demonstrates that a population of women being screened for breast cancer may greatly benefit from screening for lung cancer.

METHODS

Data from 18,040 women who were screened for breast cancer in 2015 at two imaging facilities that also performed lung screening were reviewed. A natural language-processing algorithm followed by a manual chart review identified women eligible for lung cancer screening by U.S. Preventive Services Task Force (USPSTF) criteria. A chart review of these eligible women was performed to determine subsequent enrollment in a lung screening program (2016-2019), current screening eligibility, cancer diagnoses and cancer-related outcomes.

RESULTS

Natural language processing identified 685 women undergoing screening mammography who were also potentially eligible for lung screening based on age and smoking history. Manual chart review confirmed 251 were eligible under USPSTF criteria. By June 2019, 63 (25%) had enrolled in lung screening, of which three were diagnosed with screening-detected lung cancer resulting in zero deaths. Of 188 not screened, seven were diagnosed with lung cancer resulting in five deaths by study end. Four women received a diagnosis of breast cancer with no deaths.

CONCLUSION

Women screened for breast cancer are dying from lung cancer. We must capitalize on reducing barriers to improve screening for lung cancer among high-risk women.

Preoperative coronary artery calcifications in veterans predict higher all-cause mortality in early-stage lung cancer: a cohort study

Background

Lung cancer patients often have comorbidities that may impact survival. This observational cohort study examines whether coronary artery calcifications (CAC) impact all-cause mortality in patients with resected stage I non-small cell lung cancer (NSCLC).

Methods

Veterans with stage I NSCLC who underwent resection at a single institution between 2005 and 2018 were selected from a prospectively collected database. Radiologists blinded to patient outcomes graded CAC severity (mild, moderate, or severe) in preoperative CT scans using a visual estimation scoring system. Inter-rater reliability was calculated using the kappa statistic. All-cause mortality was the primary outcome. Kaplan-Meier survival analysis and Cox proportional hazards regression were used to compare time-to-death by varying CAC.

Results

The Veteran patients (n=195) were predominantly older (median age of 67) male (98%) smokers (96%). The majority (68%) were pathologic stage IA. Overall, 12% of patients had no CAC, 27% mild, 26% moderate, and 36% severe CAC. Median unadjusted survival was 8.8 years for patients with absent or mild CAC versus 6.3 years for moderate and 5.9 years for severe CAC (P=0.01). The adjusted hazard ratio for moderate CAC was 1.44 (95% CI, 0.85–2.46) and for severe CAC was 1.73 (95% CI, 1.03–2.88; P for trend <0.05).

Conclusions

The presence of severe CAC on preoperative imaging significantly impacted the all-cause survival of patients undergoing resection for stage I NSCLC. This impact on mortality should be taken into consideration by multidisciplinary teams when making treatment plans for patients with early-stage disease.

A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19

OBJECTIVE

During the COVID-19 pandemic, health systems postponed non-essential medical procedures to accommodate surge of critically-ill patients. The long-term consequences of delaying procedures in response to COVID-19 remains unknown. We developed a high-throughput approach to understand the impact of delaying procedures on patient health outcomes using electronic health record (EHR) data.

MATERIALS AND METHODS

We used EHR data from Vanderbilt University Medical Center's (VUMC) Research and Synthetic Derivatives. Elective procedures and non-urgent visits were suspended at VUMC between March 18, 2020 and April 24, 2020. Surgical procedure data from this period were compared to a similar timeframe in 2019. Potential adverse impact of delay in cardiovascular and cancer-related procedures was evaluated using EHR data collected from January 1, 1993 to March 17, 2020. For surgical procedure delay, outcomes included length of hospitalization (days), mortality during hospitalization, and readmission within six months. For screening procedure delay, outcomes included 5-year survival and cancer stage at diagnosis.

RESULTS

We identified 416 surgical procedures that were negatively impacted during the COVID-19 pandemic compared to the same timeframe in 2019. Using retrospective data, we found 27 significant associations between procedure delay and adverse patient outcomes. Clinician review indicated that 88.9% of the significant associations were plausible and potentially clinically significant. Analytic pipelines for this study are available online.

CONCLUSION

Our approach enables health systems to identify medical procedures affected by the COVID-19 pandemic and evaluate the effect of delay, enabling them to communicate effectively with patients and prioritize rescheduling to minimize adverse patient outcomes.

Mortality for Robotic- vs Video-Assisted Lobectomy-Treated Stage I Non-Small Cell Lung Cancer Patients

Background

To address the US Food and Drug Administration’s recent safety concern on robotic surgery procedures, we compared short- and long-term mortality for stage I non-small cell lung cancer (NSCLC) patients treated by robotic-assisted thoracoscopic surgical lobectomy (RATS-L) vs video-assisted thoracoscopic surgical lobectomy (VATS-L).

Methods

From the National Cancer Database, we identified 18 908 stage I NSCLC patients who underwent RATS-L or VATS-L as the primary operation from 2010 to 2014. Cox proportional hazards models were used to estimate hazard ratios (HRs) for short- and long-term mortality using unmatched and propensity score–matched analyses. All statistical tests were 2-sided.

Results

Patients treated by RATS-L had higher 90-day mortality than those with VATS-L (6.6% vs 3.8%, P = .03) if conversion to open thoracotomy occurred. After excluding first-year observation, multiple regression analyses showed RATS-L was associated with increased long-term mortality, compared with VATS-L, in cases with tumor size 20 mm or less: hazard ratio (HR) = 1.33 (95% confidence interval [CI] = 1.15 to 1.55), HR = 1.36 (95% CI = 1.17 to 1.58), and HR = 1.33 (95% CI = 1.11 to 1.61) for unmatched, N:1 matched, and 1:1 matched analyses, respectively, in the intention-to-treat analysis. Among patients without conversion to an open thoracotomy, the respective hazard ratios were 1.19 (95% CI = 1.10 to 1.29), 1.19 (95% CI = 1.10 to 1.29), and 1.17 (95% CI = 1.06 to 1.29). Similar associations were observed when follow-up time started 18 or 24 months postsurgery. No statistically significant mortality difference was found for patients with tumor size of greater than 20 mm. These associations were not related to case volume of VATS-L or RATS-L performed at treatment institutes.

