MRI纹理分析在乳腺癌诊疗中的应用进展
摘要
<正>乳腺癌占全球女性恶性肿瘤发病率和病死率的首位,占所有癌症死亡人数的11.6%~([1])。乳腺MRI已广泛应用于乳腺癌的诊治中,包括扩散加权成像(diffusion weight image, DWI)、动态增强MRI (dynamic contrast-enhanced MRI, DCE-MRI)等。但是肉眼直接观察到MRI图像信息是有限的,纹理分析可以非侵入性定性、定量描述兴趣区乳腺组织的信号强度分布,研究组织像素灰度值的局部特征,灰度值的变化规律及其分布,以辅助诊断和治疗。本文就MRI纹理分析原理及其在乳腺癌临床诊疗中的应用进行综述。
引文
[1]Bray F,Ferlay J,Soerjomataram I,et al.Global cancer statistics2018:GLOBOCAN estimates of incidenceand mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin,2018,68:394-424.
[2]Kumar V,Gu Y,Basu S,et al.Radiomics:the process and the challenges.Magn Reson Imaging,2012,30:1234-1248.
[3]Lambin P,Rios-Velazquez E,Leijenaar R,et al.Radiomics:extracting more information from medical images using advanced feature analysis.Eur J Cancer,2012,48:441-446.
[4]Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data.Radiology,2016,278:563-577.
[5]Parekh V,Jacobs MA.Radiomics:a new application from established techniques.Expert Rev Precis Med Drug Dev,2016,1:207-226.
[6]陈瑾,王海屹,叶慧义.纹理分析在肿瘤影像学中的研究进展.中华放射学杂志,2017,51:979-982.
[7]Forghani R,Srinivasan A,Forghani B.Advanced tissue characterization and texture analysis using dual-energy computed tomography:horizons and emergin applications.Neuroimaging Clin N Am,2017,27:533-546.
[8]Hatt M,Tixier F,Pierce L,et al.Characterization of PET/CTimages using texture analysis:the past,the present...any future?Eur J Nucl Med Mol Imaging,2017,44:151-165.
[9]Li Z,Yu L,Wang X,et al.Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors.Clin Breast Cancer,2018,18:e621-e627.
[10]Bhooshan N,Giger M,Lan L,et al.Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions.Magn Reson Med,2011,66:555-564.
[11]Holli K,L覿覿peri AL,Harrison L,et al.Characterization of breast cancer types by texture analysis of magnetic resonance images.Acad Radiol,2010,17:135-141.
[12]Suo S,Zhang K,Cao M,et al.Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.J Magn ResonImaging,2016,43:894-902.
[13]Agner SC,Rosen MA,Englander S,et al.Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images:a feasibility study.Radiology,2014,272:91-99.
[14]Frank GA,Danilova NV,Andreeva IuIu,et al.WHOclassification of tumors of the breast,2012.Arkh Patol,2013,75:53-63.
[15]李汉森,章强,齐海坤,等.基于增强MRI纹理分析区分非产褥期乳腺炎与非肿块样强化病灶乳腺癌.中国医学影像学杂志,2017,25:354-359.
[16]Waugh SA,Purdie CA,Jordan LB,et al.Magnetic resonance imaging texture analysis classification of primary breast cancer.Eur Radiol,2016,26:322-330.
[17]Cancer Genome Atlas Network.Comprehensive molecular portraits of human breast tumours.Nature,2012,490:61-70.
[18]Bagaria SP,Ray PS,Sim MS,et al.Personalizing breast cancer staging by the inclusion of ER,PR,and HER2.JAMA Surg,2014,149:125-129.
[19]Ko ES,Han BK,Kim RB,et al.Apparent diffusion coefficient in estrogen receptor-positive invasive ductal breast carcinoma:correlations with tumor-stroma ratio.Radiology,2014,271:30-37.
[20]Huber KE,Carey LA,Wazer DE.Breast cancer molecular subtypes in patients with locally advanced disease:impact on prognosis,patterns of recurrence,and response to therapy.Semin Radiat Oncol,2009,19:204-210.
[21]Wiechmann L,Sampson M,Stempel M,et al.Presenting features of breast cancer differ by molecular subtype.Ann Surg Oncol,2009,16:2705-2710.
[22]Montagna E,Bagnardi V,Rotmensz N,et al.Immunohistochemically defined subtypes and outcome in occult breast carcinoma with axillary presentation.Breast Cancer Res Treat,2011,129:867-875.
[23]Sun X,He B,Luo X,et al.Preliminary study on molecular subtypes of breast cancer based on magnetic resonance imaging texture analysis.J Comput Assist Tomogr,2018,42:531-535.
[24]Sutton EJ,Dashevsky BZ,Oh JH,et al.Breast cancer molecular subtype classifier that incorporates MRI features.JMagn Reson Imaging,2016,44:122-129.
[25]Li H,Zhu Y,Burnside ES,et al.Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.NPJ Breast Cancer,2016,2:16012.
