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Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma

Yan Gu, Bo Zheng, Tianjing Zhao, Yingjie Fan

2022Journal of Thoracic Imaging14 citationsDOIOpen Access PDF

Abstract

To compare computed tomography (CT)-based radiologic features in patients, who are diagnosed with lung adenocarcinoma with the pathologically detected spread of tumor cells through air spaces (STAS positive [STAS+]) and those with no STAS. PubMed, Embase, and Scopus databases were systematically searched for observational studies (either retrospective or prospective) of patients with lung adenocarcinoma that had compared CT-based features between STAS+ and STAS-negative cases (STAS-). The pooled effect sizes were reported as odds ratio (OR) and weighted mean difference (WMD). STATA software was used for statistical analysis. The meta-analysis included 10 studies. Compared with STAS-, STAS+ adenocarcinoma was associated with increased odds of solid nodule (OR: 3.30, 95% CI: 2.52, 4.31), spiculation (OR: 2.05, 95% CI: 1.36, 3.08), presence of cavitation (OR: 1.49, 95% CI: 1.00, 2.22), presence of clear boundary (OR: 3.01, 95% CI: 1.70, 5.32), lobulation (OR: 1.65, 95% CI: 1.11, 2.47), and pleural indentation (OR: 1.98, 95% CI: 1.41, 2.77). STAS+ tumors had significant association with the presence of pulmonary vessel convergence (OR: 2.15, 95% CI: 1.61, 2.87), mediastinal lymphadenopathy (OR: 2.06, 95% CI: 1.20, 3.56), and pleural thickening (OR: 2.58, 95% CI: 1.73, 3.84). The mean nodule diameter (mm) (WMD: 6.19, 95% CI: 3.71, 8.66) and the mean solid component (%) (WMD: 24.5, 95% CI: 10.5, 38.6) were higher in STAS+ tumors, compared with STAS- ones. The findings suggest a significant association of certain CT-based features with the presence of STAS in patients with lung adenocarcinoma. These features may be important in influencing the nature of surgical management.

Topics & Concepts

MedicineAdenocarcinomaComputed tomographyLungTomographyRadiologyAdenocarcinoma of the lungLung tumorLung cancerPathologyCancerInternal medicineLung Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingMedical Imaging Techniques and Applications
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