Conclusions

Patients with small (≤20 mm) stage I NSCLC treated with RATS-L had statistically significantly higher long-term mortality risk than VATS-L after 1 year postsurgery.

Uptake of Video-Assisted Thoracoscopic Lung Resections Within the Veterans Affairs for Known or Suspected Lung Cancer

Importance

Minimally invasive lobectomy for early-stage lung cancer has become more prevalent. Video-assisted thoracoscopic surgery has lower rates of morbidity, better long-term survival, and equivalent oncologic outcomes compared with thoracotomy. However, little has been published on the use and outcomes of video-assisted thoracoscopic surgery within Veterans Affairs. There is a public assumption that the the Veterans Affairs is slow to adopt new procedures and technologies.

Objective

To determine the uptake of video-assisted thoracoscopic surgery within the Veterans Affairs for patients with known or suspected lung cancer.

Design, Setting, and Participants

In this retrospective cohort study of national Veterans Affairs Corporate Data Warehouse data from January 2002 to December 2015, a total of 11 004 veterans underwent lung resection for known or suspected lung cancer. Data were analyzed from March to November 2018.

Exposures

Open or video-assisted thoracoscopic lobectomy or wedge resection.

Main Outcomes and Measures

Patient demographic characteristics and procedure and diagnosis International Classification of Diseases, Ninth Revision codes were abstracted from Corporate Data Warehouse data.

Results

Of the 11 004 included veterans, 10 587 (96.2%) were male, and the median (interquartile range) age was 66.0 (61.0-72.0) years. Of 11 004 included procedures, 8526 (77.5%) were lobectomies and 2478 (22.5%) were wedge resections. The proportion of video-assisted thoracoscopic lung resections increased steadily from 15.6% in 2002 to 50.6% in 2015. Video-assisted thoracoscopic surgery use by Veterans Integrated Service Networks ranged from 0% to 81.7%, and higher Veterans Integrated Service Network volume was correlated with higher video-assisted thoracoscopic surgery use (Pearson r = 0.35; 95% CI, 0.15-0.52; P < .001). Video-assisted thoracoscopic surgery use and rate of uptake varied widely across Veteran Affairs regions (P < .001 by Wilcoxon signed rank test).

Conclusions and Relevance

Paralleling academic hospitals, most lung resections are now performed in the Veterans Affairs using video-assisted thoracoscopic surgery. More research is needed to identify reasons behind the heterogeneous uptake of video-assisted thoracoscopic surgery across Veterans Affairs regions.

Impact of Culture-Independent Diagnostic Testing on Recovery of Enteric Bacterial Infections

Background

Culture-independent diagnostic tests (CIDTs) are increasingly used to identify enteric pathogens. However, foodborne illness surveillance systems have relied upon culture confirmation to estimate disease burden and identify outbreaks through molecular subtyping. This study examined the impacts of CIDT and estimated costs for culture verification of Shigella, Salmonella, Shiga toxin-producing Escherichia coli (STEC), and Campylobacter at the Tennessee Department of Health Public Health Laboratory (PHL).

Methods

This observational study included laboratory and epidemiological surveillance data collected between years 2013-2016 from patients with the reported enteric illness. We calculated pathogen recovery at PHL based on initial diagnostic test type reported at the clinical laboratory. Adjusted prevalence ratios (PRs) and 95% confidence intervals (CIs) were estimated with modified Poisson regression. Estimates of cost were calculated for pathogen recovery from CIDT-positive specimens compared to recovery from culture-derived isolates.

Results

During the study period, PHL received 5553 specimens from clinical laboratories from patients with the enteric illness. Pathogen recovery was 57% (984/1713) from referred CIDT-positive stool specimens and 95% (3662/3840) from culture-derived isolates (PR, 0.61 [95% CI, .56-.66]). Pathogen recovery from CIDT-positive specimens varied based on pathogen type: Salmonella (72%), Shigella (64%), STEC (57%), and Campylobacter (26%). Compared to stool culture-derived isolates, the cost to recover pathogens from 100 CIDT-positive specimens was higher for Shigella (US $6192), Salmonella (US $18373), and STEC (US $27783).

Conclusions

Pathogen recovery was low from CIDT-positive specimens for enteric bacteria. This has important implications for the current enteric disease surveillance system, outbreak detection, and costs for public health programs.

Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data Model: A Multidimensional Quality Assurance Approach

BACKGROUND

The development and adoption of health care common data models (CDMs) has addressed some of the logistical challenges of performing research on data generated from disparate health care systems by standardizing data representations and leveraging standardized terminology to express clinical information consistently. However, transforming a data system into a CDM is not a trivial task, and maintaining an operational, enterprise capable CDM that is incrementally updated within a data warehouse is challenging.

OBJECTIVES

To develop a quality assurance (QA) process and code base to accompany our incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse health care database into the Observational Medical Outcomes Partnership (OMOP) CDM to prevent incremental load errors.

METHODS

We designed and implemented a multistage QA) approach centered on completeness, value conformance, and relational conformance data-quality elements. For each element we describe key incremental load challenges, our extract, transform, and load (ETL) solution of data to overcome those challenges, and potential impacts of incremental load failure.

RESULTS

Completeness and value conformance data-quality elements are most affected by incremental changes to the CDW, while updates to source identifiers impact relational conformance. ETL failures surrounding these elements lead to incomplete and inaccurate capture of clinical concepts as well as data fragmentation across patients, providers, and locations.

CONCLUSION

Development of robust QA processes supporting accurate transformation of OMOP and other CDMs from source data is still in evolution, and opportunities exist to extend the existing QA framework and tools used for incremental ETL QA processes.

Assessment of Fluorodeoxyglucose F18-Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules

Importance

Clinicians rely heavily on fluorodeoxyglucose F18-labeled positron emission tomography (FDG-PET) imaging to evaluate lung nodules suspicious for cancer. We evaluated the performance of FDG-PET for the diagnosis of malignancy in differing populations with varying cancer prevalence.

Objective

To determine the performance of FDG-PET/computed tomography (CT) in diagnosing lung malignancy across different populations with varying cancer prevalence.

Design, Setting, and Participants

Multicenter retrospective cohort study at 6 academic medical centers and 1 Veterans Affairs facility that comprised a total of 1188 patients with known or suspected lung cancer from 7 different cohorts from 2005 to 2015.

