Rodriguez Alvarez Adriana A, Crosby Benjamin, Singh Sarah, Weinberg Janice, Byrne Nicole, Vazirani Aniket, Suzuki Kei
Department of Clinical Research, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
Department of Surgery, University of California Davis, Sacramento, CA, USA.
Transl Cancer Res. 2024 Apr 30;13(4):1596-1605. doi: 10.21037/tcr-23-2304. Epub 2024 Apr 12.
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.
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 - 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.
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.
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.
使用个性化风险分层来确定肺癌（LC）风险可能会提高筛查效果。虽然前列腺、肺癌、结直肠癌和卵巢癌筛查试验（PLCO）是一种成熟的LC筛查分层模型，但它主要源自白种人群体，其在安全网医院（SNH）人群中的有效性尚不清楚。我们开发了一种更适合SNH人群的模型，并在SNH环境中将其性能与PLCO模型进行了比较。
回顾性数据集来自2015年至2019年在SNH接受LC筛查的患者。使用以下变量计算描述性统计数据：年龄、性别、种族、教育程度、体重指数（BMI）、吸烟史、个人癌症史、家族LC病史、慢性阻塞性肺疾病（COPD）和肺气肿。使用t检验和卡方检验比较变量分布。使用SNH和PLCO模型计算LC风险评分，并分为低（评分<0.65%）、中（0.65 - 1.49%）和高（>1.5%）。应用线性回归来评估模型与协变量之间的关系。
在896名个体中，38人被诊断为LC。数据反映了SNH患者的人口统计学特征，其中主要是非洲裔美国人（53.5%）、当前吸烟者（69.9%）和患有肺气肿者（70.1%）。在非LC队列中，SNH模型最常将患者分类为低风险，而PLCO模型最常将患者分类为中等风险。在LC队列中，平均评分或风险分层之间没有显著差异。SNH模型显示出92.1%的敏感性和96.8%的特异性。而PLCO模型显示出89.4%的敏感性和26.1%的特异性。肺气肿在SNH模型中显示出强烈关联（P<0.001），而种族则无关联。
SNH模型在表征SNH人群中的LC风险方面表现出更高的特异性。结果表明，在分层模型中识别风险因素时，研究样本代表性的重要性。
