CT影像组学对结直肠癌肝转移的诊断价值

doi:10.12117/jccmi.2018.11.008

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摘要目的：探讨基于结直肠癌CT图像的影像组学方法在结直肠癌肝转移的诊断价值。方法：回顾性分析2017年6—12月吉林大学第一医院经手术病理证实为结直肠癌，并具有完整的术前结肠CT影像资料及术前常规检查等临床资料齐全的100例患者（肝转移、非肝转移患者各50例），按照4∶1的比例由计算机软件随机将患者分为建模组80例，测试组20例。采用Matlab 2017a软件和Python软件的特征提取算法提取影像组学特征，进行特征筛选并建立影像组学标签。通过纳入影像组学标签及患者的临床资料建立多变量随机森林分类器模型（Random forest classifier，RFC），并进行模型优化及验证，采用留出法、交叉验证法评价模型效能。结果：肝转移组与非肝转移组患者影像组学标签有显著差异（P<0.05）。影像组学标签、癌胚抗原（CEA）、糖类抗原19-9（CA19-9）表达水平均与结直肠癌肝转移诊断成正相关（P均<0.05）。RFC将建模组中训练集与验证集比例界定为7∶3，模型准确率为81.5%，训练集中ROC下面积（AUC）为0.991，敏感度为84.0%，特异度为96.8%，阳性预测值0.955，阴性预测值0.882；验证集中AUC为0.811，敏感度为72.7%，特异度为92.3%，阳性预测值为0.889，阴性预测值为0.800；RFC在十折交叉验证中准确率为81.0%。RFC对于测试组准确率为75.0%。结论：通过基于结直肠癌CT图像获得的影像组学标签及临床资料（CEA、CA19-9表达水平）建立的RFC，对于术前诊断结直肠癌肝转移有应用价值。

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	郭钰1
	李明洋2
	刘祥春1
	王鸣飞1
	李雪妍2
	张惠茅1

关键词 ：结直肠肿瘤, 肝肿瘤, 肿瘤转移, 体层摄影术, X线计算机

Abstract：Objective: To explore the predictive value of a CT-based radiomics for liver metastasis in colorectal cancer. Methods: In the retrospective study, 100 patients with pathologically confirmed by surgery to colorectal cancer and preoperative contrast-enhanced CT examination in the First Hospital of Jilin University from June to December 2017 were included(50 cases with liver metastasis; 50 cases without liver metastasis). The patients were divided into modeling group(80 cases) and testing group(20 cases) by computer random software according to the ratio of 4∶1. Using the software matlab 2017a and python to extract a list of radiomics features and construct the corresponding radiomics signature. The radiomics signature and clinical variable were included to establish multivariable random forest classifier(RFC) model that was simplified and validated. The efficiency of the model was evaluated by the method of hold-out and cross validation. Results: The discrimination of radiomics signature between the liver metastasis group and non-liver metastasis group is significant(P<0.05). The radiomics signature, carcinoembryonic antigen(CEA), carbohydrate antigen 19-9(CA19-9) expression were showed positive correlation with the liver metastasis of colorectal cancer(P<0.05). The RFC splits modeling group into training dataset and validation dataset as 7∶3, the accuracy is 81.5%, training dataset(AUC=0.991, sensitivity=84.0%, specificity=96.8%, positive predicted value=0.955, negative predictive value=0.882) and validation dataset(AUC=0.811, sensitivity=72.7%, specificity=92.3%, positive predicted value=0.889, negative predictive value=0.800). The accuracy in ten folded cross validation is 81.0%. The accuracy of testing group is 75.0%. Conclusion: The RFC model integrated with the radiomics signature based on CT imaging and clinical characteristic can be useful for diagnosis of liver metastasis in colorectal cancer.