Exposures

18F fluorodeoxyglucose PET/CT imaging.

Main Outcome and Measures

Final diagnosis of cancer or benign disease was determined by pathological tissue diagnosis or at least 18 months of stable radiographic follow-up.

Results

Most patients were male smokers older than 60 years. Overall cancer prevalence was 81% (range by cohort, 50%-95%). The median nodule size was 22 mm (interquartile range, 15-33 mm). Positron emission tomography/CT sensitivity and specificity were 90.1% (95% CI, 88.1%-91.9%) and 39.8% (95% CI, 33.4%-46.5%), respectively. False-positive PET scans occurred in 136 of 1188 patients. Positive predictive value and negative predictive value were 86.4% (95% CI, 84.2%-88.5%) and 48.7% (95% CI, 41.3%-56.1%), respectively. On logistic regression, larger nodule size and higher population cancer prevalence were both significantly associated with PET accuracy (odds ratio, 1.027; 95% CI, 1.015-1.040 and odds ratio, 1.030; 95% CI, 1.021-1.040, respectively). As the Mayo Clinic model-predicted probability of cancer increased, the sensitivity and positive predictive value of PET/CT imaging increased, whereas the specificity and negative predictive value dropped.

Conclusions and Relevance

High false-positive rates were observed across a range of cancer prevalence. Normal PET/CT scans were not found to be reliable indicators of the absence of disease in patients with a high probability of lung cancer. In this population, aggressive tissue acquisition should be prioritized using a comprehensive lung nodule program that emphasizes advanced tissue acquisition techniques such as CT-guided fine-needle aspiration, navigational bronchoscopy, and endobronchial ultrasonography.

Communication About the Probability of Cancer in Indeterminate Pulmonary Nodules

Importance

Clinical guidelines recommend that clinicians estimate the probability of malignancy for patients with indeterminate pulmonary nodules (IPNs) larger than 8 mm. Adherence to these guidelines is unknown.

Objectives

To determine whether clinicians document the probability of malignancy in high-risk IPNs and to compare these quantitative or qualitative predictions with the validated Mayo Clinic Model.

Design, Setting, and Participants

Single-institution, retrospective cohort study of patients from a tertiary care Department of Veterans Affairs hospital from January 1, 2003, through December 31, 2015. Cohort 1 included 291 veterans undergoing surgical resection of known or suspected lung cancer from January 1, 2003, through December 31, 2015. Cohort 2 included a random sample of 239 veterans undergoing inpatient or outpatient pulmonary evaluation of IPNs at the hospital from January 1, 2003, through December 31, 2012.

Exposures

Clinician documentation of the quantitative or qualitative probability of malignancy.

Main Outcomes and Measures

Documentation from pulmonary and/or thoracic surgery clinicians as well as information from multidisciplinary tumor board presentations was reviewed. Any documented quantitative or qualitative predictions of malignancy were extracted and summarized using descriptive statistics. Clinicians' predictions were compared with risk estimates from the Mayo Clinic Model.

Results

Of 291 patients in cohort 1, 282 (96.9%) were men; mean (SD) age was 64.6 (9.0) years. Of 239 patients in cohort 2, 233 (97.5%) were men; mean (SD) age was 65.5 (10.8) years. Cancer prevalence was 258 of 291 cases (88.7%) in cohort 1 and 110 of 225 patients with a definitive diagnosis (48.9%) in cohort 2. Only 13 patients (4.5%) in cohort 1 and 3 (1.3%) in cohort 2 had a documented quantitative prediction of malignancy prior to tissue diagnosis. Of the remaining patients, 217 of 278 (78.1%) in cohort 1 and 149 of 236 (63.1%) in cohort 2 had qualitative statements of cancer risk. In cohort 2, 23 of 79 patients (29.1%) without any documented malignancy risk statements had a final diagnosis of cancer. Qualitative risk statements were distributed among 32 broad categories. The most frequently used statements aligned well with Mayo Clinic Model predictions for cohort 1 compared with cohort 2. The median Mayo Clinic Model-predicted probability of cancer was 68.7% (range, 2.4%-100.0%). Qualitative risk statements roughly aligned with Mayo predictions.

Conclusions and Relevance

Clinicians rarely provide quantitative documentation of cancer probability for high-risk IPNs, even among patients drawn from a broad range of cancer probabilities. Qualitative statements of cancer risk in current practice are imprecise and highly variable. A standard scale that correlates with predicted cancer risk for IPNs should be used to communicate with patients and other clinicians.

Developing prediction models for clinical use using logistic regression: an overview

Prediction models help healthcare professionals and patients make clinical decisions. The goal of an accurate prediction model is to provide patient risk stratification to support tailored clinical decision-making with the hope of improving patient outcomes and quality of care. Clinical prediction models use variables selected because they are thought to be associated (either negatively or positively) with the outcome of interest. Building a model requires data that are computer-interpretable and reliably recorded within the time frame of interest for the prediction. Such models are generally defined as either diagnostic, likelihood of disease or disease group classification, or prognostic, likelihood of response or risk of recurrence. We describe a set of guidelines and heuristics for clinicians to use to develop a logistic regression-based prediction model for binary outcomes that is intended to augment clinical decision-making.

Accuracy of a Novel Histoplasmosis Enzyme Immunoassay to Evaluate Suspicious Lung Nodules

BACKGROUND

Granulomas caused by infectious lung diseases present as indeterminate pulmonary nodules (IPNs) on radiography. Newly available serum enzyme immunoassay (EIA) for histoplasmosis has not been studied for the evaluation of IPNs. We investigated serum biomarkers of histoplasmosis antibodies as an indication of benign disease in IPNs from a highly endemic region.

METHODS

A total of 152 serum samples from patients presenting with pulmonary nodules ≤30 mm in maximum diameter were analyzed for histoplasmosis antibodies by immunodiffusion and EIA IgG and IgM tests. Serology and FDG-PET/CT scan diagnostic test characteristics were estimated and compared.

RESULTS

Cancer prevalence was 55% (n = 83). Thirty-nine (26%) individuals were positive for IgG histoplasmosis antibodies. Twelve samples were IgM antibody positive. Immunodiffusion serology was similar to IgM antibody results with 13 positive tests. Diagnostic likelihood ratios for benign disease were 0.62, 0.33, and 0.28 for FDG-PET/CT, IgG, and IgM antibodies, respectively. When both IgG and IgM were positive (n = 8), no nodules were cancerous and six were FDG-PET/CT avid.

