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Received: 10 February 2021
|
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|
|
|
|
| [1] |
Bray F, Ferlay J, Soerjomataram I, et al .Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424.
|
| [2] |
Chen W Q,Zheng R S,Baade P D, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
|
| [3] |
Dong Y, Feng Q, Yang W,et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J]. Eur Radiol, 2018, 28(2): 582-591.
|
| [4] |
Wang Jun, Liu Xia, Dong Di, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method[C]// 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL, USA: IEEE, 2016: 1272-1275.
|
| [5] |
Grootjans W, Tixier F, van der Vos C S, et al. The impact of optimal respiratory gating and image noise on evaluation of intratum or heterogeneity on 18F-FDG PET imaging of lung cancer[J]. J Nucl Med, 2016, 57(11): 1692-1698.
|
| [6] |
Hatt M, Tixier F, Pierce L, et al. Characterization of PET/CT images using texture analysis: the past, the present any future?[J]. Eur J Nucl Med Mol Imaging, 2017, 44(1): 151-165.
|
| [7] |
Gnep K,Fargeas A,Gutierrez-Carvajal R E,et al.Haralick texturalfeatures on T2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer[J].J Magn Reson Imaging, 2017, 45(1): 103-117.
|
| [8] |
王 敏,宋 彬,黄子星,等.大数据时代的精准影像医学:放射组学[J].中国普外基础与临床杂志, 2016,23(6): 752-755.
|
| [9] |
刘 慧,王小宜,龙学颖.基于CT图像纹理分析肿瘤异质性的研究进展及应用[J].国际医学放射学杂志, 2016,39(5): 543-548.
|
| [10] |
Yang L, Dong D, Fang M, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?[J]. Eur Radiol, 2018, 28(5): 2058-2067.
|
| [11] |
王国蓉.CT纹理分析技术对评估结直肠癌KRAS基因突变的可行性研究[D].北京:协和医学院, 2019.
|
| [12] |
黄晓媚.基于CT影像组学术前评估结直肠腺癌组织学分级的研究[D].广州:南方医科大学, 2019.
|
| [13] |
Liang C S, Huang Y Q, He L, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stageⅠ-Ⅱand stageⅢ-Ⅳcolorectal cancer[J]. Oncotarget, 2016, 7(21): 31401-31412.
|
| [14] |
Liu L, Liu Y, Xu L, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer [J]. J Magn Reson Imaging, 2017, 45(6): 1798-1808.
|
| [15] |
Huang Y Q, Liang C H, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. J Clin Oncol, 2016, 34(18): 2157-2164.
|
| [16] |
Manfredi S, Lepage C M, Hatem C, et al. Epidemiology and management of liver metastases fromcolorectal cancer [J]. Ann Surg, 2006, 244(2): 254-259.
|
| [17] |
黄燕琪.基于结直肠癌原发病灶的CT纹理分析在检测同时性肝转移中的临床应用研究[D].广州:南方医科大学, 2016.
|
| [18] |
韩 哲,邵国良,庞佩佩.纹理分析及影像组学在直肠癌新辅助放化疗评估中的研究进展[J].医学影像学杂志, 2019, 29(9): 1582-1586.
|
| [19] |
Ke N, Shi L M, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI[J]. J Clin Oncol, 2016, 22(21): 5256-5264.
|
| [20] |
Liu Z Y, Zhang X Y, Shi Y J, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. J Clin Oncol, 2017, 23(23): 7253-7262.
|
| [21] |
Liu M, Lv H, Liu L H, et al. Locally advanced rectal cancer: predicting non-responders to neoadjuvant chemoradiotherapy using apparent diffusion coefficient textures[J]. Int J Colorectal Dis, 2017, 32(7): 1009-1012.
|
| [22] |
Enkhbaatar N E, Inoue S, Yamamuro H, et al. MR imaging with apparent diffusion coefficient histogram analysis: evaluation of locally advanced rectal cancer after chemotherapy and radiation therapy[J]. Radiology, 2018, 288(1): 129-137.
|
| [23] |
Chen P J, Lin M C, Lai M J, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis[J]. Gastroenterology, 2018, 154(3): 568-575.
|
| [24] |
Xu Y, Jia Z P, Wang L B, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features [J]. BMC Bioinformatics, 2017, 18(1): 281-298.
|
|
|
2021, Vol. 32 
