Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors.
Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors.
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DOI：
10.1016/j.lungcan.2010.03.009
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发表时间：
2010-12
期刊：
影响因子：
5.3
通讯作者：
M. Yanagawa;Y. Tanaka;M. Kusumoto;Shun-ichi Watanabe;R. Tsuchiya;O. Honda;H. Sumikawa;A. Inoue;M. Inoue;M. Okumura;N. Tomiyama;T. Johkoh
中科院分区：
文献类型：
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作者：
M. Yanagawa;Y. Tanaka;M. Kusumoto;Shun-ichi Watanabe;R. Tsuchiya;O. Honda;H. Sumikawa;A. Inoue;M. Inoue;M. Okumura;N. Tomiyama;T. Johkoh
ESSETo评估一个定制开发的软件，用于分析恶性程度的小周围腺癌的体积CT数据相比，病理预后factors.MATERIALS和METHODSForsix腺癌的直径为2cm或更少的46例患者被包括在内。定制开发的软件可以计算固体部分占整个结核的体积比率，即使固体部分显示点状分布，并根据固体部分的体积比率自动将结核分为以下六种类型：1型，纯磨玻璃状混浊（GGO）; 2型，微固体; 3型，具有GGO晕的小固体部分; 4型为混合型，主要表现为GGO和实性部分组成的区域，实性部分有气支气管征或呈点状分布，5型为大实性部分伴GGO晕，6型为纯实性。采用两种阈值选择方法对灰度图像进行分割，确定CT图像上实体部分与GGO的边界。放射科医生还检查了实体部分与总不透明度的二维比率（2D % solid），这已经在之前的报告中得到证实。结果软件确定的分类与放射科医生确定的分类之间存在良好的一致性（加权kappa=0.778-0.804）。多因素Logistic回归分析显示，2D % solid和计算机自动分类在评估淋巴管浸润方面均具有显著的意义。（p=0.0007，0.0027），血管浸润（p=0.003，0.012），胸膜浸润（p=0.021，0.025）.结论应用我们开发的软件预测周围型小腺癌的病理预后因素是可行的。
PURPOSETo evaluate a custom-developed software for analyzing malignant degrees of small peripheral adenocarcinomas on volumetric CT data compared to pathological prognostic factors.MATERIALS AND METHODSForty-six adenocarcinomas with a diameter of 2cm or less from 46 patients were included. The custom-developed software can calculate the volumetric rates of solid parts to whole nodules even though solid parts show a punctate distribution, and automatically classify nodules into the following six types according to the volumetric rates of solid parts: type 1, pure ground-glass opacity (GGO); type 2, semiconsolidation; type 3, small solid part with a GGO halo; type 4, mixed type with an area that consisted of GGO and solid parts which have air-bronchogram or show a punctate distribution; type 5, large solid part with a GGO halo; and type 6, pure solid type. The boundary between solid portion and GGO on CT was decided using two threshold selection methods for segmenting gray-scale images. A radiologist also examined two-dimensional rates of solid parts to total opacity (2D%solid) which was already confirmed with previous reports.RESULTSThere were good agreements between the classification determined by the software and radiologists (weighted kappa=0.778–0.804). Multivariate logistic regression analyses showed that both 2D%solid and computer-automated classification were significantly useful in estimating lymphatic invasion (p=0.0007, 0.0027), vascular invasion (p=0.003, 0.012), and pleural invasion (p=0.021, 0.025).CONCLUSIONUsing our custom-developed software, it is feasible to predict the pathological prognostic factors of small peripheral adenocarcinomas.