ArticleEuropean journal of radiology open2024
The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA.
Article in European journal of radiology open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A radiomics-based machine learning model for the preoperative differentiation of lung adenocarcinoma subtypes.Quantitative imaging in medicine and surgery · 2026Article
- The value of relative CT attenuation in predicting invasiveness in patients with T1-stage lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Integrating deep learning and radiomics in the differentiation of major histological subtypes of invasive non-mucinous lung adenocarcinoma using positron emission tomography and computed tomography.Translational lung cancer research · 2025Article
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4 authors.
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Abstract
Objective: This study aims to analyze the quantitative parameters and morphological indices of three-dimensional (3D) visualization to differentiate lepidic predominant adenocarcinoma (LPA) from non-LPA subtypes, which include acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), micropapillary predominant adenocarcinoma (MPA), and solid predominant adenocarcinoma (SPA). Methods: A group of 178 individuals diagnosed with lung adenocarcinoma were chosen and categorized into two groups: the LPA group and the non-LPA group, according to their pathological results. Quantitative parameters and morphological indexes such as 3D volume, solid proportion, and vascular cluster sign were obtained using 3D visualization and reconstruction techniques. Results: Significant differences were observed in the vascular cluster sign, spiculation, shape, air bronchogram, bubble-like lucency, margin, pleural indentation, lobulation, maximum tumor diameter, 3D mean CT value, 3D volume, 3D mass, 3D density, and solid proportion between two groups (P<0.05). The optimal cut-off values for diagnosing non-LPA were a 3D mean CT value of -445.45 HU, a 3D density of 0.56 mg·mm Conclusion: The utilization of 3D visualization technology in a systematic approach enables the acquisition of 3D quantitative parameters and morphological indicators of thin-slice CT lesions. These efforts significantly contribute to the identification of histopathological subtypes for stageⅠinvasive lung adenocarcinoma. When integrated with pertinent clinical data, this offers essential guidance for developing various surgical techniques and treatment plans.
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