Chinese Medical Sciences Journal ›› 2019, Vol. 34 ›› Issue (1): 45-50.doi: 10.24920/003572
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Wang Guorong, Wang Zhiwei(), Jin Zhengyu(
)
Received:
2019-02-01
Revised:
2019-02-25
Published:
2019-03-30
Online:
2019-04-08
Contact:
Wang Zhiwei,Jin Zhengyu
E-mail:zhiweiwang1981@sina.com;jinzhengyu@163.com
Wang Guorong, Wang Zhiwei, Jin Zhengyu. Application and Progress of Texture Analysis in the Therapeutic Effect Prediction and Prognosis of Neoadjuvant Chemoradiotherapy for Colorectal Cancer[J].Chinese Medical Sciences Journal, 2019, 34(1): 45-50.
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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. CA Cancer J Clin 2018; 68(6):394-424. doi: 10.3322/caac.21492.
doi: 10.3322/caac.v68.6 |
2. |
Kemeny N . The management of resectable and unresectable liver metastases from colorectal cancer. Curr Opin Oncol 2010; 22(4):364-73. doi: 10.1097/CCO.0b013e32833a6c8a.
doi: 10.1097/CCO.0b013e32833a6c8a |
3. | Zhang S, Bai W, Tong X , et al. Correlation between tumor microenvironment-associated factors and the efficacy and prognosis of neoadjuvant therapy for rectal cancer. Oncol Lett 2019; 17(1):1062-70. doi: 10.3892/ol.2018.9682. |
4. |
Yang Z, Tang LH, Klimstra DS . Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol 2011; 35(6):853-60. doi: 10.1097/PAS.0b013e31821a0696.
doi: 10.1097/PAS.0b013e31821a0696 |
5. |
Tsujikawa T, Yamamoto M, Shono K , et al. Assessment of intratumor heterogeneity in mesenchymal uterine tumor by an (18)F-FDG PET/CT texture analysis. Ann Nucl Med 2017; 31(10):752-7. doi: 10.1007/s12149-017-1208-x.
doi: 10.1007/s12149-017-1208-x pmid: 28905201 |
6. |
Kim HS, Kim JH, Yoon YC , et al. Tumor spatial heterogeneity in myxoid-containing soft tissue using texture analysis of diffusion-weighted MRI. PLoS One 2017; 12(7):e0181339. doi: 10.1371/journal.pone.0181339.
doi: 10.1371/journal.pone.0181339 pmid: 28708850 |
7. |
Van Cutsem E, Cervantes A, Adam R , et al. ESMO consensus guidelines for the management of patients with metastatic colorectal cancer. Ann Oncol 2016; 27(8):1386-422. doi: 10.1093/annonc/mdw235.
doi: 10.1093/annonc/mdw235 pmid: 27380959 |
8. |
Li Y, Wang J, Ma X , et al. A review of neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Int J Biol Sci 2016; 12(8):1022-31. doi: 10.7150/ijbs.15438.
doi: 10.7150/ijbs.15438 pmid: 27489505 |
9. |
Birbeck KF, Macklin CP, Tiffin NJ , et al. Rates of circumferential resection margin involvement vary between surgeons and predict outcomes in rectal cancer surgery. Ann Surg 2002; 235(4):449-57.
doi: 10.1097/00000658-200204000-00001 pmid: 11923599 |
10. |
Rodel C, Grabenbauer GG, Papadopoulos T , et al. Apoptosis as a cellular predictor for histopathologic response to neoadjuvant radiochemotherapy in patients with rectal cancer. Int J Radiat Oncol Biol Phys 2002; 52(2):294-303.
doi: 10.1016/S0360-3016(01)02643-8 pmid: 11872273 |
11. |
Dworak O, Keilholz L, Hoffmann A . Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis 1997; 12(1):19-23.
