Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results

Tammemägi, Martin C.; Haaf, Kevin ten; Toumazis, Iakovos; Kong, Chung Yin; Han, Summer S.; Jeon, Jihyoun; Commins, John; Riley, Thomas L.; Meza, Rafael

doi:10.1001/jamanetworkopen.2019.0204

Cited by 79 publications

(62 citation statements)

References 37 publications

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“…Future screening programmes may incorporate screening result as a way of optimising lung cancer risk prediction. Examples of such models include PLCO 2012results 41 and LCRAT+CT. 42 Population selection could also take into account ‘life-years gained’ to reduce the impact of comorbidity on screening efficacy.…”

Section: Discussionmentioning

confidence: 99%

Analysis of lung cancer risk model (PLCO_M2012 and LLP_v2) performance in a community-based lung cancer screening programme

Lebrett

Balata

Evison

et al. 2020

Thorax

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IntroductionLow-dose CT (LDCT) screening of high-risk smokers reduces lung cancer (LC) specific mortality. Determining screening eligibility using individualised risk may improve screening effectiveness and reduce harm. Here, we compare the performance of two risk prediction models (PLCOM2012 and Liverpool Lung Project model (LLPv2)) and National Lung Screening Trial (NLST) eligibility criteria in a community-based screening programme.MethodsEver-smokers aged 55–74, from deprived areas of Manchester, were invited to a Lung Health Check (LHC). Individuals at higher risk (PLCOM2012 score ≥1.51%) were offered annual LDCT screening over two rounds. LLPv2 score was calculated but not used for screening selection; ≥2.5% and ≥5% thresholds were used for analysis.ResultsPLCOM2012 ≥1.51% selected 56% (n=1429) of LHC attendees for screening. LLPv2 ≥2.5% also selected 56% (n=1430) whereas NLST (47%, n=1188) and LLPv2 ≥5% (33%, n=826) selected fewer. Over two screening rounds 62 individuals were diagnosed with LC; representing 87% (n=62/71) of 6-year incidence predicted by mean PLCOM2012 score (5.0%). 26% (n=16/62) of individuals with LC were not eligible for screening using LLPv2 ≥5%, 18% (n=11/62) with NLST criteria and 7% (n=5/62) with LLPv2 ≥2.5%. NLST eligible Manchester attendees had 2.5 times the LC detection rate than NLST participants after two annual screens (≈4.3% (n=51/1188) vs 1.7% (n=438/26 309); p<0.0001). Adverse measures of health, including airflow obstruction, respiratory symptoms and cardiovascular disease, were positively correlated with LC risk. Coronary artery calcification was predictive of LC (adjOR 2.50, 95% CI 1.11 to 5.64; p=0.028).ConclusionProspective comparisons of risk prediction tools are required to optimise screening selection in different settings. The PLCOM2012 model may underestimate risk in deprived UK populations; further research focused on model calibration is required.

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Section: Discussionmentioning

confidence: 99%

Analysis of lung cancer risk model (PLCO_M2012 and LLP_v2) performance in a community-based lung cancer screening programme

Lebrett

Balata

Evison

et al. 2020

Thorax

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“…30 Furthermore, our models differed from other models in many ways, such as in target population and parameters. [18][19][20][21][22][23][24][25][26]31,32 On one hand, our models focused on patients with lung diseases and pleural effusion, in which 73.6% of cases were adenocarcinomatous in the lung cancer group, and 66.1% of subjects had tuberculosis in the benign group. This composition ratio is similar to that of epidemiological distribution of lung diseases with pleural effusion.…”

Section: Discussionmentioning

confidence: 99%

Development of risk prediction models for lung cancer based on tumor markers and radiological signs

et al. 2020

Clinical Laboratory Analysis

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Lung cancer is one of the leading causes of cancer-related deaths worldwide, accounting for about 787,000 deaths each year in China. 1,2 Many patients are identified in the advanced stages of lung cancer during initial diagnosis. The age-standardized 5-year relative survival of lung cancer was only 19.7% in China, but if diagnosed at an early stage, then surgical resection offers a favorable prognosis. 3-5 There are several methods for diagnosing lung cancer. The most commonly recommended method for screening lung cancer is computed tomography (CT), especially the low-dose CT (LDCT). Early screening of lung cancer through LDCT decreases the mortality rate by 20%. 6 Fluorodeoxyglucose positron emission tomography

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“…79 Risk models can be enhanced by including additional predictors, such as the patient's latest CTLS or biomarker results. 108,109 As technologies improve, deep learning algorithms may further enhance lung cancer screening. 110 However, many models are not practical for population-based CTLS, because they require blood or genetic tests, or extensive medical record data, or are limited to specific populations.…”

Section: Use Of Risk Modelsmentioning

confidence: 99%

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

Sands

Tammemägi

Couraud

et al. 2021

Journal of Thoracic Oncology

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Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for almost a fifth of all cancerrelated 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.

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Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results

Cited by 79 publications

References 37 publications

Analysis of lung cancer risk model (PLCO_M2012 and LLP_v2) performance in a community-based lung cancer screening programme

Analysis of lung cancer risk model (PLCO_M2012 and LLP_v2) performance in a community-based lung cancer screening programme

Development of risk prediction models for lung cancer based on tumor markers and radiological signs

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

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Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results

Cited by 79 publications

References 37 publications

Analysis of lung cancer risk model (PLCOM2012 and LLPv2) performance in a community-based lung cancer screening programme

Analysis of lung cancer risk model (PLCOM2012 and LLPv2) performance in a community-based lung cancer screening programme

Development of risk prediction models for lung cancer based on tumor markers and radiological signs

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

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Analysis of lung cancer risk model (PLCO_M2012 and LLP_v2) performance in a community-based lung cancer screening programme

Analysis of lung cancer risk model (PLCO_M2012 and LLP_v2) performance in a community-based lung cancer screening programme