ArticleFrontiers in oncology2023
Preoperative prognostic prediction for stage I lung adenocarcinomas: Impact of the computed tomography features associated with the new histological grading system.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Nonlinear association between standardized uptake value (SUV) index based onQuantitative imaging in medicine and surgery · 2026Article
- Correlation of solid proportion and lymph node metastasis in lung cancers ≤30 mm in diameter.Quantitative imaging in medicine and surgery · 2025Article
- Visceral Pleural Invasion as a Determinant of Surgical Strategy in Non-Small Cell Lung Cancer: A Multicenter Study.Cancers · 2025Article
- CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma.Scientific reports · 2025Article
- Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines.Insights into imaging · 2025Review
- Using the length of pleural tag to predetermine pleural invasion by lung adenocarcinomas.Frontiers in oncology · 2024Article
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Abstract
Objectives: This study aimed to identify the computed tomography (CT) features associated with the new International Association for the Study of Lung Cancer (IASLC) three-tiered grading system to improve the preoperative prediction of disease-free survival of stage I lung adenocarcinoma patients. Methods: The study included 379 patients. Ordinal logistic regression analysis was used to identify the independent predictors of IASLC grades. The first multivariate Cox regression model (Model 1) was based on the significant factors from the univariate analysis. The second multivariate model (Model 2) excluded the histologic grade and based only on preoperative factors. Results: Larger consolidation tumor ratio (OR=2.15, Conclusion: CTR (cut-off values of <25% and ≥75%) and whole tumor size (cut-off value of 17 mm) could stratify patients into different prognosis and be used as preoperative surrogates for the IASLC grading system. Integrating these CT features with clinical T staging can improve the preoperative prognostic prediction for stage I lung adenocarcinoma patients.
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