The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA
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 adenocarci...
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Elsevier
2024-12-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047724000558 |
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| author | Jinxin Chen Xinyi Zeng Feng Li Jidong Peng |
| author_facet | Jinxin Chen Xinyi Zeng Feng Li Jidong Peng |
| author_sort | Jinxin Chen |
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| description | 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−3, and a solid proportion reaching 27.95 %. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881). 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. |
| format | Article |
| id | doaj-art-29560f55174642cea69c41f363840a56 |
| institution | Kabale University |
| issn | 2352-0477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-29560f55174642cea69c41f363840a562024-12-15T06:15:48ZengElsevierEuropean Journal of Radiology Open2352-04772024-12-0113100600The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPAJinxin Chen0Xinyi Zeng1Feng Li2Jidong Peng3Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR ChinaGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR ChinaGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR ChinaCorrespondence to: No.16, MeiGuan Avenue, Ganzhou 341000, PR China.; Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR ChinaObjective: 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−3, and a solid proportion reaching 27.95 %. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881). 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.http://www.sciencedirect.com/science/article/pii/S2352047724000558Lung cancerAdenocarcinomaHistological subtypeThree-dimensional visualizationLepidic predominant adenocarcinoma |
| spellingShingle | Jinxin Chen Xinyi Zeng Feng Li Jidong Peng The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA European Journal of Radiology Open Lung cancer Adenocarcinoma Histological subtype Three-dimensional visualization Lepidic predominant adenocarcinoma |
| title | The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA |
| title_full | The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA |
| title_fullStr | The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA |
| title_full_unstemmed | The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA |
| title_short | The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA |
| title_sort | value of non enhanced ct 3d visualization in differentiating stage i invasive lung adenocarcinoma between lpa and non lpa |
| topic | Lung cancer Adenocarcinoma Histological subtype Three-dimensional visualization Lepidic predominant adenocarcinoma |
| url | http://www.sciencedirect.com/science/article/pii/S2352047724000558 |
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