CONCLUSIONS

A positive EIA test for both IgM and IgG strongly suggested histoplasmosis etiology and benign granuloma for 12% of benign nodules arising from a highly endemic region. Presence of either IgG or IgM histoplasma antibodies was associated with benign disease. The EIA test was more sensitive in assessing histoplasma exposure than immunodiffusion serology.

IMPACT

A new CLIA-certified histoplasmosis antibody EIA test measures histoplasmosis exposure, offers a possible alternative clinical diagnosis for benign IPNs, and may improve IPN evaluation while avoiding harmful invasive biopsies.

Racial Disparities in Lung Cancer Survival: The Contribution of Stage, Treatment, and Ancestry

INTRODUCTION

Lung cancer is a leading cause of cancer-related death worldwide. Racial disparities in lung cancer survival exist between blacks and whites, yet they are limited by categorical definitions of race. We sought to examine the impact of African ancestry on overall survival among blacks and whites with NSCLC cases.

METHODS

Incident cases of NSCLC in blacks and whites from the prospective Southern Community Cohort Study (N = 425) were identified through linkage with state cancer registries in 12 southern states. Vital status was determined by linkage with the National Death Index and Social Security Administration. We evaluated the impact of African ancestry (as estimated by using genome-wide ancestry-informative markers) on overall survival by calculating the time-dependent area under the curve (AUC) for Cox proportional hazards models, adjusting for relevant covariates such as stage and treatment. We replicated our findings in an independent population of NSCLC cases in blacks.

RESULTS

Global African ancestry was not significantly associated with overall survival among NSCLC cases. There was no change in model performance when Cox proportional hazards models with and without African ancestry were compared (AUC = 0.79 for each model). Removal of stage and treatment reduced the average time-dependent AUC from 0.79 to 0.65. Similar findings were observed in our replication study.

CONCLUSIONS

Stage and treatment are more important predictors of survival than African ancestry is. These findings suggest that racial disparities in lung cancer survival may disappear with similar early detection efforts for blacks and whites.

Timeliness of Care and Lung Cancer Tumor-Stage Progression: How Long Can We Wait?

BACKGROUND

Timely care of lung cancer is presumed critical, yet clear evidence of stage progression with delays in care is lacking. We investigated the reasons for delays in treatment and the impact these delays have on tumor-stage progression.

METHODS

We queried our retrospective database of 265 veterans who underwent cancer resection from 2005 to 2015. We extracted time intervals between nodule identification, diagnosis, and surgical resection; changes in nodule radiographic size over time; final pathologic staging; and reasons for delays in care. Pearson's correlation and Fisher's exact test were used to compare cancer growth and stage by time to treatment.

RESULTS

Median time from referral to surgical evaluation was 11 days (interquartile range, 8 to 17). Median time from identification to therapeutic resection was 98 days (interquartile range, 66 to 139), and from diagnosis to resection, 53 days (interquartile range, 35 to 77). Sixty-eight patients (26%) were diagnosed at resection; the remainder had preoperative tissue diagnoses. No significant correlation existed between tumor growth and time between nodule identification and resection, or between tumor growth and time between diagnosis and resection. Among 197 patients with preoperative diagnoses, 42% (83) had intervals longer than 60 days between diagnosis and resection. Most common reasons for delay were cardiac clearance, staging, and smoking cessation. Larger nodules had fewer days between identification and resection (p = 0.03).

CONCLUSIONS

Evaluation, staging, and smoking cessation drive resection delays. The lack of association between tumor growth and time to treatment suggests other clinical or biological factors, not time alone, underlie growth risk. Until these factors are identified, delays to diagnosis and treatment should be minimized.

68Ga-DOTATATE Compared with 111In-DTPA-Octreotide and Conventional Imaging for Pulmonary and Gastroenteropancreatic Neuroendocrine Tumors: A Systematic Review and Meta-Analysis

Neuroendocrine tumors (NETs) are uncommon tumors with increasing incidence and prevalence. Current reports suggest that (68)Ga-DOTATATE PET/CT imaging improves diagnosis and staging of NETs compared with (111)In-DTPA-octreotide and conventional imaging. We performed a systematic review of (68)Ga-DOTATATE for safety and efficacy compared with octreotide and conventional imaging to determine whether available evidence supports U.S. Food and Drug Administration approval.

METHODS

Medline, EMBASE, Web of Science, and Cochrane Reviews electronic databases were searched from January 1999 to September 2015. Results were restricted to human studies comparing diagnostic accuracy of (68)Ga-DOTATATE with octreotide or conventional imaging for pulmonary or gastroenteropancreatic NET and for human studies reporting safety/toxicity for (68)Ga-DOTATATE with 10 subjects or more thought to have NETs. Direct communication with corresponding authors was attempted to obtain missing information. Abstracts meeting eligibility criteria were collected by a research librarian and assembled for reviewers; 2 reviewers independently determined whether or not to include each abstract. If either reviewer chose inclusion, the abstract was accepted for review.

RESULTS

Database and bibliography searches yielded 2,479 articles, of which 42 were eligible. Three studies compared the 2 radiopharmaceuticals in the same patient, finding (68)Ga-DOTATATE to be more sensitive than octreotide. Nine studies compared (68)Ga-DOTATATE with conventional imaging. (68)Ga-DOTATATE estimated sensitivity, 90.9% (95% confidence interval, 81.4%-96.4%), and specificity, 90.6% (95% confidence interval, 77.8%-96.1%), were high. Five studies were retained for safety reporting only. Report of harm possibly related to (68)Ga-DOTATATE was rare (6 of 974), and no study reported major toxicity or safety issues.

CONCLUSION

No direct comparison of octreotide and (68)Ga-DOTATATE imaging for diagnosis and staging in an unbiased population of NETs has been published. Available information in the peer-reviewed literature regarding diagnostic efficacy and safety supports the use of (68)Ga-DOTATATE for imaging of NETs where it is available.

