ArticleQuantitative imaging in medicine and surgery2022
Dual-layer spectral detector computed tomography parameters can improve diagnostic efficiency of lung adenocarcinoma grading.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Advanced characterization and grading of invasive lung adenocarcinoma: integrative analysis with spectral CT andQuantitative imaging in medicine and surgery · 2026Article
- Spectral computed tomography-based quantitative parameters combined with extracellular volume fraction to predict lymph node metastases in gastric cancer.European radiology · 2025Article
- The value of dual-energy spectral CT in differentiating the pathological grades of T1-size lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Spectral computed tomography iodine concentration spatial distribution and multi-parameter quantitative analysis in the differential diagnosis of central small cell lung cancer from squamous cell carcinoma.Translational cancer research · 2025Article
- Spectral computed tomography parameters for predicting vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma: a pilot study.Quantitative imaging in medicine and surgery · 2025Article
- Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.Translational lung cancer research · 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
- Feasibility of using 8 mL of iodinated contrast media in cerebral computed tomographic angiography with a dual-layer spectral detector.Quantitative imaging in medicine and surgery · 2024Article
- Comparison of the diagnostic efficacy between imaging features and iodine density values for predicting microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2024Article
- Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer.Insights into imaging · 2023Article
- Can spectral computed tomography (CT) replace perfusion CT to assess the histological classification of non-small cell lung cancer?Quantitative imaging in medicine and surgery · 2023Article
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Abstract
Background: It is difficult to distinguish the pathological grade of lung adenocarcinoma (LUAD) with traditional computed tomography (CT). The aim of this study was to assess tumor differentiation by dual-layer spectral detector CT combined with morphological parameters. Methods: In this prospective study, a total of 67 patients with pathologically diagnosed LUAD were enrolled: 39 patients in the well- and moderately-differentiated group (14 and 25 patients, respectively) and 28 patients in the poorly-differentiated group. Morphological parameters, non-enhanced CT number, double-enhanced CT number, effective atomic number, monoenergetic CT images (40 and 70 keV), iodine density, and thoracic aorta iodine density of tumors were measured. The slope of the spectral curve and normalized iodine density were calculated. The diagnostic efficiency of the spectral parameters alone, and the combined spectral and morphological parameters were obtained by statistical analysis. Results: The morphological signs of LUAD (the vessel convergence sign, bronchus encapsulated air sign, and liquefactive necrosis) were higher in the poorly-differentiated group than in the well-moderately-differentiated group (57.1% Conclusions: The parameters of dual-layer spectral detector CT can help discriminate the pathological grade of LUAD. Among the spectral parameters, the normalized iodine density in the arterial phase has the best diagnostic efficiency, and the combination of spectral and morphological parameters improves the pathological grading of LUAD.
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