Chen, Heidi

7008 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.

Abstract This study assessed the inter-user reproducibility of Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel analytical tool that risk stratifies lung adenocarcinomas (ADCs) according to defined computed tomography (CT) structural characteristics. CANARY detects nine distinct voxel signatures in ADCs based on standard CT imaging, thereby defining nodule characteristics correlating with patient outcomes, and corresponding to invasion or lepidic growth histologically. A software user segments the borders of each ADC prior to voxel analysis, introducing potential variability into the assessment. While CANARY is a promising method, it requires validation of the analytical variability between users and prediction of accuracy in an independent cohort. To evaluate the reproducibility of CANARY analysis, three independent users who are not part of the CANARY development team segmented and analyzed 50 biopsy-confirmed primary lung ADCs from Vanderbilt University Medical Center. The CT scans of ADCs were selected retrospectively based on the following criteria: ADC-histology proven on biopsy, diagnosed between 2009-2015, less than 3cm in greatest diameter, and stages IA-IV. Users followed a standard operating procedure established at the Mayo Clinic, and were blinded to clinical characteristics and patient outcomes. Results: To measure inter-user variability of ADC voxel composition, the intraclass correlation coefficient (ICC) was calculated based upon the percentage of each voxel subtype within the whole ADC. An ICC of 1 reflects high reproducibility between users. Amongst all 50 ADCs, the average ICC for all nine voxel types was 0.828 (95% CI 0.76, 0.895). The ICC of the four voxel types associated with invasive features on CT was 0.865 (95% CI 0.805, 0.924). ICCs were also calculated using a logarithmic transformation for data normalization, generating an ICC of 0.745 (95% CI 0.663, 0.826) for all nine voxel types, and an ICC of 0.995 (95% CI 0.993, 0.997) for the four voxel types associated with invasion. Conclusions: (1) CANARY analysis of lung ADC voxel signatures appears to be reproducible amongst users, making it a reliable tool for the evaluation of ADC voxel density. (2) Correlation of invasive signatures associated with more aggressive ADCs was nearly perfect amongst users. Additional validation metrics for CANARY with larger datasets are being evaluated, including the accuracy of tumor prognostic predictions between users and analysis of ADC datasets from other institutions. Citation Format: Erica C. Nakajima, Michael P. Frankland, Tucker Johnson, Sanja L. Antic, Ronald A. Karwoski, Bennett Landman, Heidi Chen, Ronald C. Walker, Brian J. Bartholmai, Tobias Peikert, Srinivasan Rajagopalan, Pierre P. Massion, Fabien Maldonado. Assessing the reproducibility of computer-aided nodule assessment and risk yield (CANARY) method to characterize lung adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3723. doi:10.1158/1538-7445.AM2017-3723