Clinical Research Directory

Virtual Follow-up Prediction of Subsolid Pulmonary Nodules: A Prospective Silent Reader Study

The goal of this prospective, non-interventional study is to evaluate the predictive performance and potential clinical utility of an artificial intelligence (AI)-based virtual follow-up framework for incidentally detected pulmonary subsolid nodules (SSNs), including pure ground-glass and part-solid nodules. The main questions it aims to answer are: 1. Can the AI model accurately predict future CT appearances, quantitative growth characteristics, and morphological progression of pulmonary SSNs at specified follow-up intervals? 2. Does AI-assisted virtual follow-up improve radiologists' assessment of nodule progression and simulated surveillance management decisions compared with assessment without AI assistance? Researchers will compare radiologists' assessments with and without access to AI-generated future CT images and quantitative predictions. An additional reader evaluation will examine whether generated future images provide incremental value beyond quantitative predictions alone. Participants will undergo clinically indicated CT examinations and receive routine care according to standard clinical practice. The research team will: * Collect previously acquired CT images and clinical information. * Generate and lock AI predictions before the corresponding follow-up CT examinations occur. * Obtain and lock radiologists' assessments and simulated management recommendations before actual follow-up outcomes become available. * Compare predicted nodule progression with observed follow-up CT findings. AI predictions and research-related reader assessments will not be disclosed to treating clinicians or used to alter patients' actual surveillance or treatment decisions.