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Background Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. Methods In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. Results In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). Conclusions The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Background Bronchoscopy is commonly used to evaluate suspicious lung lesions. The yield is likely dependent on patient, radiographic, and bronchoscopic factors. Few studies have assessed these factors simultaneously while also including the preprocedure physician-assessed probability of cancer (pCA) when assessing yield. Methods This study is a secondary data analysis from a prospective multicenter trial. Diagnostic yield of standard bronchoscopy with biopsy ± fluoroscopy, endobronchial ultrasound with transbronchial needle aspiration (EBUS-TBNA), electromagnetic navigation, and combination bronchoscopies was assessed. Definitions for diagnostic and nondiagnostic bronchoscopies were rigorously predefined. The association of diagnostic yield with individual variables was examined by using univariate and multivariate logistic regression analyses where appropriate. Results A total of 687 patients were included from 28 sites. Overall diagnostic yield was 69%; 80% for EBUS, 55% for bronchoscopy with biopsy ± fluoroscopy, 57% for electromagnetic navigation, and 74% for combination procedures (P 60% yielded a positive result in 77% (P Conclusions This study is the first to assess the yield of bronchoscopy according to physician-assessed pCA in a large, prospective multicenter trial. The yield of bronchoscopy varied greatly according to physician suspicion that cancer is present, the patients’ clinical/radiographic features, and the type of procedure performed. Of the procedures performed, EBUS-TBNA was the most likely to provide a diagnosis.

Rationale: Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A "molecular diagnosis of UIP" in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP.Objectives: We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern.Methods: Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists' reports was compared with histopathology.Measurements and Main Results: In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6-98.3%) and a sensitivity of 60.3% (CI, 46.6-73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5-48.3%) and a specificity of 96.9% (CI, 83.8-100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%).Conclusions: In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

Background The Percepta genomic classifier has been clinically validated as a complement to bronchoscopy for lung nodule evaluation. Research Question The goal of this study was to examine the impact on clinical management decisions of the Percepta result in patients with low- and intermediate-risk lung nodules. Study Design and Methods A prospective “real world” registry was instituted across 35 US centers to observe physician management of pulmonary nodules following a nondiagnostic bronchoscopy. To assess the impact on management decisions of the Percepta genomic classifier, a subset of patients was analyzed who had an inconclusive bronchoscopy for a pulmonary nodule, a Percepta result, and an adjudicated lung diagnosis with at least 1 year of follow-up. In this cohort, change in the decision to pursue additional invasive procedures following Percepta results was assessed. Results A total of 283 patients met the study eligibility criteria. In patients with a low/intermediate risk of malignancy for whom the clinician had designated a plan for a subsequent invasive procedure, a negative Percepta result down-classified the risk of malignancy in 34.3% of cases. Of these down-classified patients, 73.9% had a change in their management plan from an invasive procedure to surveillance, and the majority avoided a procedure up to 12 months following the initial evaluation. In patients with confirmed lung cancers, the time to diagnosis was not significantly delayed when comparing Percepta down-classified patients vs patients who were not down-classified (P = .58). Interpretation The down-classification of nodule malignancy risk with the Percepta test decreased additional invasive procedures without a delay in time to diagnosis among those with lung cancer.

Background Deciding when to biopsy a man with non-suspicious DRE findings and tPSA in the 4–10 ng/ml range can be challenging, because two-thirds of such biopsies are typically found to be benign. The Prostate Health Index (phi) exhibits significantly improved diagnostic accuracy for prostate cancer detection when compared to tPSA and %fPSA, however only one published study to date has investigated its impact on biopsy decisions in clinical practice. Methods An IRB approved observational study was conducted at four large urology group practices using a physician reported two-part questionnaire. Physician recommendations were recorded before and after receiving the phi test result. A historical control group was queried from each site's electronic medical records for eligible men who were seen by the same participating urologists prior to the implementation of the phi test in their practice. 506 men receiving a phi test were prospectively enrolled and 683 men were identified for the historical control group (without phi). Biopsy and pathological findings were also recorded for both groups. Results Men receiving a phi test showed a significant reduction in biopsy procedures performed when compared to the historical control group (36.4% vs. 60.3%, respectively, P

