Objectives To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT. Methods A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle–Ottawa scale, and publication bias was assessed by Egger’s test. Results Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15–29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12–39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3–7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth. Conclusions The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth. Key Points • Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

Objectives To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. Methods The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4–19), while all of included studies were assessed as having a high risk of bias (ROB) overall. Conclusions Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. Key Points • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4–19), and all of studies were considered to have a high risk of bias overall.