Lung cancer is the leading cause of cancer-related death due in large part to our inability to diagnose it at an early and potentially curable stage. Screening for lung cancer via low dose computed tomographic (LDCT) imaging has been demonstrated to improve mortality but also results in a high rate of false positive tests. The identification and application of non-invasive molecular biomarkers that improve the performance of CT imaging for the detection of lung cancer in high risk individuals would aid in clinical decision-making, eliminate the need for unnecessary LDCT follow-up, and further refine the screening criteria for an already large high-risk population. The Detection of Early Lung Cancer Among Military Personnel (DECAMP) consortium is conducting two multicenter prospective studies with the goals of developing an integrated panel of both airway and blood-based molecular biomarkers that discriminate benign and malignant indeterminate nodules detected on CT scan as well as predict the future development of lung cancer in high-risk individuals. To achieve these goals, DECAMP is compiling an extensive array of biospecimens including nasal brushings, serum, plasma and intrathoracic airway samples (bronchial brushings and bronchial biopsies) from normal-appearing airway epithelium. This bank of samples is the foundation for multiple DECAMP efforts focused on the identification of those at greatest risk of developing lung cancer as well as the discrimination of benign and malignant pulmonary nodules. The clinical, imaging and biospecimen repositories will serve as a resource for the biomedical community and their investigation of the molecular basis of chronic respiratory disease. Retrospectively registered as NCT01785342 - DECAMP-1: Diagnosis and Surveillance of Indeterminate Pulmonary Nodules (DECAMP-1). Date of Registration: February 7, 2013. Retrospectively registered as NCT02504697 - DECAMP-2: Screening of Patients With Early Stage Lung Cancer or at High Risk for Developing Lung Cancer (DECAMP-2). Date of Registration: July 22, 2015.

In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm²) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test–retest scans. Centralized tumor ADC and perfusion fraction (f_p) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60–0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75–0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm²) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9–5.2% versus 5.4%). The alternate metrics ADC_fast (b ≤ 100 s/mm²), ADC_slow (b ≥ 100 s/mm²), and f_p did not improve predictive performance (AUCs = 0.54–0.60, p = 0.08–0.81), and ADC_fast and f_p demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.

A simple assessment of lung nodule location for reduction in unnecessary invasive procedures C. Matthew Kinsey, Ehab Billatos, Vitor Mori, Ben Tonelli, Bernard F. Cole, Fenghai Duan, Helga Marques, Isaac de la Bruere, Jorge Onieva, Rubén San José Estépar, Alyx Cleveland, Dan Idelkope, Chris Stevenson, Jason H. T. Bates, Denise Aberle, Avi Spira, George Washko, Raúl San José Estépar