[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. doi: 10.3322/caac.21492
[2] RUSSO AE, STRONG VE. Gastric cancer etiology and management in asia and the west[J]. Annu Rev Med, 2019, 70: 353. doi: 10.1146/annurev-med-081117-043436
[3] CHEN W, ZHENG R, BAADE PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115. doi: 10.3322/caac.21338
[4] FUKAGAWA T, KATAI H, MIZUSAWA J, et al. A prospective multi-institutional validity study to evaluate the accuracy of clinical diagnosis of pathological stage Ⅲ gastric cancer (JCOG1302A)[J]. Gastric Cancer, 2018, 21(1): 68. doi: 10.1007/s10120-017-0701-1
[5] SMYTH EC, VERHEIJ M, ALLUM W, et al. Gastric cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2016, 27(Suppl 5): v38.
[6] WU S, ZHENG J, LI Y, et al. Development and validation of an mri-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer[J]. EBio Medicine, 2018, 34: 76.
[7] LUBNER MG, SMITH AD, SANDRASEGARAN K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges[J]. Radiographics, 2017, 37(5): 1483. doi: 10.1148/rg.2017170056
[8] KANESAKA T, NAGAHAMA T, UEDO N, et al. Clinical predictors of histologic type of gastric cancer[J]. Gastrointest Endosc, 2018, 87(4): 1014. doi: 10.1016/j.gie.2017.10.037
[9] 徐成, 胡月珍, 张再军, 等. CT能谱扫描及40 keV对应CT值等参数对肺内良、恶性肿块的诊疗价值分析[J]. 实用癌症杂志, 2019, 34(1): 89.
[10] LIU JY, DENG JY, ZHANG NN, et al. Clinical significance of skip lymph-node metastasis in pN1 gastric-cancer patients after curative surgery[J]. Gastroenterol Rep (Oxf), 2019, 7(3): 193. doi: 10.1093/gastro/goz008
[11] 柴亚如, 高剑波, 邢静静, 等. 能谱CT定量参数对胃癌淋巴的定性评估价值[J]. 中华胃肠外科杂志, 2017, 20(3): 309. doi: 10.3760/cma.j.issn.1671-0274.2017.03.016
[12] FEHRENBACH U, FELDHAUS F, KAHN J, et al. Tumour response in non-small-cell lung cancer patients treated with chemoradiotherapy-Can spectral CT predict recurrence?[J]. J Med Imaging Radiat Oncol, 2019, 63(5): 641. doi: 10.1111/1754-9485.12926
[13] LIU Z, WANG S, DONG D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303. doi: 10.7150/thno.30309
[14] 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. doi: 10.1038/nrclinonc.2017.141
[15] MA Z, FANG M, HUANG Y, et al. CT-based radiomics signature for differentiating Borrmann type Ⅳ gastric cancer from primary gastric lymphoma[J]. Eur J Radiol, 2017, 91: 142. doi: 10.1016/j.ejrad.2017.04.007
[16] 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. doi: 10.1007/s00330-017-4881-1
[17] XU Y, LU L, E LN, et al. Application of radiomics in predicting the malig-nancy of pulmonary nodules in different sizes[J]. AJR Am J Roentgenol, 2019, 213(6): 1213. doi: 10.2214/AJR.19.21490
[18] ZHANG R, XU L, WEN X, et al. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Quant Imaging Med Surg, 2019, 9(9): 1503. doi: 10.21037/qims.2019.09.07
[19] LI Y, ERESEN A, LU Y, et al. Radiomics signature for the preoperative assessment of stage in advanced colon cancer[J]. Am J Cancer Res, 2019, 9(7): 1429.
[20] WANG Y, LIU W, YU Y, et al. CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer[J]. Eur Radiol, 2020, 30(2): 976. doi: 10.1007/s00330-019-06398-z
[21] MA Z, LIANG C, HUANG Y, et al. Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer?[J]. Eur Radiol, 2017, 27(8): 3383. doi: 10.1007/s00330-016-4695-6
[22] LI J, DONG D, FANG M, et al. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer[J]. Eur Radiol, 2020, 30(4): 2324. doi: 10.1007/s00330-019-06621-x
[23] 陆中元, 陈兵, 刘淼, 等. CT能谱曲线对非小细胞肺癌胸内淋巴结转移诊断价值[J]. 临床军医杂志, 2016, 44(2): 200.