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复旦大学附属肿瘤医院放射诊断科,复旦大学上海医学院肿瘤学系,上海,200032
网络出版:2019-05-24,
纸质出版:2019-05-24
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任采月,王升平,任敏,等. CT纹理分析鉴别胃肠道间质瘤c-kit/PDGFRA基因突变的初步研究[J]. 肿瘤影像学, 2019, 28(2): 101-105 https://doi.
org/10.19732/j.cnki.2096-6210.2019.02.007
任采月,王升平,任敏,等. CT纹理分析鉴别胃肠道间质瘤c-kit/PDGFRA基因突变的初步研究[J]. 肿瘤影像学, 2019, 28(2): 101-105 https://doi. DOI: 10.19732/j.cnki.2096-6210.2019.02.007.
org/10.19732/j.cnki.2096-6210.2019.02.007 DOI:
目的:
探讨CT纹理分析在鉴别胃肠道间质瘤(gastrointestinal stromal tumor,GIST)
c-kit
/血小板源性生长因子受体(platelet-derived growth factor receptor alpha,PDGFRA)基因突变类型中的价值。
方法:
回顾性分析140例经分子病理学检查证实的GIST患者的CT增强图像,其中
突变型126例,
PDGFRA
突变型14例。采用LIFEx软件对CT门静脉期图像进行分析,共提取41种纹理特征。采用Mann-Whitney
U
检验比较两组间各参数的差异并绘制受试者工作特征(receiver operating characteristic,ROC)曲线。
结果:
短区域因子(short-zone emphasis,SZE)、短区高灰度级因子(short-zone high gray-level emphasis,SZHGE)及区域百分比(zone percentage,ZP)在
突变型GIST中高于PDGFRA突变型GIST(
P
<0.05)。两组间其余纹理参数差异均无统计学意义(
>0.05)。SZE、SZHGE和ZP鉴别两者的曲线下面积(area under curve,AUC)分别为0.69、0.70和0.71(
<0.05),灵敏度分别为46.03%、47.62%及78.57%,特异度分别为92.86%、92.86%及64.29%,准确率分别为83.57%、83.57%及90.00%。
结论:
CT纹理分析能够为鉴别GIST
c-kit/PDGFRA
基因突变提 供一定的诊断信息。
Objective:
To investigate the feasibility of differentiating
/platelet-derived growth factor receptor alpha (
) gene mutations in gastrointestinal stromal tumors (GI
STs) based on CT texture analysis.
Methods:
The contrast-enhanced CT features of 140 GIST patients confirmed by molecular pathology were retrospectively analyzed (126 with
mutation
14 with
mutation). Texture features were extracted using LIFEx package. Mann-Whitney
test was used to compare the difference in the parameters between the patients with
and
mutations. The differentiation performance was evaluated by the area under curve (AUC) of receiver operating characteristic (ROC) curve.
Results:
GIST patients with
mutation exhibited higher short-zone emphasis (SZE)
higher short-zone high gray-level emphasis (SZHGE) and higher zone percentage (ZP) than those with
mutation (
<0.05). There was no statistically significant difference in other texture parameters between the two groups (
>0.05). The SZE
SZHGE and ZP successfully showed the differentiation performance between GIST patients with
mutations with AUCs of 0.69
0.70 and 0.71 (
<0.05)
sensitivities of 46.03%
47.62% and 78.57%
specificities of 92.86%
92.86% and 64.29%
accuracies of 83.57%
83.57% and 90.00%
respectively.
Conclusion:
CT texture analysis has certain values in differentiating
mutation from
mutation in GIST patients.
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