Potential Impact of Criteria Modifications on Race and Sex Disparities in Eligibility for Lung Cancer Screening

Smeltzer, Matthew; Liao, Wei; Faris, Nicholas R.; Fehnel, Carrie; Goss, Jordan; Shepherd, Catherine J.; Ramos, Rodolfo; Qureshi, Talat; Mukhopadhyay, Ayesha; Ray, Meredith; Osarogiagbon, Raymond U.

doi:10.1016/j.jtho.2022.09.220

Cited by 14 publications

(15 citation statements)

References 54 publications

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“…In this lung cancer screening study, more women than men had lung cancer (χ 2 = 4.321, p = 0.000) and more women had adenocarcinoma (χ 2 = 14.421, p = 0.000), and similar results were found in other studies that screened for lung cancer [12] .…”

Section: Discussionsupporting

confidence: 88%

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors evaluation in lung cancer screening

Gao¹,

Kang

et al. 2023

Preprint

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Objective To evaluate the consistency between doctors and Artificial Intelligence (AI) software in analyzing lung nodules, and further compare the advantages of doctors and artificial intelligence in identifying benign and malignant lung nodules based on the pathological results as the gold standard. Materials and methods This retrospective study analyzed participants over 40 years old in the local place from 2011 to 2013. Pulmonary nodules were radiologically examined with a low-dose chest CT scan while being evaluated by a doctor expert panel of radiology, oncology, and thoracic department, and by a computer-aided diagnostic system driven by AI, respectively. Consistency tests were used to evaluate the consistency of the radiological features of the lung nodules. The Receiver Operating Characteristic curve(ROC) was used to evaluate the diagnostic efficacy. Logistic regression analysis was used to predict the possibility of lung cancer. Results Totally 570 subjects were included in this retrospective study. The AI software was very consistent with the panel's evaluation in the position and diameter of the lung nodules (kappa = 0.883, Concordance Correlation Coefficien(CCC) = 0.809, p = 0.000), and the consistency comparison of the solid nodules was acceptable in attenuation characteristics (kappa = 0.503); For patients diagnosed with lung cancer, the AUC of the panel and AI were 0.873 and 0.921, respectively, however, there was no significant difference (p = 0.0950). The diameter is a predictor of lung cancer in both panel and AI predictive models. Conclusion AI software can help doctors diagnose nodules and is consistent with doctors in differentiating between benign and malignant lung nodules.

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Section: Discussionsupporting

confidence: 88%

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors evaluation in lung cancer screening

Gao¹,

Kang

et al. 2023

Preprint

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“…We identified seven studies that examined individuals who were eligible and ineligible for lung cancer screening according to various guidelines (e.g., USPSTF, Centers for Medicare and Medicaid Services, National Comprehensive Cancer Network, and risk models; i.e., the risk‐prediction model developed in 2012 using data from the 2012 PLCO and National Lung Cancer Screening Trials [PLCO M2012 ]) 16,22,24,25,28,31,36 . Across studies, populations varied widely, and the rates of those reported ineligible for screening because of YSQ criteria ranged from 6.6% 31 to 45.8% 36 (Tables 2 and 9).…”

Section: Resultsmentioning

confidence: 99%

“…e., the risk-prediction model developed in 2012 using data from the 2012 PLCO and National Lung Cancer Screening Trials [PLCO M2012 ]). 16,22,24,25,28,31,36 Across studies, populations varied widely, and the rates of those reported ineligible for screening because of YSQ criteria ranged from 6.6% 31 to 45.8% 36 (Tables 2 and 9). 2 and 6).…”

Section: Screening Ineligibility Because Of the 15-ysq Criterionmentioning

confidence: 99%

Lung cancer diagnosis and mortality beyond 15 years since quit in individuals with a 20+ pack‐year history: A systematic review

Kondo,

Rahman,

Ayers

et al. 2023

CA A Cancer J Clinicians

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Current US lung cancer screening recommendations limit eligibility to adults with a pack‐year (PY) history of ≥20 years and the first 15 years since quit (YSQ). The authors conducted a systematic review to better understand lung cancer incidence, risk and mortality among otherwise eligible individuals in this population beyond 15 YSQ. The PubMed and Scopus databases were searched through February 14, 2023, and relevant articles were searched by hand. Included studies examined the relationship between adults with both a ≥20‐PY history and ≥15 YSQ and lung cancer diagnosis, mortality, and screening ineligibility. One investigator abstracted data and a second confirmed. Two investigators independently assessed study quality and certainty of evidence (COE) and resolved discordance through consensus. From 2636 titles, 22 studies in 26 articles were included. Three studies provided low COE of elevated lung cancer incidence beyond 15 YSQ, as compared with people who never smoked, and six studies provided moderate COE that the risk of a lung cancer diagnosis after 15 YSQ declines gradually, but with no clinically significant difference just before and after 15 YSQ. Studies examining lung cancer‐related disparities suggest that outcomes after 15 YSQ were similar between African American/Black and White participants; increasing YSQ would expand eligibility for African American/Black individuals, but for a significantly larger proportion of White individuals. The authors observed that the risk of lung cancer not only persists beyond 15 YSQ but that, compared with individuals who never smoked, the risk may remain significantly elevated for 2 or 3 decades. Future research of nationally representative samples with consistent reporting across studies is needed, as are better data from which to examine the effects on health disparities across different populations.

