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