[26]Fan M,Li H,Wang S,et al.Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.PLoS One,2017,12:e171683.
[27]Just N.Improving tumour heterogeneity MRI assessment with histograms.Br J Cancer,2014,111:2205-2213.
[28]Wu J,Sun X,Wang J,et al.Identifying relations between imaging phenotypes and molecular subtypes of breast cancer:model discovery and external validation.J Magn Reson Imaging,2017,46:1017-1027.
[29]Austin D,Hamilton N,Elshimali,et al.Estrogen receptor-beta is apotential target for triple negative breast cancer treatment.Oncotarget,2018,9:33912-33930.
[30]吴佩琪,赵可,吴磊,等.基于扩散加权成像和动态增强MRI的影像组学特征与乳腺癌分子分型的关系初探.中华放射学杂志,2018,52:338-343.
[31]Choi Y,Kim SH,Youn IK,et al.Rim sign and histogram analysis of apparent diffusion coefficient values on diffusionweighted MRI in triple-negative breast cancer:comparison with ER-positive subtype.PLoS One,2017,12:e0177903.
[32]Fox MJ,Gibbs P,Pickles MD.Minkowski functionals:an MRItexture analysis tool for determination of the aggressiveness of breast cancer.J Magn Reson Imaging,2016,43:903-910.
[33]Grimm LJ,Zhang J,Mazurowski MA.Computational approach to radiogenomics of breast cancer:luminal A and luminal Bmolecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.J Magn Reson Imaging,2015,42:902-907.
[34]Falato C,Lorent J,Tani E,et al.Ki67 measured in metastatic tissue and prognosis in patients with advanced breast cancer.Breast Cancer Res Treat,2014,147:407-414.
[35]Li H,Han X,Liu Y,et al.Ki67 as a predictor of poor prognosis in patients with triple-negative breast cancer.Oncol Lett,2015,9:149-152.
[36]Bustreo S,Osella-Abate S,Cassoni P,et al.Optimal Ki67 cutoff for luminal breast cancer prognostic evaluation:a large case series study with a long-term follow-up.Breast Cancer Res Treat,2016,157:363-371.
[37]Goldhirsch A,Wood WC,Coates AS,et al.Strategies for subtypes-dealing with the diversity of breast cancer:highlights of the St.Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011.Ann Oncol,2011,22:1736-1747.
[38]Liu Y,Xu L,Xin L,et al.Interpretation of top issues of the2013 St Gallen International Expert Consensus.Zhonghua Wai Ke Za Zhi,2014,52:401-404.
[39]Holli-Helenius K,Salminen A,Rinta-Kiikka I,et al.MRItexture analysis in differentiating luminal A and luminal Bbreast cancer molecular subtypes-a feasibility study.BMC Med Imaging,2017,17:69.
[40]Juan MW,Yu J,Peng GX,et al.Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer.Oncol Lett,2018,16:5084-5090.
[41]Nelson DA,Tan TT,Rabson AB,et al.Hypoxia and defective apoptosis drive genomic instability and tumorigenesis.Genes Dev,2004,18:2095-2107.
[42]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,2011,35:853-860.
[43]Kim JH,Ko ES,Lim Y,et al.Breast cancer heterogeneity:MRimaging texture analysis and survival outcomes.Radiology,2017,282:665-675.
[44]Pickles MD,Lowry M,Gibbs P.Pretreatment prognostic value of dynamic contrast-enhanced magnetic resonance imaging vascular,texture,shape,and size parameters compared with traditional survival indicators obtained from locally advanced breast cancer patients.Invest Radiol,2016,51:177-185.
[45]Siegel R,DeSantis C,Virgo K,et al.Cancer treatment and survivorship statistics,2012.CA Cancer J Clin,2012,62:220-241.
[46]Liu C,Ding J,Spuhler K,et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.J Magn Reson Imaging,2019,49:131-140.
[47]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 diffusionweighted MRI.Eur Radiol,2018,28:582-591.
[48]Semiglazov V.RECIST for response(clinical and imaging)in neoadjuvant clinical trials in operable breast cancer.J Natl Cancer Inst Monogr,2015,51:21-23.
[49]Thibault G,Tudorica A,Afzal A,et al.DCE-MRI texture features for early prediction of breast cancer therapy response.Tomography,2017,3:23-32.
[50]曹崑,刘慧,赵博,等.早期增强MRI纹理特征分析对乳腺癌新辅助化疗后病理完全缓解的判断能力.中华放射学杂志,2018,52:523-527.
[51]金观桥,苏丹柯,罗殿中,等.表观扩散系数直方图预测局部晚期乳腺癌患者新辅助化疗疗效的价值.中华放射学杂志,2015,7:491-494.
[52]Von Minckwitz G,Untch M,Blohmer JU,et al.Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes.J Clin Oncol,2012,30:1796-1804.
[53]Ahmed A,Gibbs P,Pickles M,et al.Texture analysis in assessment and prediction of chemotherapy response in breast cancer.J Magn Reson Imaging,2013,38:89-101.
