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• W3208970813 abstract "<h3>Purpose/Objective(s)</h3> Despite recent guideline recommendations, the utilization of low-dose CT (LDCT) for lung cancer screening has remained low. Little is known about the driving factors behind these low rates or the effect of real-world LDCT utilization on lung cancer outcomes. We hypothesize that LDCT screening will be associated with less advanced disease at diagnosis and improved cancer survival. Furthermore, we utilize a machine learning approach to investigate predictors for receiving LDCT screening. <h3>Materials/Methods</h3> We identified patients with non-small cell lung cancer (NSCLC) within the Veterans Health Administration (VHA) diagnosed from 2015 to 2017. LDCT screening was identified using procedural codes. We calculated LDCT rates using the pre-diagnostic observation period that included up to 3 years prior to diagnosis. Multivariable logistic regression assessed the influence of LDCT usage on metastatic disease at diagnosis. Lead-time correction using published LDCT lead-times was performed. Cancer-specific mortality (CSM) was evaluated using Fine-Gray regression and non-cancer death as a competing risk. A lasso machine learning model was used to identify important predictors for receiving LDCT screening. The data were split 75%/25% into training/testing datasets. We constructed the model with training data and evaluated performance within the test data using area under the curve (AUC). An AUC of 1.0 indicated perfect prediction. <h3>Results</h3> The cohort included 4,664 patients diagnosed with NSCLC at a mean age of 67.8. The pre-diagnostic period ranged from 1-3 years, median of 1 year. Median follow-up was 12 months. Overall LDCT screening rate was 1.3%. From 2015-2017, this rate increased from 0.1% to 6.6%. Compared with no screening, Patients with ≥ 1 LDCT were more likely to present with stage I disease and less likely to present with stage IV disease (Stage I: 64% vs 42%, <i>P</i> < 0.01; Stage IV: 10% vs 22%, <i>P</i> < 0.01). In multivariable models, screened patients had lower risk of metastatic disease at diagnosis (odds ratio = 0.40, 95% confidence interval (CI) = [0.22-0.74], <i>P</i> = 0.03) and CSM (sub-distribution hazard ratio = 0.17, 95% CI = [0.04-0.63], <i>P</i> < 0.01). Our lasso model achieved an AUC of 0.82 and identified year of diagnosis and regional VA site of care as the most important predictors associated with receiving LDCT screening. Non-Hispanic White patients were more likely to undergo LDCT screening. <h3>Conclusion</h3> Within the VHA, utilization of LDCT screening is increasing though the usage remains low. Patients undergoing LDCT were diagnosed at earlier stages and were less likely to die from cancer. While overdiagnosis and lead-time bias need to be considered, these findings are consistent with randomized data and support the need for increased utilization of LDCT screening. Availability of LDCT appeared to be the main predictor of utilization. Providing access to more patients, including those in diverse racial and ethnic groups, should be a priority to reduce lung cancer mortality." @default.
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• W3208970813 date "2021-11-01" @default.
• W3208970813 modified "2023-09-27" @default.
• W3208970813 title "Evaluating Clinical Trends and Benefits of Low-Dose CT in Lung Cancer Patients" @default.
• W3208970813 doi "https://doi.org/10.1016/j.ijrobp.2021.07.1267" @default.
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