Safety and Efficacy of 68Ga-DOTATATE PET/CT for Diagnosis, Staging, and Treatment Management of Neuroendocrine Tumors

Our purpose was to evaluate the safety and efficacy of (68)Ga-DOTATATE PET/CT compared with (111)In-pentetreotide imaging for diagnosis, staging, and restaging of pulmonary and gastroenteropancreatic neuroendocrine tumors.

METHODS

(68)Ga-DOTATATE PET/CT and (111)In-pentetreotide scans were obtained for 78 of 97 consecutively enrolled patients with known or suspected pulmonary or gastroenteropancreatic neuroendocrine tumors. Safety and toxicity were measured by comparing vital signs, serum chemistry values, or acquisition-related medical complications before and after (68)Ga-DOTATATE injection. Added value was determined by changes in treatment plan when (68)Ga-DOTATATE PET/CT results were added to all prior imaging, including (111)In-pentetreotide. Interobserver reproducibility of (68)Ga-DOTATATE PET/CT scan interpretation was measured between blinded and nonblinded interpreters.

RESULTS

(68)Ga-DOTATATE PET/CT and (111)In-pentetreotide scans were significantly different in impact on treatment (P < 0.001). (68)Ga-DOTATATE PET/CT combined with CT or liver MRI changed care in 28 of 78 (36%) patients. Interobserver agreement between blinded and nonblinded interpreters was high. No participant had a trial-related event requiring treatment. Mild, transient events were tachycardia in 1, alanine transaminase elevation in 1, and hyperglycemia in 2 participants. No clinically significant arrhythmias occurred. (68)Ga-DOTATATE PET/CT correctly identified 3 patients for peptide-receptor radiotherapy incorrectly classified by (111)In-pentetreotide.

CONCLUSION

(68)Ga-DOTATATE PET/CT was equivalent or superior to (111)In-pentetreotide imaging in all 78 patients. No adverse events requiring treatment were observed. (68)Ga-DOTATATE PET/CT changed treatment in 36% of participants. Given the lack of significant toxicity, lower radiation exposure, and improved accuracy compared with (111)In-pentetreotide, (68)Ga-DOTATATE imaging should be used instead of (111)In-pentetreotide imaging where available.

Predicting lung cancer prior to surgical resection in patients with lung nodules

BACKGROUND

Existing predictive models for lung cancer focus on improving screening or referral for biopsy in general medical populations. A predictive model calibrated for use during preoperative evaluation of suspicious lung lesions is needed to reduce unnecessary operations for a benign disease. A clinical prediction model (Thoracic Research Evaluation And Treatment [TREAT]) is proposed for this purpose.

METHODS

We developed and internally validated a clinical prediction model for lung cancer in a prospective cohort evaluated at our institution. Best statistical practices were used to construct, evaluate, and validate the logistic regression model in the presence of missing covariate data using bootstrap and optimism corrected techniques. The TREAT model was externally validated in a retrospectively collected Veteran Affairs population. The discrimination and calibration of the model was estimated and compared with the Mayo Clinic model in both the populations.

RESULTS

The TREAT model was developed in 492 patients from Vanderbilt whose lung cancer prevalence was 72% and validated among 226 Veteran Affairs patients with a lung cancer prevalence of 93%. In the development cohort, the area under the receiver operating curve (AUC) and Brier score were 0.87 (95% confidence interval [CI], 0.83-0.92) and 0.12, respectively compared with the AUC 0.89 (95% CI, 0.79-0.98) and Brier score 0.13 in the validation dataset. The TREAT model had significantly higher accuracy (p < 0.001) and better calibration than the Mayo Clinic model (AUC = 0.80; 95% CI, 75-85; Brier score = 0.17).

CONCLUSION

The validated TREAT model had better diagnostic accuracy than the Mayo Clinic model in preoperative assessment of suspicious lung lesions in a population being evaluated for lung resection.

Using Clinical Risk Models for Lung Nodule Classification

Evaluation and diagnosis of indeterminate pulmonary nodules is a significant and increasing burden on our health care system. The advent of lung cancer screening with low-dose computed tomography only exacerbates this problem, and more surgeons will be evaluating smaller and screening discovered nodules. Multiple calculators exist that can help the clinician diagnose lung cancer at the bedside. The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) model helps to determine who needs lung cancer screening, and the McWilliams and Mayo models help to guide the primary care clinician or pulmonologist with diagnosis by estimating the probability of cancer in patients with indeterminate pulmonary nodules. The Thoracic Research Evaluation And Treatment (TREAT) model assists surgeons to determine who needs a surgical biopsy among patients referred for suspicious lesions. Additional work is needed to develop decision support tools that will facilitate the use of these models in clinical practice, to complement the clinician's judgment and enhance shared decision making with the patient at the bedside.

Abstract 4153: Global African ancestry is not associated with lung cancer survival

Lung cancer is the leading cause of cancer-related mortality among men and women in the United States, accounting for 27% of all cancer-related deaths. Blacks experience poorer 5-year survival compared to whites. We hypothesized that individuals with higher global African ancestry have poorer survival compared to individuals with lower global African ancestry. We identified incident non-small cell lung cancer cases in the Southern Community Cohort Study (SCCS), a prospective study of low-income adults recruited from across the Southeast region of the United States. Individuals who donated a biospecimen were genotyped using the Illumina Human Exome BeadChip, which contains a panel of ancestry informative markers (AIMs). After standard quality control, 398 individuals (262 self-reported black and 134 self-reported white) remained for analysis. Global ancestry was estimated from 2,604 AIMs using the software program ADMIXTURE. Self-reported blacks had a median global African ancestry of 88.12%, while the median global African ancestry in self-reported whites was less than 0.01%. We estimated hazard ratios and 95% confidence intervals using Cox proportional hazard models adjusted for age, sex, body mass index (BMI), cigarettes per day, disease stage, treatment, insurance coverage, family history of lung cancer and recruitment site. BMI, age, cigarettes per day and global African ancestry were modeled using restricted cubic splines. We estimated time dependent area under the curve (AUC) for our main effects model and a main effects model with interactions, both with and without genetic ancestry. We found that the main effects model had an average AUC of 0.81. When global African ancestry was excluded, the AUC was minimally reduced to 0.80. When interactions were added to the main effects model, the AUC increased to 0.88. Removal of global ancestry from the interactions model reduced the AUC to 0.84. In the main effects model, the two most predictive variables were stage and treatment with X2 values of 36.13 (degrees of freedom, df=2) and 15.47 (df=4), respectively. While we conclude that global African ancestry has little effect on overall survival, a relationship between global African ancestry and stage or treatment remains to be investigated.