Key words： Colorectal neoplasms Liver neoplasms Neoplasm metastasis Tomography, X-ray computed

收稿日期: 2018-08-11

PACS:	Ｒ735.3
	R735.7
	Ｒ814.42

基金资助:吉林省财政厅项目（编号2018SCZWSZX-026）；吉林省省级产业创新专项资金项目（编号2017C020）；吉林省卫生技术创新项目（编号2017J073）；重大疾病防治科技行动计划肿瘤防治专项项目（编号ZX-07-C2016003）。

通讯作者: 张惠茅，吉林大学第一医院放射科，130021。E-mail：huimaozhanglinda@163.com

作者简介: 郭钰（1993-），女，吉林人，在读硕士研究生。E-mail：1105372728@qq.com

引用本文:

郭钰1，李明洋2，刘祥春1，王鸣飞1，李雪妍2，张惠茅1. CT影像组学对结直肠癌肝转移的诊断价值[J]. 中国临床医学影像杂志, 2018, 29(11): 798-802.
GUO Yu1, LI Ming-yang2, LIU Xiang-chun1, WANG Ming-fei1, LI Xue-yan2, ZHANG Hui-mao1. The diagnostic value of CT-based radiomics in liver metastasis of colorectal cancer. JOURNAL OF CHINA MEDICAL IMAGING, 2018, 29(11): 798-802.

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http://www.jccmi.com.cn/CN/10.12117/jccmi.2018.11.008 或 http://www.jccmi.com.cn/CN/Y2018/V29/I11/798

[1]梁立，刘天舒. 结直肠癌肝转移危险及预后因素分析[J]. 中国实用外科杂志，2016，36（4）：383-386. [2]蔡建强，蔡三军，秦新裕，等. 结直肠癌肝转移诊断和综合治疗指南（2016）[J]. 中国实用外科杂志，2016，9（1）：858-869. [3]Ludwig DR, Mintz AJ, Sanders VR, et al. Liver imaging for colorectal cancer metastases[J]. Cur Colorectal Cancer Rep, 2017, 13(6): 470-480. [4]Sivesgaard K, Larsen LP, Sorensen M, et al. Diagnostic accuracy of CE-CT, MRI and FDG PET/CT for detecting colorectal cancer liver metastases in patients considered eligible for hepatic resection and/or local ablation[J]. Eur Radiol, 2018, 5(7): 1432-1444. [5]Gillies RJ, Kinahan PE, Hricak H, et al. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. [6]Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. [7]吴佩琪，刘再毅，何兰，等. 影像组学与大数据结合的研究现状[J]. 中华放射学杂志，2017，51（7）：554-558. [8]Aerts H, Velazquez ER, Leijenaar RTH, et al. Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014, 3(5): 4644-4652. [9]李华秀，李振辉，王关顺，等. 影像组学在消化道系统的应用进展[J]. 中国临床医学影像杂志，2017，28（9）：672-679. [10]何兰，黄燕琪，马泽兰，等. CT影像组学在非小细胞肺癌临床分期中的价值[J]. 中华放射学杂志，2017，51（12）：906-911. [11萨莎，李晶，李晓东，等. 基于CT图像及临床资料的随机森林模型对结直肠癌术前T分期的诊断价值[J]. 中华放射学杂志，2017，51（12）：933-938. [12]周志华. 机器学习[M]. 北京：清华大学出版社，2016：25-27. [13]Watanabe T, Itabashi M, Shimada Y, et al. Japanese society for cancer of the colon and rectum(JSCCR) guidelines 2014 for treatment of colorectal cancer[J]. Int J Clin Oncol, 2015, 20(2): 207-239. [14]Russo M, Siravegna G, Blaszkowsky LS, et al. Tumor heterogeneity and lesion-specific response to targeted therapy in colorectal cancer[J]. Cancer Discov, 2016, 6(2): 147-153. [15]Adam R, de Gramont A, Figueras J, et al. Managing synchronous liver metastases from colorectal cancer: A multidisciplinary international consensus[J]. Cancer Treat Rev, 2015, 41(9): 729-741. [16]Rianne CJ, Doenja MJ, Roald SS, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases—A multicenter study[J]. Eur Radiol, 2017, 9(2): 64-71. [17]Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review[J]. JAMA Oncol, 2016, 2(12): 1636-1642. [18]Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging(radiomics) in oncology[J]. Ann Oncol, 2017, 28(6): 1191-1206.