Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening

Katki, Hormuzd A.; Kovalchik, Stephanie; Petito, Lucia; Cheung, Li C.; Jacobs, Eric J.; Jemal, Ahmedin; Berg, Christine D.; Chaturvedi, Anil K.

doi:10.7326/m17-2701

Cited by 139 publications

(151 citation statements)

References 35 publications

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“…We also applied the

C_{τ}^{*}

index to ever smokers at risk of lung cancer incidence and subsequent death. The four best performing lung cancer models (identified as such in the previously published work) had previously reported differences in calibration performance in ethic/racial subgroups . We examined discrimination ability in ethic/racial subgroups of the NLST; despite using information from both lung cancer incidence and death, the

C_{τ}^{*}

index was not able to find a statistically significant difference in discrimination ability.…”

Section: Discussionmentioning

confidence: 99%

“…Subgroup analysis found that these models performed less well in racial/ethnic minorities likely because the data sets used to develop the models were predominantly caucasian . The differences in calibration ability of these models in racial/ethnic minorities have previously been described; we used the

C_{τ}^{*}

index to examine potential differences in discrimination ability of these models in racial/ethnic minorities.…”

Section: Applications Of the C∗ Indexmentioning

confidence: 97%

“…Using risk prediction models to select US ever smokers for CT lung screening is likely to prevent more lung cancer deaths than the current USPSTF recommendations . Previously, published work examined calibration and discrimination of all lung cancer incidence/mortality models in the existing literature and found that three models for lung cancer incidence, ie, the Bach, PLCOm, and LCRAT models, and 1 model for lung cancer death, ie, the LCDRAT model, performed best in US cohorts and may be best suited for risk‐based selections . Subgroup analysis found that these models performed less well in racial/ethnic minorities likely because the data sets used to develop the models were predominantly caucasian .…”

Section: Applications Of the C∗ Indexmentioning

confidence: 99%

“…Previously, published work examined calibration and discrimination of all lung cancer incidence/mortality models in the existing literature and found that three models for lung cancer incidence, ie, the Bach, PLCOm, and LCRAT models, and 1 model for lung cancer death, ie, the LCDRAT model, performed best in US cohorts and may be best suited for risk‐based selections . Subgroup analysis found that these models performed less well in racial/ethnic minorities likely because the data sets used to develop the models were predominantly caucasian . The differences in calibration ability of these models in racial/ethnic minorities have previously been described; we used the

C_{τ}^{*}

index to examine potential differences in discrimination ability of these models in racial/ethnic minorities.…”

Section: Applications Of the C∗ Indexmentioning

confidence: 99%

“…21 Subgroup analysis found that these models performed less well in racial/ethnic minorities likely because the data sets used to develop the models were predominantly caucasian. 21 The differences in calibration ability of these models in racial/ethnic minorities TABLE 6 Evaluation of discrimination ability of lung cancer models in African-Americans, Hispanic-Americans, and Asian-Americans in the control arm of the National Lung Screening Trial. We evaluate three models for lung cancer incidence (Bach, PLCOm2012, and LCRAT) and one model for lung cancer death (LCDRAT).…”

Section: Example Application In Illness-death Disease Processmentioning

confidence: 99%

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Prioritized concordance index for hierarchical survival outcomes

Cheung

Pan

Hyun

et al. 2019

Statistics in Medicine

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We propose an extension of Harrell's concordance (C) index to evaluate the prognostic utility of biomarkers for diseases with multiple measurable outcomes that can be prioritized. Our prioritized concordance index measures the probability that, given a random subject pair, the subject with the worst disease status as of a time τ has the higher predicted risk. Our prioritized concordance index uses the same approach as the win ratio, by basing generalized pairwise comparisons on the most severe or clinically important comparable outcome. We use an inverse probability weighting technique to correct for study‐specific censoring. Asymptotic properties are derived using U‐statistic properties. We apply the prioritized concordance index to two types of disease processes with a rare primary outcome and a more common secondary outcome. Our simulation studies show that when a predictor is predictive of both outcomes, the new concordance index can gain efficiency and power in identifying true prognostic variables compared to using the primary outcome alone. Using the prioritized concordance index, we examine whether novel clinical measures can be useful in predicting risk of type II diabetes in patients with impaired glucose resistance whose disease status can also regress to normal glucose resistance. We also examine the discrimination ability of four published risk models among ever smokers at risk of lung cancer incidence and subsequent death.

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“…We also applied the

C_{τ}^{*}

C_{τ}^{*}

index was not able to find a statistically significant difference in discrimination ability.…”

Section: Discussionmentioning

confidence: 99%

C_{τ}^{*}

index to examine potential differences in discrimination ability of these models in racial/ethnic minorities.…”

Section: Applications Of the C∗ Indexmentioning

confidence: 97%

Section: Applications Of the C∗ Indexmentioning

confidence: 99%

C_{τ}^{*}

index to examine potential differences in discrimination ability of these models in racial/ethnic minorities.…”

Section: Applications Of the C∗ Indexmentioning

confidence: 99%

Section: Example Application In Illness-death Disease Processmentioning

confidence: 99%

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Prioritized concordance index for hierarchical survival outcomes

Cheung

Pan

Hyun

et al. 2019

Statistics in Medicine

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Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography

