Abstract Background Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules. Methods Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio...