Improving Selection for Lung Cancer Screening in Socio-economically Disadvantaged Communities

Student thesis: Phd


Background Lung cancer (LC) is the leading cause of cancer mortality worldwide. Poor survival is driven by late onset of non-specific symptoms, resulting in advanced stage diagnoses. Evidence for the efficacy of low-dose CT (LDCT) screening in detecting cancer earlier, thereby reducing lung-cancer specific mortality, is now well established. Attention has turned to developing and implementing screening programmes in the population. A key aspect of an effective screening programme is the successful selection of participants; this ensures a favourable benefit-to-harm ratio for participants and an efficient and cost-effective programme. This thesis aims to improve screening selection in socio-economically disadvantaged populations by identifying areas of sub-optimal performance and considering strategies for further improvement. The focus on socio-economically deprived populations is of particular importance, as lung cancer risk is often higher in these subgroups, positioning them to be an ideal target population for LDCT screening. Methods I) A retrospective study of the Manchester Lung Health Check (LHC) pilot, a community-based LC screening programme, comparing the selection performance and calibration of National Lung Screening Trial (NLST) criteria and two risk prediction models (RPMs) (PLCOM2012 and LLPV2), as well as the comorbidity profile of the screening cohort. II) Retrospective modelling of a benefit-based selection approach (LYFS-CT) in the LHC pilot, comparing performance with a risk-based approach and examining the characteristics and outcomes of the screening cohort. III) A Manchester-based case-control study validating nine published polygenic risk score (PRS) tools and assessing if they could improve risk prediction. IV) A cross-sectional questionnaire study of LHC programme participants, examining risk perception, worry and disease knowledge. Results There were significant differences in screening selection performance based on the method of selection used. RPMs contributed to increased screening efficiency compared to NLST, but under-estimated LC risk in this population and selected a screening cohort with high levels of comorbidity. Inclusion of spirometry (FEV1/FVC ratio) or coronary artery calcification in RPMs may improve risk prediction but would further increase participant comorbidity. LYFS-CT selected significantly younger and less comorbid participants but also directed screening away from the most socio-economically disadvantaged. Eight PRS tools were successfully validated in the Manchester cohort and two novel genetic loci were identified for possible inclusion in a future PRS. Participants’ comparative risk perception was more accurate than absolute risk perception. Women and those at high LC risk were more likely to have adverse psychological indicators. Conclusion Risk-based selection leads to high screening efficiency, but RPMs are not well calibrated for use in socio-economically deprived populations and the optimal RPM and risk threshold strategy is unclear. Benefit-based selection may be an important tool for maximising the screening benefit provided to participants. Prospective studies are required to further elucidate the most advantageous selection strategy. Inclusion of genetic risk factors in RPMs may improve both risk- and benefit-based screening selection. Comparative-based language and decision aids should be employed for communicating risk to screening participants and ensuring effective shared decision making.
Date of Award31 Dec 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorPhilip Crosbie (Supervisor), Dafydd Evans (Supervisor), Emma Crosbie (Supervisor) & Miriam Smith (Supervisor)


  • Polygenic Risk Score
  • Risk Perception
  • Genetics
  • Screening
  • Lung Cancer
  • Risk Prediction

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