doi: 10.1007/s003840050072 pmid: 9548104 |
12. |
Davnall F, Yip CS, Ljungqvist G , et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2012; 3(6):573-89. doi: 10.1007/s13244-012-0196-6.
doi: 10.1007/s13244-012-0196-6 pmid: 23093486 |
13. |
Alobaidli S , McQuaid S, South C, et al. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014; 87(1042):20140369. doi: 10.1259/bjr.20140369.
doi: 10.1259/bjr.20140369 pmid: 25051978 |
14. |
Scalco E, Rizzo G . Texture analysis of medical images for radiotherapy applications. Br J Radiol 2017; 90(1070):20160642. doi: 10.1259/bjr.20160642.
doi: 10.1259/bjr.20160642 pmid: 27885836 |
15. |
Lubner MG, Smith AD, Sandrasegaran K , et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017; 37(5):1483-503. doi: 10.1148/rg.2017170056.
doi: 10.1148/rg.2017170056 pmid: 28898189 |
16. | Habr-Gama A, Perez RO, Nadalin W , et al. Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: long-term results. Ann Surg 2004; 240(4):711-7; discussion 717-8. |
17. |
De Cecco CN, Ganeshan B, Ciolina M , et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol 2015; 50(4):239-45. doi: 10.1097/rli.0000000000000116.
doi: 10.1097/RLI.0000000000000116 pmid: 25501017 |
18. |
De Cecco CN, Ciolina M, Caruso D , et al. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY) 2016; 41(9):1728-35. doi: 10.1007/s00261-016-0733-8.
doi: 10.1007/s00261-016-0733-8 pmid: 27056748 |
19. |
Caruso D, Zerunian M, Ciolina M , et al. Haralick’s texture features for the prediction of response to therapy in colorectal cancer: a preliminary study. La Radiol Med 2018; 123(3):161-7. doi: 10.1007/s11547-017-0833-8.
doi: 10.1007/s11547-017-0833-8 pmid: 29119525 |
20. |
Liu M, Lv H, Liu LH , et al. Locally advanced rectal cancer: predicting non-responders to neoadjuvant chemoradiotherapy using apparent diffusion coefficient textures. Int J Colorectal Dis 2017; 32(7):1009-12. doi: 10.1007/s00384-017-2835-3.
doi: 10.1007/s00384-017-2835-3 pmid: 28497403 |
21. |
Chee CG, Kim YH, Lee KH , et al. CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS One 2017; 12(8):e0182883. doi: 10.1371/journal.pone.0182883.
doi: 10.1371/journal.pone.0182883 pmid: 28797063 |
22. |
Bundschuh RA, Dinges J, Neumann L , et al. Textural parameters of tumor heterogeneity in (1)(8)F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med 2014; 55(6):891-7. doi: 10.2967/jnumed.113.127340.
doi: 10.2967/jnumed.113.127340 pmid: 24752672 |
23. |
Landreau P, Drouillard A, Launoy G , et al. Incidence and survival in late liver metastases of colorectal cancer. J Gastroenterol Hepatol 2015; 30(1):82-5. doi: 10.1111/jgh.12685.
doi: 10.1111/jgh.12685 pmid: 25088563 |
24. |
Chakedis J, Squires MH, Beal EW , et al. Update on current problems in colorectal liver metastasis. Current Probl Surg 2017; 54(11):554-602. doi: 10.1067/j.cpsurg.2017.10.002.
doi: 10.1067/j.cpsurg.2017.10.002 |
25. |
Chung WS, Park MS, Shin SJ , et al. Response evaluation in patients with colorectal liver metastases: RECIST version 1.1 versus modified CT criteria. AJR Am J Roentgenol 2012; 199(4):809-15. doi: 10.2214/ajr.11.7910.
doi: 10.2214/AJR.11.7910 pmid: 22997372 |
26. |
Rao SX, Lambregts DM, Schnerr RS , et al. CT texture analysis in colorectal liver metastases: a better way than size and volume measurements to assess response to chemotherapy? United European Gastroenterol J 2016; 4(2):257-63. doi: 10.1177/2050640615601603.