Citation Format: Carissa C. Iverson, Sarah Fletcher, Jeffery Blume, Holli Dilks, Heidi Chen, Stephen A. Deppen, William S. Bush, Dana C. Crawford, William J. Blot, Eric L. Grogan, Melinda C. Aldrich. Global African ancestry is not associated with lung cancer survival. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4153. doi:10.1158/1538-7445.AM2014-4153

Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis

IMPORTANCE

Positron emission tomography (PET) combined with fludeoxyglucose F 18 (FDG) is recommended for the noninvasive diagnosis of pulmonary nodules suspicious for lung cancer. In populations with endemic infectious lung disease, FDG-PET may not accurately identify malignant lesions.

OBJECTIVES

To estimate the diagnostic accuracy of FDG-PET for pulmonary nodules suspicious for lung cancer in regions where infectious lung disease is endemic and compare the test accuracy in regions where infectious lung disease is rare.

DATA SOURCES AND STUDY SELECTION

Databases of MEDLINE, EMBASE, and the Web of Science were searched from October 1, 2000, through April 28, 2014. Articles reporting information sufficient to calculate sensitivity and specificity of FDG-PET to diagnose lung cancer were included. Only studies that enrolled more than 10 participants with benign and malignant lesions were included. Database searches yielded 1923 articles, of which 257 were assessed for eligibility. Seventy studies were included in the analysis. Studies reported on a total of 8511 nodules; 5105 (60%) were malignant.

DATA EXTRACTION AND SYNTHESIS

Abstracts meeting eligibility criteria were collected by a research librarian and reviewed by 2 independent reviewers. Hierarchical summary receiver operating characteristic curves were constructed. A random-effects logistic regression model was used to summarize and assess the effect of endemic infectious lung disease on test performance.

MAIN OUTCOME AND MEASURES

The sensitivity and specificity for FDG-PET test performance.

RESULTS

Heterogeneity for sensitivity (I2 = 87%) and specificity (I2 = 82%) was observed across studies. The pooled (unadjusted) sensitivity was 89% (95% CI, 86%-91%) and specificity was 75% (95% CI, 71%-79%). There was a 16% lower average adjusted specificity in regions with endemic infectious lung disease (61% [95% CI, 49%-72%]) compared with nonendemic regions (77% [95% CI, 73%-80%]). Lower specificity was observed when the analysis was limited to rigorously conducted and well-controlled studies. In general, sensitivity did not change appreciably by endemic infection status, even after adjusting for relevant factors.

CONCLUSIONS AND RELEVANCE

The accuracy of FDG-PET for diagnosing lung nodules was extremely heterogeneous. Use of FDG-PET combined with computed tomography was less specific in diagnosing malignancy in populations with endemic infectious lung disease compared with nonendemic regions. These data do not support the use of FDG-PET to diagnose lung cancer in endemic regions unless an institution achieves test performance accuracy similar to that found in nonendemic regions.

Accuracy of fluorodeoxyglucose-positron emission tomography within the clinical practice of the American College of Surgeons Oncology Group Z4031 trial to diagnose clinical stage I non-small cell lung cancer

BACKGROUND

Fluorodeoxyglucose-positron emission tomography (FDG-PET) is recommended for diagnosis and staging of non-small cell lung cancer (NSCLC). Meta-analyses of FDG-PET diagnostic accuracy demonstrated sensitivity of 96% and specificity of 78% but were performed in select centers, introducing potential bias. This study evaluates the accuracy of FDG-PET to diagnose NSCLC and examines differences across enrolling sites in the national American College of Surgeons Oncology Group (ACOSOG) Z4031 trial.

METHODS

Between 2004 and 2006, 959 eligible patients with clinical stage I (cT1-2 N0 M0) known or suspected NSCLC were enrolled in the Z4031 trial, and with a baseline FDG-PET available for 682. Final diagnosis was determined by pathologic examination. FDG-PET avidity was categorized into avid or not avid by radiologist description or reported maximum standard uptake value. FDG-PET diagnostic accuracy was calculated for the entire cohort. Accuracy differences based on preoperative size and by enrolling site were examined.

RESULTS

Preoperative FDG-PET results were available for 682 participants enrolled at 51 sites in 39 cities. Lung cancer prevalence was 83%. FDG-PET sensitivity was 82% (95% confidence interval, 79 to 85) and specificity was 31% (95% confidence interval, 23% to 40%). Positive and negative predictive values were 85% and 26%, respectively. Accuracy improved with lesion size. Of 80 false-positive scans, 69% were granulomas. False-negative scans occurred in 101 patients, with adenocarcinoma being the most frequent (64%), and 11 were 10 mm or less. The sensitivity varied from 68% to 91% (p=0.03), and the specificity ranged from 15% to 44% (p=0.72) across cities with more than 25 participants.

CONCLUSIONS

In a national surgical population with clinical stage I NSCLC, FDG-PET to diagnose lung cancer performed poorly compared with published studies.

Abstract 3634: A new model improves lung cancer prediction in the preoperative evaluation of patients with suspicious lung lesions

Lung cancer predictive models, such as the Mayo model (Swensen, 1997), that are used for screening and referral to surgery are based on only traditional variables and are outdated. Clinical information such as FEV1, FDG-PET and lesion growth increasingly are used in treatment decisions. This information needs to be incorporated into our clinical prediction models. We developed a lung cancer risk model utilizing the more extensive clinical information available at the point of a decision for surgery that predicts cancer better than existing models.

We evaluated a lung cancer prediction model using multivariable logistic regression in a population being evaluated for lung surgery at a single academic institution in an IRB approved study. The model included non-linear relationships between continuous variables and lung cancer and used multiple imputation to handle missing data. Internal validation was conducted using the bootstrap method with 500 iterations. Area under the curve (AUC) and Brier score were calculated for the training model and internal bootstrap validation model. The characteristics of the new model were compared to the Mayo model.

