  Statistical models for predicting lung cancer have the potential to
facilitate earlier diagnosis of malignancy and avoid invasive workup of benign
disease. Many models have been published, but comparative studies of their
utility in different clinical settings in which patients would arguably most
benefit are scarce. This study retrospectively evaluated promising predictive
models for lung cancer prediction in three clinical settings: lung cancer
screening with low-dose computed tomography, incidentally detected pulmonary
nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged
9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple
institutions to assess the area under the receiver operating characteristic
curve (AUC) of validated models including logistic regressions on clinical
variables and radiologist nodule characterizations, artificial intelligence on
chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented
each model from their published literature, re-training the models if
necessary, and curated each cohort from primary data sources. We observed that
model performance varied greatly across clinical use cases. No single
predictive model emerged as a clear winner across all cohorts, but certain
models excelled in specific clinical contexts. Single timepoint chest CT AI
performed well in lung screening, but struggled to generalize to other clinical
settings. Longitudinal imaging and multimodal models demonstrated comparatively
promising performance on incidentally-detected nodules. However, when applied
to nodules that underwent biopsy, all models underperformed. These results
underscore the strengths and limitations of 8 validated predictive models and
highlight promising directions towards personalized, noninvasive lung cancer
diagnosis.
