Dataset Information

A simple model for predicting lung cancer occurrence in a lung cancer screening program: The Pittsburgh Predictor.

ABSTRACT: A user-friendly method for assessing lung cancer risk may help standardize selection of current and former smokers for screening. We evaluated a simple 4-factor model, the Pittsburgh Predictor, against two well-known, but more complicated models for predicting lung cancer risk.Trained against outcomes observed in the National Lung Screening Trial (NLST), the Pittsburgh Predictor used four risk factors, duration of smoking, smoking status, smoking intensity, and age, to predict 6-year lung cancer incidence. After calibrating the Bach and PLCOM2012 models to outcomes observed in the low-dose computed tomography arm of the NLST, we compared model calibration, discrimination, and clinical usefulness (net benefit) in the NLST and Pittsburgh Lung Screening Study (PLuSS) populations.The Pittsburgh Predictor, Bach, and PLCOM2012 represented risk equally well, except for the tendency of PLCOM2012 to overestimate risk in subjects at highest risk. Relative to the Pittsburgh Predictor, Bach and PLCOM2012 increased the area under the receiver operator characteristic curve by 0.007-0.009 and 0.012-0.021 units, respectively, depending on study population. Across a clinically relevant span of 6-year lung cancer risk thresholds (0.01-0.05), Bach and PLCOM2012 increased net benefit by less than 0.1% in NLST and 0.3% in PLuSS.In exchange for a small reduction in prediction accuracy, a simpler lung cancer risk prediction model may facilitate standardized procedures for advising and selecting patients with respect to lung cancer screening.

SUBMITTER: Wilson DO

PROVIDER: S-EPMC4457558 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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A simple model for predicting lung cancer occurrence in a lung cancer screening program: The Pittsburgh Predictor.

Wilson David O DO Weissfeld Joel J

Lung cancer (Amsterdam, Netherlands) 20150328 1

<h4>Background</h4>A user-friendly method for assessing lung cancer risk may help standardize selection of current and former smokers for screening. We evaluated a simple 4-factor model, the Pittsburgh Predictor, against two well-known, but more complicated models for predicting lung cancer risk.<h4>Methods</h4>Trained against outcomes observed in the National Lung Screening Trial (NLST), the Pittsburgh Predictor used four risk factors, duration of smoking, smoking status, smoking intensity, and ...[more]

PMID: 25863905

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Project description:ImportanceThe US Preventive Services Task Force recommends that individuals at high risk for lung cancer consider benefits and harms before pursuing lung cancer screening. Medical centers develop websites for their lung cancer screening programs, but to date little is known about the websites' portrayal of benefits and harms or what next steps they recommend for individuals considering screening.ObjectiveTo assess the presentation of potential benefits and harms and recommended next steps on lung cancer screening program websites.Design, setting, and participantsCross-sectional content analysis of 162 lung cancer screening program websites of academic medical centers (n = 81) and state-matched community medical centers (n = 81) that were randomly selected from American College of Radiology lung cancer screening-designated centers was conducted. The study was performed from December 1, 2018, to January 31, 2019.Main outcomes and measuresWebsite presentation of screening-associated benefits and harms was the primary outcome. Benefit was defined as any description related to the potential reduction in lung cancer mortality. Harms were based on the US Preventive Services Task Force recommendations and included false positives, false negatives, overdiagnosis, radiation exposure, and incidental findings. The secondary outcome was next steps that are recommended by websites.ResultsOverall, the 162 lung cancer screening program websites described the potential benefits more frequently than they described any potential harms (159 [98%] vs 78 [48%], P < .01). False-positive findings were the most frequently reported (72 [44%]) potential harm. Community centers were less likely than academic centers to report any potential harm (32 [40%] vs 46 [57%], P = .03), potential harm from radiation (20 [25%] vs 35 [43%], P = .01), and overdiagnosis (0% vs 11 [14%], P < .01). One hundred nineteen websites (73%) did not explicitly recommend that individuals personally consider the potential benefits and harms of screening; community centers were less likely than academic centers to give this recommendation (15 [19%] vs 28 [35%], P = .02). Most institutions (157 [97%]) listed follow-up steps for screening, but few institutions (35 [22%]) recommended that individuals discuss benefits and harms with a health care professional.Conclusions and relevanceInformation on public-facing websites of US lung cancer screening programs appears to lack balance with respect to portrayal of potential benefits and harms of screening. Important harms, such as overdiagnosis, were commonly ignored in the sites evaluated, and most of the centers did not explicitly guide individuals toward a guideline-recommended, shared decision-making discussion of harms and benefits.

