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使用表观扩散系数图的全容积直方图分析评估胃癌的组织学分化

Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps.

作者信息

Zhang Yujuan, Chen Jun, Liu Song, Shi Hua, Guan Wenxian, Ji Changfeng, Guo Tingting, Zheng Huanhuan, Guan Yue, Ge Yun, He Jian, Zhou Zhengyang, Yang Xiaofeng, Liu Tian

机构信息

Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China.

Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China.

出版信息

J Magn Reson Imaging. 2017 Feb;45(2):440-449. doi: 10.1002/jmri.25360. Epub 2016 Jul 1.

Abstract

PURPOSE

To investigate the efficacy of histogram analysis of the entire tumor volume in apparent diffusion coefficient (ADC) maps for differentiating between histological grades in gastric cancer.

MATERIALS AND METHODS

Seventy-eight patients with gastric cancer were enrolled in a retrospective 3.0T magnetic resonance imaging (MRI) study. ADC maps were obtained at two different b values (0 and 1000 sec/mm ) for each patient. Tumors were delineated on each slice of the ADC maps, and a histogram for the entire tumor volume was subsequently generated. A series of histogram parameters (eg, skew and kurtosis) were calculated and correlated with the histological grade of the surgical specimen. The diagnostic performance of each parameter for distinguishing poorly from moderately well-differentiated gastric cancers was assessed by using the area under the receiver operating characteristic curve (AUC).

RESULTS

There were significant differences in the 5 , 10 , 25 , and 50 percentiles, skew, and kurtosis between poorly and well-differentiated gastric cancers (P < 0.05). There were correlations between the degrees of differentiation and histogram parameters, including the 10 percentile, skew, kurtosis, and max frequency; the correlation coefficients were 0.273, -0.361, -0.339, and -0.370, respectively. Among all the histogram parameters, the max frequency had the largest AUC value, which was 0.675.

CONCLUSION

Histogram analysis of the ADC maps on the basis of the entire tumor volume can be useful in differentiating between histological grades for gastric cancer.

LEVEL OF EVIDENCE

4 J. Magn. Reson. Imaging 2017;45:440-449.

摘要

目的

探讨表观扩散系数(ADC)图中整个肿瘤体积的直方图分析在鉴别胃癌组织学分级中的效能。

材料与方法

纳入78例胃癌患者进行回顾性3.0T磁共振成像(MRI)研究。为每位患者在两个不同的b值(0和1000 sec/mm²)下获取ADC图。在ADC图的每一层上勾勒出肿瘤,随后生成整个肿瘤体积的直方图。计算一系列直方图参数(如偏度和峰度),并将其与手术标本的组织学分级相关联。通过使用受试者操作特征曲线(AUC)下的面积评估每个参数区分低分化和中分化胃癌的诊断性能。

结果

低分化和高分化胃癌在第5、10、25和50百分位数、偏度和峰度方面存在显著差异(P < 0.05)。分化程度与直方图参数之间存在相关性,包括第10百分位数、偏度、峰度和最大频率;相关系数分别为0.273、 - 0.361、 - 0.339和 - 0.370。在所有直方图参数中,最大频率的AUC值最大,为0.675。

结论

基于整个肿瘤体积的ADC图直方图分析有助于鉴别胃癌的组织学分级。

证据水平

4 J. Magn. Reson. Imaging 2017;45:440 - 449。

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