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Special issue "The advance of solid tumor research in China": Participants with a family history of cancer have a higher participation rate in low-dose computed tomography for lung cancer screening.

Lan-Wei GuoQing-Cheng MengLi-Yang ZhengQiong ChenYin LiuHui-Fang XuRui-Hua KangLu-Yao ZhangShu-Zheng LiuXi-Bin SunShao-Kai Zhang
Published in: International journal of cancer (2022)
We aimed to determine participation in low-dose computed tomography (LDCT) of individuals with a family history of common cancers in a population-based screening program to provide timely evidence in high-risk populations in China. The analysis was conducted using data from the Cancer Screening Program in Urban China (CanSPUC), which recruited 282 377 participants aged 40 to 74 years from eight cities in the Henan province. Using the CanSPUC risk score system, 55 428 participants were evaluated to have high risk for lung cancer and were recommended for LDCT. We calculated the overall and group-specific participation rates using family history of common cancers and compared differences in participation rates between different groups. Odds ratios (ORs) and 95% confidence intervals were derived by multivariable logistic regression. Of the 55 428 participants, 22 260 underwent LDCT (participation rate, 40.16%). Family history of lung, esophageal, stomach, liver and colorectal cancer was associated with increased participation in LDCT screening. The odds of participants with a family history of one, two, three and four or more cancer cases undergoing LDCT screening were 1.9, 2.7, 2.8 and 3.5 times, respectively, than those without a family history of cancer. Compared to those without a history of cancer, participation in LDCT gradually increased as the number of cancer cases in the family increased (P < .001). Our findings suggest that there is room for improvement in lung cancer screening given the relatively low participation rate. Lung cancer screening in populations with a family history of cancer may improve efficiency and cost-effectiveness; however, this requires further verification.
Keyphrases
  • papillary thyroid
  • low dose
  • computed tomography
  • squamous cell
  • magnetic resonance imaging
  • positron emission tomography
  • machine learning
  • quality improvement