492 individuals were recruited at who had been evaluated for surgery with known or suspected lung cancer. Lung cancer prevalence was 72%. Diagnosis was determined pathologically (92%) or by greater than 18 months of followup among those who didn't undergo surgery. Missing data occurred with FDG-PET scan (22%), growth on serial CT scans (13%), predicted FEV1 (10%) and pre-operative disease symptoms (7%). The remaining variables of interest had less than 5% missing data. Age (OR 1.05; 95%CI: 1.03-1.08), pack years (OR 1.03; 95%CI: 1.00-1.05), pre-operative lesion maximum diameter (OR 1.06; 95%CI: 1.04-1.08), lesion growth (OR 2.92 95%CI: 1.10-5.65), previous cancer (OR 1.86 95%CI: 1.05-3.32) and FDG-PET avidity (OR 4.56 95%CI: 2.17-9.57) predicted lung cancer (p&lt;0.05). AUC for the initial model was 0.87 (95%CI: 0.84 - 0.91) and Brier score was 0.12. Bootstrap sampling estimated AUC of 0.85 and Brier score of 0.13 demonstrating internal validity of the model. The Mayo model was evaluated for those with complete data (93%) and had an AUC of 0.80 (95%CI: 0.75 - 0.85) which was significantly less (P&lt;0.001) than that observed for the new model. The Mayo model generally underestimated risk and its Brier score was 0.17, showing poorer calibration than the new model.

Our internally validated model performed better in distinguishing low risk from high risk patients than the Mayo model in a surgical population being evaluated for lung cancer. The Mayo model, with its more limited clinical information, underestimated risk. Future work will validate this model in an external dataset and prospectively evaluate the impact of the model on patient safety and resource utilization.

Citation Format: Stephen A. Deppen, Melinda C. Aldrich, Ronald Walker, Catherine A. Necessary, Chiu-lan Chen, Huiyun Wu, Jeffery Blume, Pierre P. Massion, Theodore Speroff, Robert S. Dittus, Bill J. Putnam, Eric L. Grogan. A new model improves lung cancer prediction in the preoperative evaluation of patients with suspicious lung lesions. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3634. doi:10.1158/1538-7445.AM2013-3634

Abstract LB-296: FDG-PET avidity negatively impacts survival in pStage I NSCLC in the ACOSOG Z4031 trial

Background: Fluoro-deoxyglucose positron emission tomography (FDG-PET) scans are used for diagnosis and staging of known or suspected non-small cell lung cancer (NSCLC). Single institution studies examining the impact of avidity on survival have reported mixed results. The purpose of this study is to evaluate the association between FDG-PET avidity and survival in the national prospective ACOSOG Z4031 trial in patients with pathological Stage I NSCLC.

Methods: Between 2004 and 2006, 1074 patients with known or suspected clinical stage I (cT1-2N0M0) NSCLC were enrolled in the ACOSOG Z4031 trial and underwent surgical resection. FDG-PET results were abstracted from radiology interpretations included in the case report forms. FDG-PET avidity was categorized based on either radiologist description or reported maximum standard uptake value (SUV). The four categories were: 1) not avid and not cancerous (SUV=0), 2) low avidity and likely not cancerous (SUV&gt;0 and &lt;2.5), 3) avid and probably cancerous (SUV≥2.5 and &lt;5) and 4) highly avid and likely cancerous (SUV≥5). The lesion was classified as avid if in categories 3 or 4. The final diagnosis was determined by pathological examination and all cause mortality was reported. Cox proportional hazard regression was used to assess the impact of FDG-PET avidity on survival. The covariates used in the model included pStage, gender, age, race, and preoperative lesion size. Kaplan-Meier survival curves were calculated and the log-rank test was used to determine differences in survival based on FDG-PET avidity.

Results: There were 51 enrolling sites in 39 cities with 969 eligible participants. Preoperative FDG-PET results were available for 540 participants with NSCLC and 81% had FDG-PET avid or highly avid lesions. 400 patients had pStage I NSCLC and the 5 year survival was 70% with 95%CI (65%, 75%). FDG-PET avidity, male gender, age, and lesion size negatively impacted survival. FDG-PET avidity in pStage I disease still negatively impacted survival (p=0.03) when controlling for lesion size. The 5 year survival for Stage I disease was 80% with 95%CI (68%, 88%) in FDG-PET negative patients and 67% with 95% CI (61%, 72%) in FDG-PET positive patients (p=0.02).

Conclusions: In a national surgical population with pathological stage I NSCLC, FDG-PET avidity negatively impacted five year survival, independently of lesion size. Further work should be done to determine if chemotherapy would be beneficial in patients with PET avid lesions and pStage I NSCLC.

Citation Format: Eric L. Grogan, Stephen A. Deppen, Heidi Chen, Karla V. Ballman, Francys C. Verdial, Melinda C. Aldrich, Paul A. Decker, David H. Harpole, Robert J. Cerfolio, Robert J. Keenan, David R. Jones, Thomas A. D'Amico, Joseph B. Shrager, Bryan F. Meyers, Joe B. Putnam. FDG-PET avidity negatively impacts survival in pStage I NSCLC in the ACOSOG Z4031 trial. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-296. doi:10.1158/1538-7445.AM2013-LB-296

Abstract 3628: Benign disease prevalence after surgical lung resection varies geographically in the US Medicare population, implications for lung cancer screening

Purpose: The National Lung Screening Trial (NLST) demonstrated that screening computed tomography (CT) reduces mortality caused by lung cancer and clinical societies and patient advocacy groups have recommended screening high risk individuals. Screening CT scan of the chest reduced lung cancer mortality in this trial but 96% of anomalies were false positives, 24% of lung surgeries resulted in a benign diagnosis. The overall mortality rate from procedures was 1.2% in the trail. The benign disease prevalence after surgical biopsy is not known at the state level and if these prevalences differ across the United States, then a nationwide screening regimen may have geographically varying success. The purpose of this study is to determine the benign disease point prevalence after surgical lung resection at the state level using a national dataset.

Methods: We examined the point prevalence of benign disease after lung surgery in a retrospective cohort being evaluated for known or suspected lung cancer. The MEDPAR Hospital National Limited Data Set from 2009 was used to identify patients who had undergone lung surgery by ICD-9CM codes. Patients less than 19 years old or who had diseases not arising from a lung lesion were excluded. Malignancy and benign disease were determined by ICD-9CM codes. The benign diagnosis prevalence was estimated at the state level by dividing the total number of benign cases by the sum of the benign cases and malignant cases. Benign disease point prevalence was compared between states using Pearson chi-square test.

