

FOLLOWUS
Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China
* Zhiwei Wang Tel: 86-10-69155544, E-mail: zhiweiwang1981@sina.com;
Zhengyu Jin Tel: 86-10-65295441; E-mail: jinzhengyu@163.com
Received:01 February 2019,
Revised:25 February 2019,
Published Online:30 March 2019,
Published:30 March 2019
Scan QR Code
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.
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. DOI: 10.24920/003572.
结直肠癌是常见的恶性肿瘤,在我国的发病率和死亡率逐步攀升。目前新辅助放化疗多用于结直肠癌患者的治疗中,有助于改善预后。但由于个体差异性,患者对新辅助放化疗的敏感性各不相同,因此预测治疗效果对下一步治疗方案的选择尤为重要。纹理分析作为一种图像后处理技术,越来越多地被应用于肿瘤影像学中。本文就纹理分析技术在结直肠癌患者新辅助放化疗疗效预测及预后分析中的应用做一综述。
Colorectal cancer is one of the most common malignant tumors
and the morbidity and mortality are increasing gradually over the last years in China. Neoadjuvant chemoradiotherapy (nCRT) is currently applied to the treatment of colorectal cancer patients
and it is helpful to improve the prognosis. The sensitivity of patients to nCRT is different due to individual differences. Predicting the therapeutic effect of nCRT is of great importance for the further treatment methods. Texture analysis
as an image post-processing technique
has been more and more utilized in the field of oncologic imaging. This article reviews the application and progress of texture analysis in the therapeutic effect prediction and prognosis of nCRT for colorectal cancer.
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 10.3322.10.3322.caac.21492 . DOI: 10.3322/caac.v68.6 http://doi.org/10.3322/caac.v68.6 http://doi.wiley.com/10.3322/caac.v68.6 http://doi.wiley.com/10.3322/caac.v68.6
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 10.1097.10.1097.CCO.0b013e32833a6c8a . DOI: 10.1097/CCO.0b013e32833a6c8a http://doi.org/10.1097/CCO.0b013e32833a6c8a https://insights.ovid.com/crossref?an=00001622-201007000-00015 https://insights.ovid.com/crossref?an=00001622-201007000-00015
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 10.3892.ol.2018.9682 .
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 10.1097.PAS.0b013e31821a0696 . DOI: 10.1097/PAS.0b013e31821a0696 http://doi.org/10.1097/PAS.0b013e31821a0696 https://insights.ovid.com/crossref?an=00000478-201106000-00009 https://insights.ovid.com/crossref?an=00000478-201106000-00009
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 10.1007.s12149-017-1208-x . DOI: 10.1007/s12149-017-1208-x http://doi.org/10.1007/s12149-017-1208-x http://link.springer.com/10.1007/s12149-017-1208-x http://link.springer.com/10.1007/s12149-017-1208-x
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 10.1371.journal.pone.0181339 . DOI: 10.1371/journal.pone.0181339 http://doi.org/10.1371/journal.pone.0181339 https://dx.plos.org/10.1371/journal.pone.0181339 https://dx.plos.org/10.1371/journal.pone.0181339
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 10.1093.annonc/mdw235 . DOI: 10.1093/annonc/mdw235 http://doi.org/10.1093/annonc/mdw235 https://academic.oup.com/annonc/article-lookup/doi/10.1093/annonc/mdw235 https://academic.oup.com/annonc/article-lookup/doi/10.1093/annonc/mdw235
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 10.7150.ijbs.15438 . DOI: 10.7150/ijbs.15438 http://doi.org/10.7150/ijbs.15438 http://www.ijbs.com/v12p1022.htm http://www.ijbs.com/v12p1022.htm
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 http://doi.org/10.1097/00000658-200204000-00001 https://insights.ovid.com/crossref?an=00000658-200204000-00001 https://insights.ovid.com/crossref?an=00000658-200204000-00001
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 http://doi.org/10.1016/S0360-3016(01)02643-8 http://linkinghub.elsevier.com/retrieve/pii/S0360301601026438 http://linkinghub.elsevier.com/retrieve/pii/S0360301601026438
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 http://doi.org/10.1007/s003840050072 http://link.springer.com/10.1007/s003840050072 http://link.springer.com/10.1007/s003840050072
