[1]王 刚,余芬芬,吴依芬,等.基于MRI成像的影像组学特征识别肺癌肺外转移特性的价值[J].医学信息,2022,35(14):41-44.[doi:10.3969/j.issn.1006-1959.2022.14.008]
　WANG Gang,YU Fen-fen,WU Yi-fen,et al.Value of Radiomic Features Based on MRI in Identifying Extrapulmonary Metastasis of Lung Cancer[J].Medical Information,2022,35(14):41-44.[doi:10.3969/j.issn.1006-1959.2022.14.008]
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基于MRI成像的影像组学特征识别肺癌肺外转移特性的价值()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
35卷
期数:
2022年14期
页码:
41-44
栏目:
论著
出版日期:
2022-07-15

文章信息/Info

Title:
Value of Radiomic Features Based on MRI in Identifying Extrapulmonary Metastasis of Lung Cancer
文章编号:
1006-1959（2022）14-0041-04
作者:
王 刚; 余芬芬; 吴依芬; 等
（1.东莞市人民医院放射科，广东 东莞 523059；2.东莞市人民医院肿瘤内科，广东 东莞 523059；3.通用电气药业<上海>有限公司，广东 广州 510526）
Author(s):
WANG Gang; YU Fen-fen; WU Yi-fen; et al.
(1.Department of Radiology,Dongguan People’s Hospital,Dongguan 523059,Guangdong,China;2.Department of Oncology,Dongguan People’s Hospital,Dongguan 523059,Guangdong,China;3.General Electric Pharmaceutical Co., Ltd.,Guangzhou 510526,Guangdong,China)
关键词:
肺肿瘤; 转移; 磁共振成像; 影像组学特征
Keywords:
Lung tumor; Metastasis; Magnetic resonance imaging; Radiomics features
分类号:
R734.2
DOI:
10.3969/j.issn.1006-1959.2022.14.008
文献标志码:
A
摘要:
目的 探讨基于肺癌原发灶MRI图像的影像组学技术识别肺癌肺外转移的价值。方法 回顾性收集2019年10月1日-2020年1月1日东莞市人民医院经病理证实的肺内占位性病变患者的临床资料和胸部MRI检查图像，将恶性肿瘤患者是否发生转移分为转移组和非转移组。选取病变T2WI、DWI（b=500）、ADC三组序列图像分别导入ITK-SNAP软件中，人工对病灶进行手动逐层标记，采用A.K软件对获得的病灶三维分割图像进行特征提取，每个病灶提取5大类共396个特征，采用R语言对临床资料和影像组学特征进行组间差异统计分析，对于具有统计学意义的影像组学特征，绘制箱图和识别肺癌转移的ROC曲线。结果 肺内占位18例，共18个病灶，转移组10例，非转移组8例，共获得有意义的影像组学特征8个，均来源于DWI和ADC序列；8个特征识别肺癌肺外转移的ROC曲线下面积（AUC）均大于0.75（0.781~0.906），其中ADC_Grey Level Nonuniformity_All Direction_offset4_SD特征AUC最大，准确性0.722~0.889，敏感性和特异性范围均在0.6~1。结论 应用影像组学技术针对肺癌原发灶的MRI图像进行分析，能够提取一定的影像组学特征识别肺癌肺外的转移特性，在肺癌转移的预测方面有一定的价值。
Abstract:
Objective To investigate the value of radiomics technology in identifying extrapulmonary metastasis of lung cancer based on MRI images of primary lung cancer.Methods The clinical data and chest MRI images of patients with intrapulmonary space-occupying lesions confirmed by pathology in Dongguan People’s Hospital from October 1, 2019 to January 1, 2020 were retrospectively collected. The patients with malignant tumors were divided into metastasis group and non-metastasis group. Three groups of sequence images of T2WI, DWI (b=500) and ADC were selected and imported into ITK-SNAP software, respectively. The lesions were manually labeled layer by layer, and the feature extraction of the obtained three-dimensional segmentation image of the lesions was carried out by A. K software. A total of 396 features were extracted from each lesion in five categories. The R language was used to statistically analyze the differences between the clinical data and the radiomic features. For the radiomic features with statistical significance, the box diagram and the ROC curve for identifying lung cancer metastasis were plotted.Results There were 18 lesions in 18 patients, including 10 cases in metastasis group and 8 cases in non-metastasis group. A total of 8 imaging omics features were obtained from DWI and ADC sequences.The area under ROC (AUC) of the 8 features were above 0.75 (0.781-0.906), and the AUC value of ADC_ GreyLevelNonuniformity_ AllDirection_ offset4_ SD was the largest. The accuracy ranged from 0.722 to 0.889, and the sensitivity and specificity ranged from 0.6 to 1.Conclusion The application of radiomics based on MRI images of primary lesion of lung cancer can extract certain radiomic features and identify the extra-pulmonary metastasis characteristics of lung cancer, which has a certain value in the prediction of lung cancer metastasis.

参考文献/References:

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更新日期/Last Update: 1900-01-01