Results: There were 25,362 patients who had a lung operation for known or suspected lung cancer. Among these, 2,312 (9.1%) had a benign diagnosis. Benign diagnosis was more frequent among women (9.8%) than men (8.5%) after surgery. Crude in hospital mortality rate for all patients was 2.3%. The mortality rate for patients with benign disease was 2.1%. Prevalence of benign disease varied significantly (chi-sq p&lt;0.001) across states from a low of 1.3% in Vermont to a high of 25.0% in Hawaii. Median benign disease by state was 8.8% (IQR: 7.8 - 10.9).

Conclusion: Benign disease prevalence after lung surgery varies widely by state and resulted in a mortality rate of 2.1 percent. Cause of observed differences is not known but may be due to practice variation or locally endemic lung diseases and should be investigated to determine the impact to a national lung cancer screening program.

Citation Format: Stephen A. Deppen, Sharon Phillips, Melissa McPheeters, Melinda C. Aldrich, Jeffery Blume, David F. Penson, Yu Shyr, Eric L. Grogan. Benign disease prevalence after surgical lung resection varies geographically in the US Medicare population, implications for lung cancer screening. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3628. doi:10.1158/1538-7445.AM2013-3628

Obesity increases operating room time for lobectomy in the society of thoracic surgeons database

BACKGROUND

Obesity has become a major epidemic in the United States. Although research suggests obesity does not increase major morbidity or mortality after thoracic operations, it likely results in greater use of health care resources.

METHODS

We examined all patients in The Society of Thoracic Surgeons General Thoracic Surgery database with primary lung cancer who underwent lobectomy from 2006 to 2010. We investigated the impact of body mass index (BMI) on total operating room time using a linear mixed-effects regression model and multiple imputations to account for missing data. Secondary outcomes included postoperative length of stay and 30-day mortality. Covariates included age, sex, race, forced expiratory volume, smoking status, Zubrod score, prior chemotherapy or radiation, steroid use, number of comorbidities, surgical approach, hospital lobectomy volume, hospital percent obesity, and the addition of mediastinoscopy or wedge resection.

RESULTS

A total of 19,337 patients were included. The mean BMI was 27.3 kg/m2, with 4,898 patients (25.3%) having a BMI of 30 kg/m2 or greater. The mean total operating room time, length of stay, and 30-day mortality were 240 minutes, 6.7 days, and 1.8%, respectively. For every 10-unit increase in BMI, mean operating room time increased by 7.2 minutes (range, 4.8 to 8.4 minutes; p<0.0001). Higher hospital lobectomy volume and hospital percentage of obese patients did not affect the association between BMI and operative time. Body mass index was not associated with 30-day mortality or increased length of stay.

CONCLUSIONS

Increased BMI is associated with increased total operating room time, regardless of institutional experience with obese patients.

Poor survival for veterans with pathologic stage I non-small-cell lung cancer

BACKGROUND

Pathologic stage (pStage) IA and IB non-small-cell lung cancer (NSCLC) has a median survival time of 119 and 81 months, respectively. We describe the outcomes of veterans with pStage I NSCLC.

METHODS

A retrospective review of 78 patients with pStage I NSCLC who underwent cancer resection was performed at the Tennessee Valley Veterans Affairs Hospital between 2005 and 2010. All-cause 30-day, 90-day, and overall mortality were determined. Survival was assessed with the Kaplan-Meier and Cox proportional hazards methods.

RESULTS

There were 55 (71%) pStage IA and 23 (29%) IB patients. Thirty- and 90-day mortality was 3.8% (3 of 78) and 6.4% (5 of 78), respectively. Median survival was 59 and 28 months for pStage 1A and 1B, respectively. Postoperative events were associated with impaired survival on multivariable analysis (hazard ratio, 1.26, P = .03).

CONCLUSIONS

Veterans with pStage I NSCLC at our institution have poorer survival than the general population. More research is needed to determine the etiology of this disparity.

Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial

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Background: Fluoro-deoxyglucose positron emission tomography (FDG-PET) is recommended for diagnosis and staging of known or suspected NSCLC. Meta-analyses examining the accuracy of FDG-PET to diagnose lung cancer demonstrated high sensitivity 94% and specificity 83% but were performed in select centers. The purpose of this study is to evaluate the accuracy of FDG-PET to diagnose NSCLC and examine enrolling site differences in the national prospective ACOSOG Z4031 trial. Methods: 1,074 patients with clinical stage I (cT1-2N0M0) known or suspected NSCLC were enrolled between 2004 and 2006 in the Z4031 study and underwent surgical resection. The final diagnosis was determined by pathological examination. FDG-PET results were abstracted from radiology interpretations included in the case report forms. FDG-PET avidity was categorized based on either radiologist description or reported maximum standard uptake value (SUV). The four categories were: not avid and not cancerous (SUV=0), low avidity and likely not cancerous (SUV>0 and <2.5), avid and probably cancerous (SUV>2.5 and <5) and highly avid and likely cancerous (SUV>5). Sensitivity analysis of FDG-PET diagnostic accuracy was performed for varying levels of avidity and by preoperative lesion size. Differences in accuracy by enrolling site were examined. Results: There were 51 enrolling sites in 39 cities with 969 eligible participants. Preoperative FDG-PET results were available for 682 participants. Lung cancer prevalence was 83%. FDG-PET sensitivity was 82% (95% CI: 79-85), and specificity was 31% (95% CI: 23-40). Positive and negative predictive values were 85% and 26%, respectively and accuracy improved with lesion size. There were 80 false positive scans and 69% were granulomas. False negative scans occurred in 101 patients (11were ≤10mm) with adenocarcinoma, squamous, bronchoalveolar cell and neuroendocrine tumors responsible for 64%, 12%, 10% and 8% of false negative results, respectively. Specificity did not differ between the 8 sites with >25 patients (p=0.74). Conclusions: In a national surgical population with clinical stage I NSCLC, FDG-PET to diagnose lung cancer performed poorly compared to published studies. Reasons for poor test performance should be explored.