doi: 10.1177/2050640615601603 pmid: 4804371 |
27. |
Ahn SJ, Kim JH, Park SJ , 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):1867-74. doi: 10.1016/j.ejrad.2016.08.014.
doi: 10.1016/j.ejrad.2016.08.014 pmid: 27666629 |
28. |
Beckers RCJ, Trebeschi S, Maas M , et al. CT texture analysis in colorectal liver metastases and the surrounding liver parenchyma and its potential as an imaging biomarker of disease aggressiveness, response and survival. Eur J Radiol 2018; 102:15-21. doi: 10.1016/j.ejrad.2018.02.031.
doi: 10.1016/j.ejrad.2018.02.031 pmid: 29685529 |
29. |
Liang HY, Huang YQ, Yang ZX , et al. Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases. European Radiol 2016; 26(7):2009-18. doi: 10.1007/s00330-015-4043-2.
doi: 10.1007/s00330-015-4043-2 pmid: 26494642 |
30. |
Zhang H, Li W, Hu F , et al. MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases. Abdom Radiol (New York) 2019; 44(1):65-71. doi: 10.1007/s00261-018-1682-1.
doi: 10.1007/s00261-018-1682-1 |
31. |
Miles KA, Ganeshan B, Griffiths MR , et al. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 2009; 250(2):444-52. doi: 10.1148/radiol.2502071879.
doi: 10.1148/radiol.2502071879 pmid: 19164695 |
32. |
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):177-84. doi: 10.1148/radiol.12120254.
doi: 10.1148/radiol.12120254 pmid: 23151829 |
33. |
Jalil O, Afaq A, Ganeshan B , et al. Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis 2017; 19(4):349-62. doi: 10.1111/codi.13496.
doi: 10.1111/codi.13496 pmid: 27538267 |
34. |
Lovinfosse P, Polus M, Van Daele D , et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. European J Nucl Med Mol Imaging 2018; 45(3):365-75. doi: 10.1007/s00259-017-3855-5.
doi: 10.1007/s00259-017-3855-5 pmid: 29046927 |
35. |
Chicklore S, Goh V, Siddique M , et al. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. European J Nucl Med Mol Imaging 2013; 40(1):133-40. doi: 10.1007/s00259-012-2247-0.
doi: 10.1007/s00259-012-2247-0 pmid: 23064544 |
36. |
Mackin D, Fave X, Zhang L , et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol 2015; 50(11):757-65. doi: 10.1097/rli.0000000000000180.
doi: 10.1097/RLI.0000000000000180 |
37. |
Fave X, Mackin D, Yang J , et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 2015; 42(12):6784-97. doi: 10.1118/1.4934826.
doi: 10.1118/1.4934826 pmid: 26632036 |
38. |
Fave X, Cook M, Frederick A , et al. Preliminary investigation into sources of uncertainty in quantitative imaging features. Comput Med Imaging Graph 2015; 44:54-61. doi: 10.1016/j.compmedimag.2015.04.006.
doi: 10.1016/j.compmedimag.2015.04.006 pmid: 26004695 |
39. |
Aerts HJ, Velazquez ER, Leijenaar RT , et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5:4006. doi: 10.1038/ncomms5006.
doi: 10.1038/ncomms5006 pmid: 4059926 |
40. |
Rizzo S, Botta F, Raimondi S , et al. Radiomics: the facts and the challenges of image analysis. European Radiol Exp 2018; 2(1):36. doi: 10.1186/s41747-018-0068-z.
doi: 10.1186/s41747-018-0068-z |
41. |
Summers RM . Texture analysis in radiology: does the emperor have no clothes? Abdom Radiol (New York) 2017; 42(2):342-5. doi: 10.1007/s00261-016-0950-1.
doi: 10.1007/s00261-016-0950-1 pmid: 27770161 |
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