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 10.1007.s13244-012-0196-6 . DOI: 10.1007/s13244-012-0196-6 http://doi.org/10.1007/s13244-012-0196-6 http://pubmedcentralcanada.ca/pmcc/articles/PMC3505569/ http://pubmedcentralcanada.ca/pmcc/articles/PMC3505569/
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 10.1259.bjr.20140369 . DOI: 10.1259/bjr.20140369 http://doi.org/10.1259/bjr.20140369 http://www.birpublications.org/doi/10.1259/bjr.20140369 http://www.birpublications.org/doi/10.1259/bjr.20140369
Scalco E , Rizzo G . Texture analysis of medical images for radiotherapy applications . Br J Radiol 2017 ; 90 ( 1070 ): 20160642 . doi: 10.1259/bjr.20160642 10.1259.bjr.20160642 . DOI: 10.1259/bjr.20160642 http://doi.org/10.1259/bjr.20160642 http://www.birpublications.org/doi/10.1259/bjr.20160642 http://www.birpublications.org/doi/10.1259/bjr.20160642
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 10.1148.rg.2017170056 . DOI: 10.1148/rg.2017170056 http://doi.org/10.1148/rg.2017170056 http://pubs.rsna.org/doi/10.1148/rg.2017170056 http://pubs.rsna.org/doi/10.1148/rg.2017170056
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 .
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 10.1097.rli.0000000000000116 . DOI: 10.1097/RLI.0000000000000116 http://doi.org/10.1097/RLI.0000000000000116 http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00004424-201504000-00008 http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00004424-201504000-00008
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 10.1007.s00261-016-0733-8 . DOI: 10.1007/s00261-016-0733-8 http://doi.org/10.1007/s00261-016-0733-8 http://link.springer.com/10.1007/s00261-016-0733-8 http://link.springer.com/10.1007/s00261-016-0733-8
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 10.1007.s11547-017-0833-8 . DOI: 10.1007/s11547-017-0833-8 http://doi.org/10.1007/s11547-017-0833-8 http://link.springer.com/10.1007/s11547-017-0833-8 http://link.springer.com/10.1007/s11547-017-0833-8
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 10.1007.s00384-017-2835-3 . DOI: 10.1007/s00384-017-2835-3 http://doi.org/10.1007/s00384-017-2835-3 http://link.springer.com/10.1007/s00384-017-2835-3 http://link.springer.com/10.1007/s00384-017-2835-3
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 10.1371.journal.pone.0182883 . DOI: 10.1371/journal.pone.0182883 http://doi.org/10.1371/journal.pone.0182883 https://dx.plos.org/10.1371/journal.pone.0182883 https://dx.plos.org/10.1371/journal.pone.0182883
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 10.2967.jnumed.113.127340 . DOI: 10.2967/jnumed.113.127340 http://doi.org/10.2967/jnumed.113.127340 http://jnm.snmjournals.org/cgi/doi/10.2967/jnumed.113.127340 http://jnm.snmjournals.org/cgi/doi/10.2967/jnumed.113.127340
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 10.1111.jgh.12685 . DOI: 10.1111/jgh.12685 http://doi.org/10.1111/jgh.12685 http://doi.wiley.com/10.1111/jgh.12685 http://doi.wiley.com/10.1111/jgh.12685
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 10.1067.j.cpsurg.2017.10.002 . DOI: 10.1067/j.cpsurg.2017.10.002 http://doi.org/10.1067/j.cpsurg.2017.10.002 https://linkinghub.elsevier.com/retrieve/pii/S0011384017301867 https://linkinghub.elsevier.com/retrieve/pii/S0011384017301867
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 10.2214.ajr.11.7910 . DOI: 10.2214/AJR.11.7910 http://doi.org/10.2214/AJR.11.7910 http://www.ajronline.org/doi/10.2214/AJR.11.7910 http://www.ajronline.org/doi/10.2214/AJR.11.7910
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 10.1177.2050640615601603 . DOI: 10.1177/2050640615601603 http://doi.org/10.1177/2050640615601603 http://journals.sagepub.com/doi/10.1177/2050640615601603 http://journals.sagepub.com/doi/10.1177/2050640615601603
