ABSTRACT
Background: Determining lung cancer (LC) risk using personalized risk stratification may improve screening effectiveness. While the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) is a well-established stratification model for LC screening, it was derived from a predominantly Caucasian population and its effectiveness in a safety net hospital (SNH) population is unknown. We have developed a model more tailored to the SNH population and compared its performance to the PLCO model in a SNH setting. Methods: Retrospective dataset was compiled from patients screened for LC at SNH from 2015 to 2019. Descriptive statistics were calculated using the following variables: age, sex, race, education, body mass index (BMI), smoking history, personal cancer history, family LC history, chronic obstructive pulmonary disease (COPD), and emphysema. Variables distribution was compared using t- and chi-square tests. LC risk scores were calculated using SNH and PLCO models and categorized as low (scores <0.65%), moderate (0.65-1.49%), and high (>1.5%). Linear regression was applied to evaluate the relationship between models and covariates. Results: Of 896 individuals, 38 were diagnosed with LC. Data reflected the SNH patient demographics, which predominantly were African American (53.5%), current smokers (69.9%), and with emphysema (70.1%). Among the non-LC cohort, SNH model most frequently categorized patients as low risk, while PLCO model most frequently classified patients as moderate risk. Among the LC cohort, there was no significant difference between mean scores or risk stratification. SNH model showed 92.1% sensitivity and 96.8% specificity while PLCO model showed 89.4% sensitivity and 26.1% specificity. Emphysema demonstrated a strong association in SNH model (P<0.001) while race showed no relation. Conclusions: SNH model demonstrated greater specificity for characterizing LC risk in a SNH population. The results demonstrated the importance of study sample representation when identifying risk factors in a stratification model.
ABSTRACT
OBJECTIVE: Low dose computed tomography (LDCT) became the standard method for lung cancer (LC) screening in 2013. However, it is unclear whether there are differences in survival rates based on sex and whether the differences depend on screening status. We aimed to evaluate the LC survival rates between females and males based on screening. MATERIAL AND METHODS: This retrospective cohort study examined data from the Boston LC Study (BLCS) between 2013 and 2021. LC screening depends on patients' demographics (age and smoking history) to determine whether a person is a high-risk individual and, therefore, undergo LDCT. Descriptive statistics were calculated for race, age, histology, smoking history, stage, and treatment. These variables' distributions were compared between sex and screening status using t-test and chi-square, respectively. Cox proportional hazards model and Kaplan-Meier curves were used to compare survival between sex and screening. Propensity score matching was applied to account for selection bias in screening when evaluating the association between screening and stage. RESULTS: A total of 1,216 LC patients were identified with a screening incidence of 9.4 %, among whom 56 % were female. Unscreened males had 1.59 times higher risk of mortality than unscreened females (P=.0002) and had a worse 5-year survival (male 50 %; 95 %CI, 0.38,0.6 vs female 70 %; 95 %CI,0.62,0.76). In contrast, there were no significant differences in survival between sexes among screened. In a balanced cohort of screened and unscreened, the odds of being diagnosed at late stages for females and smokers were 1.33 and 2.51 times that of males and nonsmokers; however, there were no statistical significance. CONCLUSION: Unscreened females had a lower risk of mortality and better survival than unscreened males, while among the screened population, there was no difference in the overall survival. These observations demonstrate the influence of sex on survival prognosis in LC when screening is not performed.