Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Foundation for the National Institutes of Health ͑FNIH͒, and accompanied by the Food and Drug Administration ͑FDA͒ through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ͑"noduleՆ 3 mm," "noduleϽ 3 mm," and "non-noduleՆ 3 mm"͒. In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results:The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleՆ 3 mm" by at least one radiologist, of which 928 ͑34.7%͒ received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions:The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.

PURPOSE To provide evidence-based recommendations to practicing clinicians on radiographic imaging and biomarker surveillance strategies after definitive curative-intent therapy in patients with stage I-III non–small-cell lung cancer (NSCLC) and SCLC. METHODS ASCO convened an Expert Panel of medical oncology, thoracic surgery, radiation oncology, pulmonary, radiology, primary care, and advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2000 through 2019. Outcomes of interest included survival, disease-free or recurrence-free survival, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. RESULTS The literature search identified 14 relevant studies to inform the evidence base for this guideline. RECOMMENDATIONS Patients should undergo surveillance imaging for recurrence every 6 months for 2 years and then annually for detection of new primary lung cancers. Chest computed tomography imaging is the optimal imaging modality for surveillance. Fluorodeoxyglucose positron emission tomography/computed tomography imaging should not be used as a surveillance tool. Surveillance imaging may not be offered to patients who are clinically unsuitable for or unwilling to accept further treatment. Age should not preclude surveillance imaging. Circulating biomarkers should not be used as a surveillance strategy for detection of recurrence. Brain magnetic resonance imaging should not be used for routine surveillance in stage I-III NSCLC but may be used every 3 months for the first year and every 6 months for the second year in patients with stage I-III small-cell lung cancer who have undergone curative-intent treatment.

There is an unmet need for pharmacodynamic and predictive biomarkers for antiangiogenic agents. Recent studies have shown that soluble vascular endothelial growth factor receptor 2 (sVEGFR2), VEGF, and several other soluble factors may be modulated by VEGF pathway inhibitors. We conducted a broad profiling of cytokine and angiogenic factors (CAF) to investigate the relationship between baseline CAF levels, CAF changes during treatment, and tumor shrinkage in early-stage non–small cell lung cancer (NSCLC) patients treated with pazopanib, an oral angiogenesis inhibitor targeting VEGFR, platelet-derived growth factor receptor, and c-kit. Plasma samples were collected before treatment and on the last day of therapy from 33 patients with early-stage NSCLC participating in a single-arm phase II trial. Levels of 31 CAFs were measured by suspension bead multiplex assays or ELISA and correlated with change in tumor volume. Pazopanib therapy was associated with significant changes of eight CAFs; sVEGFR2 showed the largest decrease, whereas placental growth factor underwent the largest increase. Increases were also observed in stromal cell–derived factor-1α, IP-10, cutaneous T-cell–attracting chemokine, monokine induced by IFN-γ, tumor necrosis factor–related apoptosis-inducing ligand, and IFN-α. Posttreatment changes in plasma sVEGFR2 and interleukin (IL)-4 significantly correlated with tumor shrinkage. Baseline levels of 11 CAFs significantly correlated with tumor shrinkage, with IL-12 showing the strongest association. Using multivariate classification, a baseline CAF signature consisting of hepatocyte growth factor and IL-12 was associated with tumor response to pazopanib and identified responding patients with 81% accuracy. These data suggest that CAF profiling may be useful for identifying patients likely to benefit from pazopanib, and merit further investigation in clinical trials.