1. |
Chen Wanqing, Zheng Rongshou, Baade P D, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
|
2. |
Siegel R L, Miller K D, Jemal A. Cancer statistics, 2018. CA Cancer J Clin, 2018, 68(1): 7-30.
|
3. |
Shah S A, Bromberg R, Coates A, et al. Survival after liver resection for metastatic colorectal carcinoma in a large population. J Am Coll Surg, 2007, 205(5): 676-683.
|
4. |
Simpson A L, Doussot A, Creasy J M, et al. Computed tomography image texture: a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer. Ann Surg Oncol, 2017, 24(9): 2482-2490.
|
5. |
郭桂芳, 夏良平, 张蓓, 等. 西妥昔单抗联合化疗对 53 例晚期结直肠癌的疗效. 癌症, 2009, 28(12): 1317-1323.
|
6. |
Humblet Y, Peeters M, Gelderblom H, et al. Cetuximab dose-escalation in patients (pts) with metastatic colorectal cancer (mCRC) with no or slight skin reactions on standard treatment: pharmacokinetic (PK), pharmacodynamic (PD) and efficacy data from the EVEREST study. EJC Supplements, 2007, 5(4): 240.
|
7. |
Andreyev H J N, Norman A R, Cunningham D, et al. Kirsten ras mutations in patients with colorectal cancer: the 'RASCAL II' study. Br J Cancer, 2001, 85(5): 692-696.
|
8. |
郭桂芳, 夏良平, 徐瑞华, 等. 西妥昔单抗联合化疗治疗晚期结直肠癌的生存分析及 KRAS 对疗效的影响. 中山大学学报:医学科学版, 2011, 32(5): 637-643.
|
9. |
Castellano G, Bonilha L, Li Lm, et al. Texture analysis of medical images. Clin Radiol, 2004, 59(12): 1061-1069.
|
10. |
Eisenhauer E A, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer, 2009, 45(2): 228-247.
|
11. |
Wagner F, Hakami Y A, Warnock G, et al. Comparison of contrast-enhanced CT and [18F] FDG PET/CT analysis using kurtosis and skewness in patients with primary colorectal cancer. Mol Imaging Biol, 2017, 19(5): 795-803.
|
12. |
Kim J H, Ko E S, Lim Y, et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology, 2017, 282(3): 665-675.
|
13. |
Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol, 2012, 22(4): 796-802.
|
14. |
Raman S P, Chen Yifei, Schroeder J L, et al. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol, 2014, 21(12): 1587-1596.
|
15. |
Smith A D, Gray M R, del Campo S M, et al. Predicting overall survival in patients with metastatic melanoma on antiangiogenic therapy and RECIST stable disease on initial posttherapy images using CT texture analysis. Am J Roentgenol, 2015, 205(3): W283-W293.
|
16. |
Lubner M G, Stabo N, Abel E J, et al. CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. Am J Roentgenol, 2016, 207(1): 96-105.
|
17. |
Yu H, Scalera J, Khalid M, et al. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol, 2017, 42(10): 2470-2478.
|
18. |
于鲁, 夏翃, 刘卫芳. 基于磁共振图像海马三维纹理特征的阿尔茨海默病及健康对照的分类研究. 生物医学工程学杂志, 2016, 33(6): 1090-1094.
|
19. |
Ahn S J, Kim J H, Park S J, et al. Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol, 2016, 85(10): 1867-1874.
|
20. |
Chaddad A, Tanougast C. Texture analysis of abnormal cell images for predicting the continuum of colorectal cancer. Anal Cell Pathol (Amst), 2017: 8428102.
|
21. |
Beckers R C J, Lambregts D M J, Schnerr R S, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases–A multicentre study. Eur J Radiol, 2017, 92: 64-71.
|
22. |
Lee S J, Zea R, Kim D H, et al. CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer. Eur Radiol, 2018, 28(4): 1520-1528.
|
23. |
Cui C, Cai H, Liu L, et al. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. Eur Radiol, 2011, 21(11): 2318-2325.
|
24. |
Grove O, Berglund A E, Schabath M B, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One, 2015, 10(3): e0118261.
|
25. |
Yang Xiangyu, Knopp M V. Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol, 2011: 732848.
|
26. |
Mayerhoefer M E, Schima W, Trattnig S, et al. Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas. J Magn Reson Imaging, 2010, 32(2): 352-359.
|
27. |
Ganeshan B, Miles K A. Quantifying tumour heterogeneity with CT. Cancer Imaging, 2013, 13(1): 140-149.
|
28. |
Ng F, Ganeshan B, Kozarski R, et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology, 2013, 266(1): 177-184.
|
29. |
Lubner M G, Stabo N, Lubner S J, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging, 2015, 40(7): 2331-2337.
|
30. |
Bezy-Wendling J, Kretowski M, Rolland Y, et al. Toward a better understanding of texture in vascular CT scan simulated images. IEEE Trans Biomed Eng, 2001, 48(1): 120-124.
|
31. |
Goh V, Ganeshan B, Nathan P, et al. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology, 2011, 261(1): 165-171.
|
32. |
Chen S, Zhu Y, Liu Z, et al. Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: A retrospective pilot study. Eur J Radiol, 2017, 90: 198-204.
|