Lung Screening Benefits and Challenges: A Review of The Data and Outline for Implementation

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for almost a fifth of all cancer-related deaths. Annual computed tomographic lung cancer screening (CTLS) detects lung cancer at earlier stages and reduces lung cancer-related mortality among high-risk individuals. Many medical organizations, including the U.S. Preventive Services Task Force, recommend annual CTLS in high-risk populations. However, fewer than 5% of individuals worldwide at high risk for lung cancer have undergone screening. In large part, this is owing to delayed implementation of CTLS in many countries throughout the world. Factors contributing to low uptake in countries with longstanding CTLS endorsement, such as the United States, include lack of patient and clinician awareness of current recommendations in favor of CTLS and clinician concerns about CTLS-related radiation exposure, false-positive results, overdiagnosis, and cost. This review of the literature serves to address these concerns by evaluating the potential risks and benefits of CTLS. Review of key components of a lung screening program, along with an updated shared decision aid, provides guidance for program development and optimization. Review of studies evaluating the population considered "high-risk" is included as this may affect future guidelines within the United States and other countries considering lung screening implementation

A Retrospective Study Assessing the Predictive Performance of a Lung Cancer Screening Risk Prediction Model in a Clinical Lung Cancer Screening Program

Background: United States Preventive Services Task Force (USPSTF) and Centers for Medicare & Medicaid Services (CMS) recommendations for annual screening for lung cancer with low dose CT (LDCT) scans rely on age and smoking history to identify those at high risk for lung cancer. The Tammemagi et al. six year lung cancer risk prediction model, PLCOm2012, developed and validated in large lung cancer screening clinical trials, demonstrated good predictive performance in screening selection. However, the model has not been validated in clinical practice. Validating the model in clinical practice would increase confidence in its ability to provide information for shared decision making discussions in the near term and would potentially allow for selection of other high risk groups, not currently recommended to be screened, in the future. Methods: Retrospective evaluation of the predictive performance of the Tammemagi et al. six year lung cancer risk prediction model in the Lahey Hospital & Medical Center, Lahey physician referred patients enrolled in the lung cancer screening program between January 1, 2012 and November 30, 2015 (n=2302). Predictor variable data were gathered from the program clinical data base and program participant clinic medical records. All patients met the National Comprehensive Cancer Network (NCCN) Lung Cancer Screening Guidelines Group 1 or Group 2 high-risk criteria. Results: The model six year mean risk for lung cancer was higher for participants with lung cancer, 4.56%, as compared to those without lung cancer, 3.55% (p=0.0265). Area under the curve (AUC) of the receiver operator characteristics (ROC) was 0.63 (95% CI 0.57 – 0.69). The mean absolute difference between observed and predicted risk was 0.013 or less for the first 9 deciles.At the 1.51% predicted risk recommended screening threshold; sensitivity = 85.7%, specificity = 29.7%, and PPV = 3.7%. In sub-group analysis, for NCCN Group 2 (younger, lighter smoking history, no limit on time quit and one additional risk factor) the mean predicted risk for participants with lung cancer was 2.39% as compared to 1.83% for those without lung cancer but the difference was not statistically significant; p=0.2507. However, the incidence of lung cancer was the same for NCCN Group 2 as for the complete sample. NCCN Group 2 model AUC was 0.634 (95% CI 0.522 – 0.746), the sensitivity and specificity of the model at the recommended screening threshold were 64.7% and 56.0%, respectively and PPV was 4.2%. Conclusions: Lung cancer risk prediction model, PLCOm2012noEd, predictive performance in a clinical lung cancer screening program was adequate to help patients and their physicians assess individual risk of lung cancer relative to the recommended model risk screening threshold (1.51%) and to supplement USPSTF and CMS screening program entry criteria for shared decision making discussions. Model risk predictive capability for the NCCN Group 2 subgroup did not match actual screening program lung cancer results

Fostering Patient- and Family-Centered Care in Radiology Practice

Patient- and family-centered care (PFCC) is “a model of providing care in which the patient and family are partners with the provider and care team”. With reimbursement linked to health care outcomes and patient satisfaction, radiology has an opportunity to add value to the health care system by fostering partnerships with patients and families

Developing a Pan-European Technical Standard for a Comprehensive High-quality Lung Cancer CT Screening Program. An ERS Technical Standard.

UNLABELLED Screening for lung cancer with low radiation dose computed tomography (LDCT) has a strong evidence base. The European Council adopted a recommendation in November 2022 that lung cancer screening be implemented using a stepwise approach. The imperative now is to ensure that implementation follows an evidence-based process that delivers clinical and cost effectiveness. This ERS Taskforce was formed to provide a technical standard for a high-quality lung cancer screening program. METHOD A collaborative group was convened to include members of multiple European societies (see below). Topics were identified during a scoping review and a systematic review of the literature was conducted. Full text was provided to members of the group for each topic. The final document was approved by all members and the ERS Scientific Advisory Committee. RESULTS Ten topics were identified representing key components of a screening program. The action on findings from the LDCT were not included as they are addressed by separate international guidelines (nodule management and clinical management of lung cancer) and by a linked taskforce (incidental findings). Other than smoking cessation, other interventions that are not part of the core screening process were not included (e.g. pulmonary function measurement). Fifty-three statements were produced and areas for further research identified. CONCLUSION This European collaborative group has produced a technical standard that is a timely contribution to implementation of LCS. It will serve as a standard that can be used, as recommended by the European Council, to ensure a high quality and effective program

Ethics of artificial intelligence in radiology:summary of the joint European and North American multisociety statement

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes