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基于 CT 特征和纹理分析预测胰腺神经内分泌肿瘤分级。

Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis.

机构信息

1 Department of Radiology, Division of Abdominal Imaging and Interventional Radiology, Massachusetts General Hospital, White 270, 55 Fruit St, Boston, MA 02114.

出版信息

AJR Am J Roentgenol. 2018 Feb;210(2):341-346. doi: 10.2214/AJR.17.18417. Epub 2017 Nov 15.

Abstract

OBJECTIVE

The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery.

MATERIALS AND METHODS

Preoperative contrast-enhanced CT images of 101 patients with PNETs were assessed. The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection. Data were analyzed with chi-square tests, kappa statistics, logistic regression analysis, and Kaplan-Meier curves.

RESULTS

The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors. Differences in progression-free survival distribution were found for grade 1 versus grades 2 and 3 tumors (χ [df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ [df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ (df, 1) = 4.4; p = 0.037).

CONCLUSION

CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection.

摘要

目的

本研究旨在评估 CT 纹理分析和 CT 特征是否能预测基于世界卫生组织(WHO)分类的胰腺神经内分泌肿瘤(PNET)分级,并确定与术后疾病进展相关的特征。

材料与方法

对 101 例 PNET 患者的术前增强 CT 图像进行评估。对肿瘤位置、肿瘤大小、肿瘤形态、以实性为主或囊性为主的成分、钙化、增强后不均匀强化、胰管扩张、胰腺萎缩、肿瘤血管受累、淋巴结肿大等进行评估。还从 CT 图像中提取纹理特征。经手术证实的肿瘤根据 WHO 分级进行分级,患者在手术后接受 CT 或 MRI 随访。采用卡方检验、kappa 统计、logistic 回归分析和 Kaplan-Meier 曲线进行数据分析。

结果

预测侵袭性肿瘤(2 级和 3 级)的 CT 特征包括肿瘤大小大于 2.0cm(比值比[OR],3.3;p=0.014)、血管受累(OR,25.2;p=0.003)、胰管扩张(OR,6.0;p=0.002)和淋巴结肿大(OR,6.8;p=0.002)。纹理参数熵(OR,3.7;p=0.008)也可预测侵袭性肿瘤。1 级与 2 级和 3 级肿瘤(χ[df,1]=21.6;p<0.001)、有血管受累的 PNET (χ[df,1]=20.8;p<0.001)和熵(空间尺度滤波器 2)值大于 4.65 的肿瘤(χ(df,1)=4.4;p=0.037)之间,无进展生存分布存在差异。

结论

CT 纹理分析和 CT 特征可预测 PNET 的侵袭性,并可用于识别术后早期疾病进展风险较高的患者。

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