8551 Background: The goal of lung nodule management is to make an early diagnosis in patients with lung cancer while avoiding unnecessary, costly and potentially harmful procedures in patients with benign lesions. Increased implementation of low dose CT screening will lead to increased numbers of both benign and malignant nodules that will require effective management. We have previously described the feasibility of detecting gene expression changes associated with lung cancer (“field of injury”) in nasal epithelium utilizing whole transcriptome RNA sequencing from non-invasive nasal brush samples. Using this approach, we now report the performance of candidate nasal classifiers that combine both genomic and clinical features to risk stratify lung nodules from ever-smokers to aid in the diagnosis of lung cancer. Methods: Patients from several clinical cohorts who were ever-smokers with a lung nodule < 30 mm and without a history of prior cancer underwent nasal epithelium sampling. All patients had at least one year of follow up or until a final diagnosis of benign or malignant nodule was made. Candidate classifiers were developed using whole-transcriptome RNA sequencing and machine learning. Training of these classifiers included genomic and clinical information (age, sex, pack-years, years-since-quit, nodule size and nodule spiculation). Two decision boundaries were chosen to maximize sensitivity and specificity for low and high-risk nodules, respectively. The performance of these classifiers was evaluated using cross-validation (CV). Results: All candidate nasal classifiers underwent CV assessment on over 700 patients with lung nodules. All candidate classifiers achieved CV performance of > 40% specificity at 95% sensitivity in low- risk nodules and all candidate classifiers achieved CV performance of > 60% sensitivity at 90% specificity in high-risk nodules. The classifiers stratified benign nodules as low- risk with > 95% negative predictive value (NPV), intermediate risk nodules were stratified as 5-65% risk and malignant nodules were stratified as high- risk with > 65% positive predictive value (PPV) in a population with estimated 25% prevalence. This performance was robust across subgroups of age ( < 65 year vs. >65 year), sex, and current versus former smokers. Conclusions: The nasal genomic-clinical candidate classifiers have a high NPV effectively identifying benign nodules when calling them low- risk, thereby, informing decisions to more safely avoid a diagnostic workup. Additionally, the high PPV of these classifiers identifies malignant nodules when calling them high- risk and informs decisions regarding the urgency of a further diagnostic work-up. A nasal genomic-clinical classifier has the potential to serve as a non-invasive tool for lung cancer risk-stratification to help inform decision making in patients with lung nodules.

8549 Background: Current guidelines recommend that patients who have lung nodules with high risk of malignancy (ROM) ( > 65%) should undergo surgical and other ablative therapies. However, prior studies have shown that clinicians may opt for more conservative management in these high-risk patients. Percepta Genomic Sequencing Classifier (GSC), a RNA-seq based classifier derived from bronchial epithelial cells to assess risk of lung cancer, was designed to risk stratify lung nodules by both down classifying ROM as a “rule -out“ test with high sensitivity as well as up-classifying ROM as a “rule- in” test with high specificity for malignancy. This study assesses the impact of up-classification of high ROM to very high- risk (ROM > 90%) by Percepta GSC in increasing the number of ablative therapies recommended for high-risk lung nodules. Methods: This prospective randomized decision impact survey included 37 patients from the AEGIS I/ II cohorts and the Percepta Registry who were undergoing work up of a lung nodule and had a high ROM that was up-classified to very high ROM by Percepta GSC. 97 physicians assessed 10 randomly assigned patient cases. They then responded to a survey designed to test the hypothesis that including a Percepta GSC result will increase the recommendation for surgical or other ablative therapy in very high- risk patients as well as their level of confidence of this recommendation. Physicians were first presented with the patient’s clinical information without Percepta GSC and then with Percepta GSC. Results: 97 physicians provided a total of 682 evaluations of 37 patients. In this study, the recommendation for surgical or other ablative therapy increased from 19/341 (5.6%) prior to the Percepta GSC result to 157/341 (46%) after the Percepta GSC result (odds ratio of 4.76, p-value < 0.001). The number of extremely confident recommendations increased from 72/341 (21%) without Percepta GSC to 106/341 (31%) with Percepta GSC. Significantly more physicians had increased confidence in their recommended next step post-Percepta GSC when collapsing the confidence level responses into increased confidence (n = 93) and decreased confidence (n = 44) (p-value = 0.002). Conclusions: Percepta GSC had a quantifiable impact on clinical decision making. It increased the number of surgical and other ablative therapies recommended when patients were re-classified from high to very high- risk of lung cancer with a higher confidence in the recommended next step. By up-classifying nodules from high to very high ROM, Percepta GSC will improve the likelihood and timeliness of appropriate therapies and assist clinicians more effectively manage patients to improve patient outcomes.

BACKGROUND: AMH is widely used for assessing ovarian reserve, and it is particularly convenient, because it is thought to have minimal variability throughout the menstrual cycle. However, studies assessing the stability of AMH over the menstrual cycle have been conflicting. PURPOSE: The purpose of this study is to determine whether AMH levels vary across the normal menstrual cycle. DESIGN: A multi-center, prospective cohort study conducted at three US centers. METHODS: Fifty females with regular menstrual cycles aged 18–45 underwent serial venipuncture every 3–5 days starting in the early follicular phase and lasting up to 10 collections. AMH was tested using the Access 2 immunoassay system. RESULTS: Age-adjusted mixed-effect models utilizing data from 384 samples from 50 subjects demonstrated a within subject standard deviation of 0.81 (95% CI 0.75–0.88) with a coefficient of variation of 23.8% across the menstrual cycle and between subject standard deviation of 2.56 (95% CI 2.13–3.21) with a coefficient of variation of 75.1%. Intra-class correlation (ICC) of AMH across the menstrual cycle was 0.91. CONCLUSION: Overall, AMH levels, using the automated Access AMH assay, appear to be relatively stable across the menstrual cycle. Fluctuations, if any, appear to be small, and therefore, clinicians may advise patients to have AMH levels drawn at any time in the cycle.