Meza

Jeon

Toumazis

et al. 2021

JAMA

219

261

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IMPORTANCE The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years. OBJECTIVE To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies. DESIGN, SETTING, AND PARTICIPANTS Comparative simulation modeling with 4 lung cancer natural history models for individuals from the 1950 and 1960 US birth cohorts who were followed up from aged 45 through 90 years. EXPOSURES Screening with varying starting ages, stopping ages, and screening frequency. Eligibility criteria based on age, cumulative pack-years, and years since quitting smoking (risk factor-based) or on age and individual lung cancer risk estimation using risk prediction models with varying eligibility thresholds (risk model-based). A total of 1092 LDCT screening strategies were modeled. Full uptake and adherence were assumed for all scenarios. MAIN OUTCOMES AND MEASURES Estimated lung cancer deaths averted and life-years gained (benefits) compared with no screening. Estimated lifetime number of LDCT screenings, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths (harms). RESULTS Efficient screening programs estimated to yield the most benefits for a given number of screenings were identified. Most of the efficient risk factor-based strategies started screening at aged 50 or 55 years and stopped at aged 80 years. The 2013 USPSTF-recommended criteria were not among the efficient strategies for the 1960 US birth cohort. Annual strategies with a minimum criterion of 20 pack-years of smoking were efficient and, compared with the 2013 USPSTF-recommended criteria, were estimated to increase screening eligibility (20.6%-23.6% vs 14.1% of the population ever eligible), lung cancer deaths averted (469-558 per 100 000 vs 381 per 100 000), and life-years gained (6018-7596 per 100 000 vs 4882 per 100 000). However, these strategies were estimated to result in more false-positive test results (1.9-2.5 per person screened vs 1.9 per person screened with the USPSTF strategy), overdiagnosed lung cancer cases (83-94 per 100 000 vs 69 per 100 000), and radiation-related lung cancer deaths (29.0-42.5 per 100 000 vs 20.6 per 100 000). Risk model-based vs risk factor-based strategies were estimated to be associated with more benefits and fewer radiation-related deaths but more overdiagnosed cases. CONCLUSIONS AND RELEVANCE Microsimulation modeling studies suggested that LDCT screening for lung cancer compared with no screening may increase lung cancer deaths averted and life-years gained when optimally targeted and implemented. Screening individuals at aged 50 or 55 years through aged 80 years with 20 pack-years or more of smoking exposure was estimated to result in more benefits than the 2013 USPSTF-recommended criteria and less ...

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Screening for Lung Cancer

Force¹,

Krist

Davidson

et al. 2021

JAMA

927

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ung cancer is the second most common cancer and the leading cause of cancer death in the US. In 2020, an estimated 228 820 persons were diagnosed with lung cancer, and 135 720 persons died of the disease. 1 Themostimportantriskfactorforlungcancerissmoking. 2,3 Smok-ingisestimatedtoaccountforabout90%ofalllungcancercases, 2 with a relative risk of lung cancer approximately 20-fold higher in smokers than in nonsmokers. 3 Increasing age is also a risk factor for lung cancer. The median age of diagnosis of lung cancer is 70 years. 4,5 Lung cancer has a generally poor prognosis, with an overall 5-year survival rate of 20.5%. 1 However, early-stage lung cancer has a better prognosis and is more amenable to treatment. IMPORTANCE Lung cancer is the second most common cancer and the leading cause of cancer death in the US. In 2020, an estimated 228 820 persons were diagnosed with lung cancer, and 135 720 persons died of the disease. The most important risk factor for lung cancer is smoking. Increasing age is also a risk factor for lung cancer. Lung cancer has a generally poor prognosis, with an overall 5-year survival rate of 20.5%. However, early-stage lung cancer has a better prognosis and is more amenable to treatment.OBJECTIVE To update its 2013 recommendation, the US Preventive Services Task Force (USPSTF) commissioned a systematic review on the accuracy of screening for lung cancer with low-dose computed tomography (LDCT) and on the benefits and harms of screening for lung cancer and commissioned a collaborative modeling study to provide information about the optimum age at which to begin and end screening, the optimal screening interval, and the relative benefits and harms of different screening strategies compared with modified versions of multivariate risk prediction models.POPULATION This recommendation statement applies to adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. EVIDENCE ASSESSMENTThe USPSTF concludes with moderate certainty that annual screening for lung cancer with LDCT has a moderate net benefit in persons at high risk of lung cancer based on age, total cumulative exposure to tobacco smoke, and years since quitting smoking.RECOMMENDATION The USPSTF recommends annual screening for lung cancer with LDCT in adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years. Screening should be discontinued once a person has not smoked for 15 years or develops a health problem that substantially limits life expectancy or the ability or willingness to have curative lung surgery. (B recommendation) This recommendation replaces the 2013 USPSTF statement that recommended annual screening for lung cancer with LDCT in adults aged 55 to 80 years who have a 30 pack-year smoking history and currently smoke or have quit within the past 15 years.

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Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening

Abstract: Intramural Research Program of the National Institutes of Health/National Cancer Institute.

Cited by 139 publications

References 35 publications

Prioritized concordance index for hierarchical survival outcomes

Prioritized concordance index for hierarchical survival outcomes

Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography

Screening for Lung Cancer

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