Predictors of diagnostic accuracy in oncology imaging patients with pulmonary nodules
DOI:
https://doi.org/10.5281/zenodo.22814208Keywords:
pulmonary nodule, diagnostic accuracy, computed tomography, PET-CT, radiomicsAbstract
Background: Pulmonary nodules are detected incidentally or through screening in many chest CT examinations, and distinguishing the small minority that are malignant from the majority that are benign remains a central diagnostic problem in oncology imaging, since no single modality or threshold reliably separates the two.
Objective: This review synthesizes evidence on predictors of diagnostic accuracy in pulmonary nodule imaging, covering patient risk factors, nodule morphology, positron emission tomography, clinical prediction models, and radiomics and deep learning.
Methods: We synthesized peer-reviewed cohort studies, screening trials, meta-analyses, and guideline documents addressing diagnostic performance of CT, PET-CT, clinical risk models, and quantitative image analysis in patients with indeterminate pulmonary nodules.
Findings: Size, growth rate, and morphology such as spiculation and consistency are the strongest single predictors of malignancy; clinical models combining age, smoking history, and nodule features outperform size thresholds alone; PET accuracy declines below roughly 8-10 millimeters and in regions with high granulomatous disease prevalence; and radiomic and deep-learning approaches now approach or exceed conventional models in retrospective validation.
Conclusions: Diagnostic accuracy in pulmonary nodule imaging reflects several interacting predictors rather than any single test characteristic, and structured multivariable risk stratification combined with matched imaging technique outperforms reliance on any one modality or threshold in isolation.
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