Chen Jinxin, Zeng Xinyi, Li Feng, Peng Jidong
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 China.
Eur J Radiol Open. 2024 Sep 21;13:100600. doi: 10.1016/j.ejro.2024.100600. eCollection 2024 Dec.
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).
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.
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, 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).
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.
本研究旨在分析三维（3D）可视化的定量参数和形态学指标，以鉴别鳞屑状为主型腺癌（LPA）与非LPA亚型，后者包括腺泡为主型腺癌（APA）、乳头为主型腺癌（PPA）、微乳头为主型腺癌（MPA）和实体为主型腺癌（SPA）。
选取178例经诊断为肺腺癌的患者，根据其病理结果分为两组：LPA组和非LPA组。采用3D可视化和重建技术获取三维体积、实性比例和血管簇征等定量参数和形态学指标。
两组在血管簇征、毛刺征、形态、空气支气管征、气泡样透亮区、边缘、胸膜凹陷、分叶、最大肿瘤直径、三维平均CT值、三维体积、三维肿块、三维密度和实性比例方面存在显著差异（P<0.05）。诊断非LPA的最佳截断值为三维平均CT值-445.45 HU、三维密度0.56 mg·mm和实性比例达到27.95 %。多因素logistic回归分析显示，三维平均CT值、分叶和边缘特征可独立预测Ⅰ期浸润性肺腺癌。三项指标联合可显著提高预测准确性（AUC=0.881）。
系统应用3D可视化技术能够获取薄层CT病变的三维定量参数和形态学指标。这些成果对Ⅰ期浸润性肺腺癌组织病理学亚型的鉴别有显著帮助。与相关临床数据相结合时，可为制定各种手术技术和治疗方案提供重要指导。
