Less Is More in Lung Cancer Risk Prediction Models

Abstract

Screening of high-risk individuals by low-dose chest computed tomography (CT) reduces lung cancer mortality, as has been shown by 2 large randomized clinical trials. 1,2 Contrary to other cancer screening programs, such as breast and colorectal cancer screening, individuals eligible for screening are not selected only based on sex and age. Lung cancer is in most cases diagnosed in (former) smokers. To increase the efficacy of a screening program, and to minimize harms to individuals at low risk of the disease, it is most cost-effective to invite only those individuals who have the highest risk of developing lung cancer to undergo annual low-dose chest CT. Risk prediction models aim to assist in identifying these high-risk individuals, and most international lung cancer screening guidelines recommend using a model to optimize selection of the screening population. 3In addition to prediction models for the identification of the population at risk, in recent years, a variety of models have been published predicting lung nodule malignancy risk in screen-detected pulmonary nodules. These lung nodule malignancy risk prediction models aim to improve decisionmaking regarding nodule management and diagnosis. In every second lung cancer screening participant, at least 1 lung nodule is detected at the baseline screening round. A quarter of those who undergo screening present with 2 or more baseline nodules. 4 At nodule level, fewer than 1% are diagnosed as lung cancer; the others are likely to represent benign lesions such as scars or intrapulmonary lymph nodes. Lung nodule malignancy prediction models are based either on data collected in screening studies (ie, different versions of the model used in the Pan-Canadian Early Detection of Lung Cancer Study [also referred to as the Brock model] 5 and the model using data from the UK Lung Cancer Screening [UKLS] trial 6 ) or on clinically, mostly incidentally, detected nodules (ie, models from the Mayo Clinic, the US Department of Veteran Affairs clinics, and Peking University People's Hospital).In the study by González Maldonado et al, 7 performance of these lung nodule malignancy prediction models was externally tested using data from the interventional group of the German Lung Cancer Screening Intervention (LUSI) randomized clinical trial. In total, 1159 participants with 3903 noncalcified lung nodules in any of 5 annual low-dose CT screening rounds were selected for this study. During the active screening period, 54 of 1159 participants with nodules (5%) were diagnosed with lung cancer. Most lung nodules were detected at baseline (2883 nodules [73.9%]), whereas half of the lung cancers were diagnosed in 1 of the 1020 nodules newly detected after baseline. In the rounds following baseline screening, 80.6% of lung cancers had diameter of at least 8 mm at diagnosis. González Maldonado et al 7 have shown that performance of all 8 prediction models was better on prevalence (baseline) nodules compared with nodules newly detected during incidence screenings. Previous studies have ...

Lung cancer LDCT screening and mortality reduction — evidence, pitfalls and future perspectives

“…Leaving these two variables out and keeping the other coefficients in the model unchanged would most likely result in biased estimates. The editorial associated with this publication outlined the benefits and disadvantages of attempting validation of these risk models 95 . This study exemplifies the importance of including parameters with a low risk of inter-reader variability in risk models.…”

“…This study exemplifies the importance of including parameters with a low risk of inter-reader variability in risk models. The inclusion of parameters with a high risk of inter-reader variability, such as the diagnosis of bronchitis or discrimination between part-solid and non-solid lung nodules, might strongly reduce the performance of these models for predicting outcomes in cohorts others than those with which they were developed 95 . Of note, all the models discussed herein have a reduced performance when used on nodules newly detected after baseline, confirming the need for separate management protocols for these nodules.…”