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 10.1016.j.ejrad.2016.08.014 . DOI: 10.1016/j.ejrad.2016.08.014 http://doi.org/10.1016/j.ejrad.2016.08.014 https://linkinghub.elsevier.com/retrieve/pii/S0720048X16302534 https://linkinghub.elsevier.com/retrieve/pii/S0720048X16302534
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 10.1016.j.ejrad.2018.02.031 . DOI: 10.1016/j.ejrad.2018.02.031 http://doi.org/10.1016/j.ejrad.2018.02.031 https://linkinghub.elsevier.com/retrieve/pii/S0720048X18300780 https://linkinghub.elsevier.com/retrieve/pii/S0720048X18300780
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 10.1007.s00330-015-4043-2 . DOI: 10.1007/s00330-015-4043-2 http://doi.org/10.1007/s00330-015-4043-2 http://link.springer.com/10.1007/s00330-015-4043-2 http://link.springer.com/10.1007/s00330-015-4043-2
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 10.1007.s00261-018-1682-1 . DOI: 10.1007/s00261-018-1682-1 http://doi.org/10.1007/s00261-018-1682-1
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 10.1148.radiol.2502071879 . DOI: 10.1148/radiol.2502071879 http://doi.org/10.1148/radiol.2502071879 http://pubs.rsna.org/doi/10.1148/radiol.2502071879 http://pubs.rsna.org/doi/10.1148/radiol.2502071879
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 10.1148.radiol.12120254 . DOI: 10.1148/radiol.12120254 http://doi.org/10.1148/radiol.12120254 http://pubs.rsna.org/doi/10.1148/radiol.12120254 http://pubs.rsna.org/doi/10.1148/radiol.12120254
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 10.1111.codi.13496 . DOI: 10.1111/codi.13496 http://doi.org/10.1111/codi.13496 http://doi.wiley.com/10.1111/codi.2017.19.issue-4 http://doi.wiley.com/10.1111/codi.2017.19.issue-4
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 10.1007.s00259-017-3855-5 . DOI: 10.1007/s00259-017-3855-5 http://doi.org/10.1007/s00259-017-3855-5 http://link.springer.com/10.1007/s00259-017-3855-5 http://link.springer.com/10.1007/s00259-017-3855-5
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 10.1007.s00259-012-2247-0 . DOI: 10.1007/s00259-012-2247-0 http://doi.org/10.1007/s00259-012-2247-0 http://link.springer.com/10.1007/s00259-012-2247-0 http://link.springer.com/10.1007/s00259-012-2247-0
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 10.1097.rli.0000000000000180 . DOI: 10.1097/RLI.0000000000000180 http://doi.org/10.1097/RLI.0000000000000180 http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00004424-201511000-00003 http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00004424-201511000-00003
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 10.1118.1.4934826 . DOI: 10.1118/1.4934826 http://doi.org/10.1118/1.4934826 http://doi.wiley.com/10.1118/1.4934826 http://doi.wiley.com/10.1118/1.4934826
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 10.1016.j.compmedimag.2015.04.006 . DOI: 10.1016/j.compmedimag.2015.04.006 http://doi.org/10.1016/j.compmedimag.2015.04.006 https://linkinghub.elsevier.com/retrieve/pii/S0895611115000841 https://linkinghub.elsevier.com/retrieve/pii/S0895611115000841
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 10.1038.ncomms5006 . DOI: 10.1038/ncomms5006 http://doi.org/10.1038/ncomms5006 http://www.nature.com/articles/ncomms5006 http://www.nature.com/articles/ncomms5006
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 10.1186.s41747-018-0068-z . DOI: 10.1186/s41747-018-0068-z http://doi.org/10.1186/s41747-018-0068-z http://link.springer.com/article/10.1186/s41747-018-0068-z http://link.springer.com/article/10.1186/s41747-018-0068-z
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 10.1007.s00261-016-0950-1 . DOI: 10.1007/s00261-016-0950-1 http://doi.org/10.1007/s00261-016-0950-1 http://link.springer.com/10.1007/s00261-016-0950-1 http://link.springer.com/10.1007/s00261-016-0950-1
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621