Repeat Annual Lung Cancer Screening After Baseline Screening Among Screen-Negative Individuals: No-Cost Coverage Is Not Enough

Adherence to Annual Lung Cancer Screening in a Centralized Academic Program

BACKGROUND

Adherence to lung cancer screening (LCS) protocols is critical for achieving mortality reductions. However, adherence rates, particularly for recommended annual screening among patients with low-risk findings, are often sub-optimal. We evaluated annual LCS adherence for patients with low-risk findings participating in a centralized screening program at a tertiary academic center.

PATIENTS AND METHODS

We conducted a retrospective, observational cohort study of a centralized lung cancer screening program launched in July 2018. We performed electronic medical review of 337 patients who underwent low-dose CT (LDCT) screening before February 1, 2021 (to ensure ≥ 15 months follow up) and had a low-risk Lung-RADS score of 1 or 2. Captured data included patient characteristics (smoking history, Fagerstrom score, environmental exposures, lung cancer risk score), LDCT imaging dates, and Lung-RADS results. The primary outcome measure was adherence to annual screening. We used multivariable logistic regression models to identify factors associated with adherence.

RESULTS

Overall, 337 patients had an initial Lung-RADS result of 1 (n = 189) or 2 (n = 148). Among this cohort, 139 (73.5%) of Lung-RADS 1 and 111 (75.0%) of Lung-RADS 2 patients completed the annual repeat LDCT within 15 months, respectively. The only patient characteristic associated with adherence was having Medicaid coverage; compared to having private insurance, Medicaid patients were less adherent (adjusted OR = 0.37, 95% CI = 0.15-0.92). No other patient characteristic was associated with adherence.

CONCLUSION

Our centralized screening program achieved a high initial annual adherence rate. Although LCS has first-dollar insurance coverage, other socioeconomic concerns may present barriers to annual screening for Medicaid recipients.

Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population

PURPOSE

The aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RADS®) with lung cancer diagnosis.

METHODS

This retrospective study enrolled individuals undergoing LCS between January 1, 2015, and June 30, 2020. Individuals' sociodemographic characteristics, Lung-RADS scores, pathology-proven lung cancers, and tumor characteristics were determined via electronic health record and the health system's tumor registry. Associations between the outcome of lung cancer diagnosis within 1 year after LCS and covariates of sociodemographic characteristics and Lung-RADS score were determined using logistic regression.

RESULTS

Of 3,326 individuals undergoing 5,150 LCS examinations, 102 (3.1%) were diagnosed with lung cancer within 1 year of LCS; most of these cancers were screen detected (97 of 102 [95.1%]). Over the study period, there were 118 total LCS-detected cancers in 113 individuals (3.4%). Most LCS-detected cancers were adenocarcinomas (62 of 118 [52%]), 55.9% (65 of 118) were stage I, and 16.1% (19 of 118) were stage IV. The sensitivity, specificity, positive predictive value, and negative predictive value of Lung-RADS in diagnosing lung cancer within 1 year of LCS were 93.1%, 83.8%, 10.6%, and 99.8%, respectively. On multivariable analysis controlling for sociodemographic characteristics, only Lung-RADS score was associated with lung cancer (odds ratio for a one-unit increase in Lung-RADS score, 4.68; 95% confidence interval, 3.87-5.78).

CONCLUSIONS

The frequency of LCS-detected lung cancer and stage IV cancers was higher than reported in the National Lung Screening Trial. Although Lung-RADS was a significant predictor of lung cancer, the positive predictive value of Lung-RADS is relatively low, implying opportunity for improved nodule classification.