Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.
نویسندگان
چکیده
BACKGROUND Implementing optimal lung cancer screening programs requires knowledge of the natural history and detectability of lung cancer. This information can be derived from the results of clinical trials with the aid of microsimulation models. METHODS Data from the Surveillance, Epidemiology, and End Results (SEER) program and individual-level data from the National Lung Screening Trial (NLST) and the Prostate, Lung, Colon, and Ovarian Cancer Screening trial (PLCO) were used to investigate the sensitivity (by histology and stage) of CT and chest radiography (CXR) and the mean preclinical sojourn time (MPST) of lung cancer (by gender, histology, and stage). The MISCAN-Lung model was used to reproduce the lung cancer incidence by method of detection (clinically or screen-detected), gender, histology, and stage in both trials and SEER, by calibrating CT and CXR sensitivity and natural history parameters. RESULTS CT sensitivity ranges from 8.83% to 99.35% and CXR sensitivity from 2.51% to 97.31%, depending on histology and stage. CT sensitivity for stage IA is more than 3-fold higher compared with CXR, for all histologies. The total MPST estimates for lung cancer progressing through preclinical stages IA to IV range from 3.09 to 5.32 years for men and 3.35 to 6.01 years for women. The largest difference in total MPST between genders was estimated for adenocarcinoma. CONCLUSIONS We estimate longer MPSTs for lung cancer compared with previous research, suggesting a greater window of opportunity for lung cancer screening. IMPACT This study provides detailed insights into the natural history of lung cancer and CT screening effectiveness.
منابع مشابه
Lung Cancer Detectability by Test, Histology, Stage, and Gender: Estimates from the NLST and the PLCO Trials
Background: Implementing optimal lung cancer screening programs requires knowledge of the natural history and detectability of lung cancer. This information can be derived from the results of clinical trials with the aid of microsimulation models. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) program and individual-level data from the National Lung Screening Trial (N...
متن کاملComparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials.
BACKGROUND The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to f...
متن کاملRisk prediction models for selection of lung cancer screening candidates: A retrospective validation study
BACKGROUND Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure ...
متن کاملRandomization to Screening for Prostate, Lung, Colorectal and Ovarian Cancers and Thyroid Cancer Incidence in Two Large Cancer Screening Trials
BACKGROUND Thyroid cancer incidence has increased significantly over the past three decades due, in part, to incidental detection. We examined the association between randomization to screening for lung, prostate, colorectal and/or ovarian cancers and thyroid cancer incidence in two large prospective randomized screening trials. METHODS We assessed the association between randomization to low...
متن کاملSelecting High-Risk Individuals for Lung Cancer Screening: A Prospective Evaluation of Existing Risk Models and Eligibility Criteria in the German EPIC Cohort.
Lung cancer risk prediction models are considered more accurate than the eligibility criteria based on age and smoking in identification of high-risk individuals for screening. We externally validated four lung cancer risk prediction models (Bach, Spitz, LLP, and PLCO(M2012)) among 20,700 ever smokers in the EPIC-Germany cohort. High-risk subjects were identified using the eligibility criteria ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
دوره 24 1 شماره
صفحات -
تاریخ انتشار 2015