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“…The LNP extended the possibility of early detection to LDCT-ineligible subsets whose true lung cancer hazard may have otherwise been underestimated. 24 , 43 , 44 Such programs may provide a rich recruitment ground for screening-eligible candidates and a pathway to early lung cancer detection in populations without access to LDCT, the situation in most countries. In future studies, we plan to explore how innovative risk-stratification strategies, including artificial intelligence and biomarker-based selection, might improve the effectiveness of early lung cancer detection efforts in diverse populations.…”

Section: Discussionmentioning

confidence: 99%

“…The findings of this cohort study of 2 pathways to early lung cancer detection suggest that persons enrolled in an LNP had great cumulative hazard for lung cancer diagnosis within 2 years, irrespective of smoking history. The LNP extended the possibility of early detection to LDCT-ineligible subsets whose true lung cancer hazard may have otherwise been underestimated . Such programs may provide a rich recruitment ground for screening-eligible candidates and a pathway to early lung cancer detection in populations without access to LDCT, the situation in most countries.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Lung Cancer Risk Among Persons Undergoing Screening or Guideline-Concordant Monitoring of Lung Nodules in the Mississippi Delta

Osarogiagbon¹,

Liao²,

Faris³

et al. 2023

JAMA Netw Open

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ImportanceGuideline-concordant management of lung nodules promotes early lung cancer diagnosis, but the lung cancer risk profile of persons with incidentally detected lung nodules differs from that of screening-eligible persons.ObjectiveTo compare lung cancer diagnosis hazard between participants receiving low-dose computed tomography screening (LDCT cohort) and those in a lung nodule program (LNP cohort).Design, Setting, and ParticipantsThis prospective cohort study included LDCT vs LNP enrollees from January 1, 2015, to December 31, 2021, who were seen in a community health care system. Participants were prospectively identified, data were abstracted from clinical records, and survival was updated at 6-month intervals. The LDCT cohort was stratified by Lung CT Screening Reporting and Data System as having no potentially malignant lesions (Lung-RADS 1-2 cohort) vs those with potentially malignant lesions (Lung-RADS 3-4 cohort), and the LNP cohort was stratified by smoking history into screening-eligible vs screening-ineligible groups. Participants with prior lung cancer, younger than 50 years or older than 80 years, and lacking a baseline Lung-RADS score (LDCT cohort only) were excluded. Participants were followed up to January 1, 2022.Main Outcomes and MeasuresComparative cumulative rates of lung cancer diagnosis and patient, nodule, and lung cancer characteristics between programs, using LDCT as a reference.ResultsThere were 6684 participants in the LDCT cohort (mean [SD] age, 65.05 [6.11] years; 3375 men [50.49%]; 5774 [86.39%] in the Lung-RADS 1-2 and 910 [13.61%] in the Lung-RADS 3-4 cohorts) and 12 645 in the LNP cohort (mean [SD] age, 65.42 [8.33] years; 6856 women [54.22%]; 2497 [19.75%] screening eligible and 10 148 [80.25%] screening ineligible). Black participants constituted 1244 (18.61%) of the LDCT cohort, 492 (19.70%) of the screening-eligible LNP cohort, and 2914 (28.72%) of the screening-ineligible LNP cohort (P &lt; .001). The median lesion size was 4 (IQR, 2-6) mm for the LDCT cohort (3 [IQR, 2-4] mm for Lung-RADS 1-2 and 9 [IQR, 6-15] mm for Lung-RADS 3-4 cohorts), 9 (IQR, 6-16) mm for the screening-eligible LNP cohort, and 7 (IQR, 5-11) mm for the screening-ineligible LNP cohort. In the LDCT cohort, lung cancer was diagnosed in 80 participants (1.44%) in the Lung-RADS 1-2 cohort and 162 (17.80%) in the Lung-RADS 3-4 cohort; in the LNP cohort, it was diagnosed in 531 (21.27%) in the screening-eligible cohort and 447 (4.40%) in the screening-ineligible cohort. Compared with Lung-RADS 1-2, the fully adjusted hazard ratios (aHRs) were 16.2 (95% CI, 12.7-20.6) for the screening-eligible cohort and 3.8 (95% CI, 3.0-5.0) for the screening-ineligible cohort; compared with Lung-RADS 3-4, the aHRs were 1.2 (95% CI, 1.0-1.5) and 0.3 (95% CI, 0.2-0.4), respectively. The stage of lung cancer was I to II in 156 of 242 patients (64.46%) in the LDCT cohort, 276 of 531 (52.00%) in the screening-eligible LNP cohort, and 253 of 447 (56.60%) in the screening-ineligible LNP cohort.Conclusions and RelevanceIn this cohort study, the cumulative lung cancer diagnosis hazard of screening-age persons enrolled in the LNP was higher than that in a screening cohort, irrespective of smoking history. The LNP provided access to early detection for a higher proportion of Black persons.

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Potential Impact of Criteria Modifications on Race and Sex Disparities in Eligibility for Lung Cancer Screening

Cited by 14 publications

References 54 publications

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors evaluation in lung cancer screening

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors evaluation in lung cancer screening

Lung cancer diagnosis and mortality beyond 15 years since quit in individuals with a 20+ pack‐year history: A systematic review

Evaluation of Lung Cancer Risk Among Persons Undergoing Screening or Guideline-Concordant Monitoring of Lung Nodules in the Mississippi Delta

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