A narrative review on CT-based evaluation and prediction of lung nodule growth: current status and future directions.

Background and Objective: Lung cancer ranks as the most frequently diagnosed malignancy worldwide and the leading cause of cancer deaths. Pulmonary nodule growth serves as critical information for determining the probability of malignancy and guiding clinical decisions regarding surgical intervention and follow-up strategies. This review synthesizes current evidence on the multidimensional evaluation criteria for pulmonary nodule growth and the advancements in computed tomography (CT)-derived imaging prediction models, aiming to inform and optimize the clinical management of pulmonary nodules. Methods: A comprehensive literature search was performed across the Web of Science, PubMed, Cochrane Library, and EMBASE databases. The search strategy was designed to identify articles addressing pulmonary nodule growth and CT imaging features. The search was limited to original research articles and meta-analyses published in English between January 1, 2020 and February 3, 2026. Key Content and Findings: Current guidelines still exhibit subtle differences in defining pulmonary nodule growth, with the most commonly adopted criteria in contemporary research including an increase in mean diameter of 1.5 or 2 mm, a 25% volumetric increase, or a volume doubling time (VDT) of less than 400 days. In this context, CT imaging provides critical information associated with pulmonary nodule growth, such as nodule size, density, tumor characteristics, and peritumoral signs. Meanwhile, with the advancement of artificial intelligence (AI), radiomics approaches have further improved the accuracy of pulmonary nodule growth prediction, while deep learning has enabled the visualization of future nodule imaging. Additionally, natural language processing (NLP) models, as a rapidly developing technology, have exhibited considerable promise in predicting nodule growth and enhancing the efficiency of follow-up management protocols. Conclusions: This article provides a systematic overview of the evolution of assessment methods for pulmonary nodule growth and the latest breakthroughs in predictive modeling, and offers perspectives on future directions in this domain. With the continuous empowerment of AI in pulmonary nodule growth management, the corresponding clinical pathway stands to benefit from increasingly systematic optimization and evolution.