CT影像组学预测胃癌不同预后组织学分型的可行性

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (627 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Objective: To explore the application value of CTbased radiomic nomogram in predicting histological classification of gastric cancer with different prognosis. Methods: A total of 570 patients with gastric cancer were analyzed retrospectively，and divided into poor prognosis group and good prognosis group. All the samples were randomly divided into training cohort and validation cohort in the proportion of 2 ∶1. Standardized CT images were segmented by radiologists with ITKSNAP software manually.The clinical features with the greatest correlation with histological classification of gastric cancer were screened by univariate analysis, and used as parameters to construct the clinical feature model. Features are extracted from all segmented images, and minimum Redundancy Maximum Relevance (mRMR), Least Absolute Shrinkage and Selection Operator (LASSO) regression and stepwise regression were used to screen the redundant radiomic features. The effective characteristics were used to establish radiomic signature.Finally, the clinical features closely related to histological classification were fitted, and radiomic nomogram was constructed.The performance of each model was evaluated by the area under ROC curve (AUC). Results: A total of 985 features were extracted from all the segmentation region, which included first order statistics , shape features, texture features, wavelet features and other filtering feature. After the screening of redundant features, five key features were obtained, which were used as the parameters of radiomic signature. Three clinical characteristics (age, sex, CTM) were selected as the parameters of the clinical feature model. Compared with other two models (clinical feature model and radiomic signature), the classification performance of radiomic nomogram is the best.The AUC of radiomic nomogram in training cohort and validation cohort was 0.726 2 (95%CI:0.676-0.776) and 0.707 0(95%CI:0.632-0.781). Conclusion: CT-based radiomic nomogram has certain potentiality in noninvasive predicting different prognostis related to histological classification of gastric cancer before operation.

Received: 31 January 2020

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	CHEN Jian1
	2
	HUANG Hai-xia2
	LU Chao3
	WANG Xiao-xiao3
	DING Yi1
	SHAN Xiu-hong3

Cite this article:

CHEN Jian1,2,HUANG Hai-xia2等.

The feasibility of CT-based radiomic nomogram for predicting histological classification of gastric cancer with different prognosis

[J]. Journal of Jiangsu University(Medicine Edition), 2020, 30(04): 351-356.

URL:

http://zzs.ujs.edu.cn/xbyxb/EN/ OR http://zzs.ujs.edu.cn/xbyxb/EN/Y2020/V30/I04/351

［1］Gao JP, Xu W, Liu WT,et al.Tumor heterogeneity of gastric cancer: From the perspective of tumorinitiating cell\[J\].World J Gastroenterol,2018,24(24): 2567-2581.

［2］Kang WM，Meng QB，Yu JC，et al．Factors associated with early recurrence after curative surgery for gastric cancer\[J\]．World J Gastroenterol，2015，21(19)：5934-5940.

［3］Degiuli M，Calvo F.Survival of early gastric cancer in specialized European center.Which lymphadenectomy is necessary? \[J\].World J Surg,2006,30(12):2193-2203.

［4］Liu L,Wang ZW, Ji J,et al.A cohort study and metaanalysis between histopathological classification and prognosis of gastric carcinoma\[J\]. Anticancer Agents Med Chem, 2013，13(2):27-34.

［5］Lai JF,Xu WN,Noh SH,et al.Effect of World Health Organization (WHO) histological classification on predicting lymph node metastasis and recurrence in early gastric cancer\[J\]. Med Sci Monit, 2016, 22: 3147-3153.

［6］凌斌勋，陈环球，徐新宇，等．３３例胃印戒细胞癌的临床分析［Ｊ］．临床肿瘤学杂志，2011，16（11）：1013-1015.

［7］Kumar V, Gu Y, Basu S, et al. Radiomics:the process and the challenges\[J\]. Magn Reson Imaging, 2012,30(8):1234-1248.

［8］Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures,they are data\[J\]. Radiology,2016, 278(2):563-577.

［9］Peng H, Dong D, Fang MJ, et al. Prognostic value of deep learning PET/CTbased radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma\[J\].Clin Cancer Res, 2019,25(14):4271-4279.

［10］Song JD, Shi JY, Dong D, et al. A new approach to predict progressionfree survival in stage Ⅳ EGFRmutant NSCLC patients with EGFRTKI therapy\[J\]. Clin Cancer Res, 2018,24(15): 3583-3592.

［11］Ma Z, Fang M, Huang Y, et al. CTbased radiomics signature for differentiating Borrmann typeⅣ gastric cancer from primary gastric lymphoma\[J\]. Eur J Radiol, 2017, 91(23): 142-147.

［12］BaSsalamah A, Muin D, Schernthaner R, et al. Texturebased classification of different gastric tumors at contrastenhanced CT\[J\].Radiology, 2013, 82(10): 537-543.

［13］Dong D, Tang L,Li ZY, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer\[J\].Ann Oncol，2019，30（3）：431-438.

［14］焦旭光，梁寒，邓靖宇，等.混合组织学类型胃癌临床病理特点及预后分析\[J\].中华胃肠外科杂志，2013，16（3）：260-263.

［15］于颖彦，朱正纲. 2010 版WHO 胃肿瘤临床病理学分类及分期解读\[J\]. 外科理论与实践，2011，16 （5）：508-512.

［16］张云，姚萍，崔旻，等.临床病理特征与早期胃癌分化程度的关系\[J\].中华实用诊断与治疗杂志，2019，33（5）：443-445.

［17］Ho SWT, Tan P.Dissection of gastric cancer heterogeneity for precision oncology\[J\].Cancer Sci,2019,110(11):1-10.

［18］邹茹欣，周洋，巴志昌，等．进展期胃癌64排螺旋CT三期增强影像强化特点与组织学分型相关性的研究\[J\].实用肿瘤学杂志，2013，27（6）：549-553.

［19］高剑波，杨学华，李萌太，等.进展期与早期胃癌螺旋CT三期增强的诊断价值［Ｊ］.中华放射学杂志，2001，35（4）：253-257.

［20］Liu S, Liu S, Ji C, et al. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers\[J\]. Eur Radiol, 2017, 27(12): 4951-4959.

［21］Lin X, Yang F, Zhou L,et al. A support vector machinerecursive feature elimination feature selection method based on artificial contrast variables and mutual information\[ J\]. J Chromatogr B, 2012, 910: 149-155.

［22］郭震，王春祥，王培林，等.胃癌患者血浆胆固醇、LDL与胃癌组织病理分型的相关性研究\[J\].山东大学学报（医学版），2007，45（2）: 160-162.