Project description:BackgroundLung cancer screening conducted in high-risk group using low-dose computer tomography (LDCT) has been reported as an effective method to reduce lung cancer mortality in two large randomized-control trials. However, the effectiveness is uncertain when lung cancer screening is expanded to a nationwide population-based program.MethodsThe Korean Lung Cancer Screening Project (K-LUCAS) is a single-arm cohort study that was conducted from February 2017 to evaluate the feasibility of implementing an organized national lung cancer screening program in Korea. High-risk population aged 55-74 years with more than a 30-pack-year smoking history was recruited. Smoking history was obtained from administering questionnaires at national health screening programs or public smoking cessation programs which are already established programs in Korea. The screening results were reported using the Lung Imaging Reporting and Data System (Lung-RADS), suggested by the American College of Radiology. K-LUCAS was performed by a network-based diagnosis supporting system using a computer-aided detection (CAD) program to maintain screening quality. Current smokers were provided with mandatory smoking counseling.ResultsAmong 71,829 participants aged 50 years or older in the national health screening program, 5,975 (8.3%) were eligible for lung cancer screening. Among them, 1,062 (17.8%) refused to participate in K-LUCAS. Additionally, 779 participants were recruited in the smoking cessation program. Thus, a total of 5,692 eligible high-risk participants were recruited in this study. Among them, 865 (15.2%) had positive screening results, which requires a further examination; 529 (9.3%) had Lung-RADS category 3 (indeterminate), and 336 (5.9%) had category 4 (suspicious of lung cancer); 42 (0.7%) had confirmed lung cancer. Approximately 66.7% had early-stage lung cancer: 24 (57.1%), stage I and 4 (9.5%), stage II. Six (1.1%) patients developed complications at the time of diagnosis, including one death. The anxiety level related to cancer screening was low. Participation in screening encouraged motivation to quit smoking.ConclusionsK-LUCAS provided promising evidence supporting the implementation of a national lung cancer screening program to detect early stage lung cancer and promote smoking cessation for participants in Asian population.

Project description:ObjectiveLung cancer screening has been widely conducted in Western countries. However, population-based lung cancer screening programs in Hebei in China are sparse. Our study aimed to assess the participation rate and detection rate of positive nodules and lung cancer in Hebei province.MethodIn total, 228 891 eligible participants aged 40-74 years were enrolled in the Cancer Screening Program in Hebei from 2013 to 2019. A total of 54 846 participants were evaluated as the lung cancer high-risk population by a risk score system which basically followed the Harvard Risk Index and was adjusted for the characteristics of the Chinese population. Then this high-risk population was recommended for low-dose computed tomography (LDCT) screening. And all participants attended annual passive follow-up, and the active follow-up interval was based on radiologist's suggestion. All participants were followed-up until December 31, 2020. The overall, group-specific participation rates were calculated, and its associated factors were analyzed by a multivariable logistic regression model. Participation rates and detection of positive nodules and lung cancer were reported.ResultsThe overall participation rate was 52.69%, where 28 899 participants undertook LDCT screening as recommended. The multivariable logistic regression model demonstrated that a high level of education, having disease history, and occupational exposure were found to be associated with the participation in LDCT screening. The median follow-up time was 3.56 person-years. Overall, the positive identification of lung nodules and suspected lung cancer were 12.73% and 1.46% through LDCT screening. After the native and passive follow-up, 257 lung cancer cases were diagnosed by lung cancer screening, and the detection rate of lung cancer was 0.89% in the screening group. And its incidence density was 298.72 per 100,000. Positive lung nodule rate and detection rate were increased with age.ConclusionOur study identified personal and epidemiological factors that could affect the participation rate. Our findings could provide the guideline for precise prevention and control of lung cancer in the future.

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A simple model for predicting lung cancer occurrence in a lung cancer screening program: The Pittsburgh Predictor.

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A simple model for predicting lung cancer occurrence in a lung cancer screening program: The Pittsburgh Predictor.

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