TY  - GEN
AV  - public
CY  - Heidelberg
TI  - Evaluation of Risk Models and Biomarkers for the Optimization of Lung Cancer Screening
Y1  - 2023///
ID  - heidok32435
KW  - Lung cancer
Screening
Early diagnosis
Computed Tomography
Biostatistics
Mathematical modelling
Estimation
Validation
Risk
A1  - González Maldonado, Sandra
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/32435/
N2  - More deaths can be attributed to lung cancer, than to any other cancer type. Evidence collected over the last 10 years, from randomized trials in the USA and Europe, indicates that screening by means of low-dose computed tomography (LDCT) could reduce the number of lung cancer (LC) deaths by about 20%-24%. While these findings have led to the implementation of screening programs in the USA, South Korea and Poland, discussions on their optimal design and execution are still ongoing in various countries, including Germany.
Optimizing screening means finding the right balance between mortality reduction and risks, harms, and monetary costs. LDCT-scans are expensive, expose participants to radiation and put them at risk for overdiagnosis, as well as at risk for unnecessary invasive and expensive confirmatory procedures triggered by false positive (FP) results. Minimizing the number of unnecessary screening and confirmatory examinations should be prioritized. While risk-based eligibility has been shown to best target candidates, questions regarding optimal screening frequency, accurate nodule evaluation, stop-screening criteria to reduce overdiagnosis, and the use of complementary non-invasive diagnostic methods, remain open. Statistical models and biomarkers have been developed to help answer these questions. However, there is limited evidence of their validity in data from screening contexts and populations other than those in which they were developed.
The analyses presented in this thesis are based on data collected as part of the German Lung Cancer Screening Intervention (LUSI) trial in order to validate models that address the questions: 1) can candidates for biennial vs annual screening be identified on the basis of their LC risk? 2) can the number of FP test results be reduced by accurately estimating the malignancy of LDCT-detected nodules? 3) What was the extent of overdiagnosis in the LUSI trial and how does overdiagnosis risk relate to the age and remaining lifetime of participants? Additionally, blood samples from participants of the LUSI were measured to evaluate: 4) whether the well-validated diagnostic biomarker test EarlyCDT®-Lung is sensitive enough to detect tumors seen in LDCT images.
The LCRAT+CT and Polynomial models predict LC risk based on subject characteristics and LDCT imaging findings. Results of this first external validation confirmed their ability to identify participants with LC detected within 1-2 years after first screening. Discrimination was higher compared to a criterion based on nodule size and, to a lesser degree, compared to a model based on smoking and subject characteristics (LCRAT). This suggested that while LDCT findings can enhance models, most of their performance can could be attributed to information on smoking. Skipping 50% of annual LDCT examinations (i.e., for participants with estimated risks <5th decile) would have caused <10% delayed diagnoses, indicating that candidates for biennial screening could be identified based on their predicted LC risks without compromising on early detection. Absolute risk estimates were, on average, below the observed LC rates, indicating poor calibration.
Models developed using data from the Canadian screening study PanCan showed excellent ability to differentiate between tumors and non-malignant nodules seen on LDCT scans taken at first screening participation and to accurately predict absolute malignancy risk. However, they showed lower performance when applied on data of nodules detected in later rounds. In contrast, a model developed on data from the UKLS trial and models developed on data from clinical settings did not perform as well in any screening round.
Excess incidence of screen-detected lung tumors, an estimator of overdiagnosis, was within the range of values reported by other trials after similar post-screening follow-up (ca. 5-6 years). Estimates of mean pre-clinical sojourn time (MPST) and LDCT detection sensitivity were obtained via mathematical modeling. The highest excess incidence and longest MPST estimates were found among adenocarcinomas. The proportion of tumors with long lead times predicted based on MPST estimates (e.g., 23% with lead times ?8 years) suggested a substantial overdiagnosis risk for individuals with residual life expectancies shorter than these hypothetical lead times, for example for heavy smokers over the age of 75.
The tumor autoantibody panel measured by EarlyCDT®-Lung, a test widely validated as a diagnostic tool in clinical settings and recently tested as a pre-screening tool in a large randomized Scottish trial (ECLS), was found to have insufficient sensitivity for the identification of lung tumors detected via LDCT and of participants with screen-detected pulmonary nodules for whom more invasive diagnostic procedures should be recommended.
Overall, the findings presented in this thesis indicate that risk prediction models can help optimize LC screening by assigning participants to appropriate screening intervals, and by increasing the accuracy of nodule evaluation. However, there is a need for further external model validation and re-calibration. Additionally, while excess incidence can provide estimates of overdiagnosis risk at a population-level, a better approach would be to obtain model-based personalized estimates of tumor lead and residual lifetime. Better individualized decisions about whether to start or stop screening could be taken on the basis of the relationship between these estimates and the risk of overdiagnosis. Finally, although there is evidence for the potential of biomarkers to complement LC screening, the so far most promising candidate (EarlyCDT®-Lung) cannot be recommended as a pre-screening tool given its poor sensitivity for the identification of lung tumors detected via LDCT. In conclusion, while steps have been taken in the right direction, more research is required in order to answer all open questions regarding the optimal design of lung cancer screening programs.
ER  -