非增强 CT 三维成像在区分Ⅰ期浸润性肺腺癌(LPA)和非 LPA 中的价值

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Journal of Radiology Open Pub Date : 2024-09-21 DOI:10.1016/j.ejro.2024.100600
Jinxin Chen, Xinyi Zeng, Feng Li, Jidong Peng
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引用次数: 0

摘要

目的 本研究旨在分析三维(3D)可视化的定量参数和形态学指标,以区分鳞状占位腺癌(LPA)和非LPA亚型,非LPA亚型包括针状占位腺癌(APA)、乳头状占位腺癌(PPA)、微乳头状占位腺癌(MPA)和实变占位腺癌(SPA)。方法 选择 178 例确诊为肺腺癌的患者,根据病理结果将其分为两组:LPA 组和非 LPA 组。结果两组患者的血管团征、棘点、形态、气管图、气泡样通明、边缘、胸膜压痕、分叶、肿瘤最大直径、三维平均 CT 值、三维体积、三维质量、三维密度、实性比例等定量参数和形态学指标差异显著(P<0.05)。诊断非 LPA 的最佳临界值为三维 CT 平均值为 -445.45 HU、三维密度为 0.56 mg-mm-3、实性比例达到 27.95 %。多变量逻辑回归分析显示,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。结论通过系统地利用三维可视化技术,可获得薄层 CT 病灶的三维定量参数和形态学指标。这些工作大大有助于确定Ⅰ期浸润性肺腺癌的组织病理学亚型。结合相关临床数据,这为制定各种手术技术和治疗方案提供了重要指导。
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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 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.

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来源期刊
European Journal of Radiology Open
European Journal of Radiology Open Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.10
自引率
5.00%
发文量
55
审稿时长
51 days
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