ArticleFrontiers in oncology2021
Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A narrative review on CT-based evaluation and prediction of lung nodule growth: current status and future directions.Translational lung cancer research · 2026Review
- [Advances in the Growth Risk Assessment and Precision Management of Pulmonary Subsolid Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Predicting pulmonary nodule growth from a single time point: a fusion model of radiomics and deep learning to optimize follow-up strategies.Journal of thoracic disease · 2026Article
- A nomogram predicts the new high-grade patterns of pulmonary invasive non-mucinous adenocarcinoma based on the radiomics and clinical features.American journal of translational research · 2025Article
- Article
- Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.Quantitative imaging in medicine and surgery · 2024Article
- Prediction of subsolid pulmonary nodule growth rate using radiomics.BMC medical imaging · 2023Article
- Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns.Diagnostics (Basel, Switzerland) · 2023Review
- Radiomics-Based Machine Learning for Predicting the Injury Time of Rib Fractures in Gemstone Spectral Imaging Scans.Bioengineering (Basel, Switzerland) · 2022Article
- Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study.Insights into imaging · 2022Article
- Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images.Frontiers in oncology · 2022Article
- The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management.Frontiers in oncology · 2022Review
- MRI-Based Radiomic Signature Identifying Secondary Loss of Response to Infliximab in Crohn's Disease.Frontiers in nutrition · 2021Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.
methodsFrom January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.
resultsSixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.
conclusionsThe model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.
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