Integrated care for resected early stage lung cancer: innovations and exploring patient needs

There is no consensus as to the duration and nature of follow-up following surgical resection with curative intent of lung cancer. The integration of cancer follow-up into primary care is likely to be a key future area for quality and cost-effective cancer care. Evidence from other solid cancer types demonstrates that such follow-up has no adverse outcomes, similar health-related quality of life, high patient satisfaction rates at a lower cost to the healthcare system. Core elements for successful models of shared cancer care are required: clear roles and responsibilities, timely effective communication, guidance on follow-up protocols and common treatments and rapid routes to (re)access specialist care. There is thus a need for improved communication between hospital specialists and primary care. Unmet needs for patients with early stage lung cancer are likely to include psychological symptoms and carer stress; the importance of smoking cessation may frequently be overlooked or underappreciated in the current hospital-based follow-up system. There is therefore a need for quality randomised controlled trials of patients with resected early stage lung cancer to establish optimal protocols for primary care-based follow-up and to more adequately address patients' and carers' unmet psychosocial needs, including the crucial role of smoking cessation

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation.

PurposeTo improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care.MethodsELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC's hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk.ResultsThe goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools.ConclusionThis initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide

The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of CT lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open source, cloud-based, globally distributed, screening CT imaging dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be utilized for clinically relevant AI research.MethodsIn this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.ResultsA total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets

Progression of Airway Dysplasia and C-Reactive Protein in Smokers at High Risk of Lung Cancer

Rationale: Chronic inflammation has been implicated in the development of airway dysplasia and lung cancer. It is unclear whether circulating biomarkers of inflammation could be used to predict progression of airway dysplasia

Automated Sputum Cytometry for Detection of Intraepithelial Neoplasias in the Lung

Background: Despite the benefits of early lung cancer detection, no effective strategy for early screening and treatment exists, partly due to a lack of effective surrogate biomarkers. Our novel sputum biomarker, the Combined Score (CS), uses automated image cytometric analysis of ploidy and nuclear morphology to detect subtle intraepithelial changes that often precede lung tumours