[54]Michoux N,Van den Broeck S,Lacoste L,et al.Texture analysis on MR images helps predicting non-response to NACin breast cancer.BMC Cancer,2015,15:574.
[55]Gibbs P,Turnbull LW.Textural analysis of contrast-enhanced MR images of the breast.Magn Reson Med,2003,50:92-98.
[56]Ahmed A,Gibbs P,Pickles M,et al.Texture analysis in assessment and prediction of chemotherapy response in breast cancer.J Magn Reson Imaging,2013,38:89-101.
[57]Braman NM,Etesami M,Prasanna P,et al.Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.Breast Cancer Res,2017,19:57.
[58]Haralick RM,Shanmugan K,Dinstein I.Textural features for image classification.IEEE Trans Syst Man Cybern,1973,SMC-3:610-621.
[59]Zhang Y,Moore GR,Laule C,et al.Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis.Ann Neurol,2013,74:91-99.
[2]Kumar V,Gu Y,Basu S,et al.Radiomics:the process and the challenges.Magn Reson Imaging,2012,30:1234-1248.
[3]Lambin P,Rios-Velazquez E,Leijenaar R,et al.Radiomics:extracting more information from medical images using advanced feature analysis.Eur J Cancer,2012,48:441-446.
[4]Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data.Radiology,2016,278:563-577.
[5]Parekh V,Jacobs MA.Radiomics:a new application from established techniques.Expert Rev Precis Med Drug Dev,2016,1:207-226.
[6]陈瑾,王海屹,叶慧义.纹理分析在肿瘤影像学中的研究进展.中华放射学杂志,2017,51:979-982.
[7]Forghani R,Srinivasan A,Forghani B.Advanced tissue characterization and texture analysis using dual-energy computed tomography:horizons and emergin applications.Neuroimaging Clin N Am,2017,27:533-546.
[8]Hatt M,Tixier F,Pierce L,et al.Characterization of PET/CTimages using texture analysis:the past,the present...any future?Eur J Nucl Med Mol Imaging,2017,44:151-165.
[9]Li Z,Yu L,Wang X,et al.Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors.Clin Breast Cancer,2018,18:e621-e627.
[10]Bhooshan N,Giger M,Lan L,et al.Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions.Magn Reson Med,2011,66:555-564.
[11]Holli K,L覿覿peri AL,Harrison L,et al.Characterization of breast cancer types by texture analysis of magnetic resonance images.Acad Radiol,2010,17:135-141.
[12]Suo S,Zhang K,Cao M,et al.Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.J Magn ResonImaging,2016,43:894-902.
[13]Agner SC,Rosen MA,Englander S,et al.Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images:a feasibility study.Radiology,2014,272:91-99.
[14]Frank GA,Danilova NV,Andreeva IuIu,et al.WHOclassification of tumors of the breast,2012.Arkh Patol,2013,75:53-63.
[15]李汉森,章强,齐海坤,等.基于增强MRI纹理分析区分非产褥期乳腺炎与非肿块样强化病灶乳腺癌.中国医学影像学杂志,2017,25:354-359.
[16]Waugh SA,Purdie CA,Jordan LB,et al.Magnetic resonance imaging texture analysis classification of primary breast cancer.Eur Radiol,2016,26:322-330.
[17]Cancer Genome Atlas Network.Comprehensive molecular portraits of human breast tumours.Nature,2012,490:61-70.
[18]Bagaria SP,Ray PS,Sim MS,et al.Personalizing breast cancer staging by the inclusion of ER,PR,and HER2.JAMA Surg,2014,149:125-129.
[19]Ko ES,Han BK,Kim RB,et al.Apparent diffusion coefficient in estrogen receptor-positive invasive ductal breast carcinoma:correlations with tumor-stroma ratio.Radiology,2014,271:30-37.
[20]Huber KE,Carey LA,Wazer DE.Breast cancer molecular subtypes in patients with locally advanced disease:impact on prognosis,patterns of recurrence,and response to therapy.Semin Radiat Oncol,2009,19:204-210.
[21]Wiechmann L,Sampson M,Stempel M,et al.Presenting features of breast cancer differ by molecular subtype.Ann Surg Oncol,2009,16:2705-2710.
[22]Montagna E,Bagnardi V,Rotmensz N,et al.Immunohistochemically defined subtypes and outcome in occult breast carcinoma with axillary presentation.Breast Cancer Res Treat,2011,129:867-875.
[23]Sun X,He B,Luo X,et al.Preliminary study on molecular subtypes of breast cancer based on magnetic resonance imaging texture analysis.J Comput Assist Tomogr,2018,42:531-535.
[24]Sutton EJ,Dashevsky BZ,Oh JH,et al.Breast cancer molecular subtype classifier that incorporates MRI features.JMagn Reson Imaging,2016,44:122-129.
[25]Li H,Zhu Y,Burnside ES,et al.Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.NPJ Breast Cancer,2016,2:16012.
[26]Fan M,Li H,Wang S,et al.Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.PLoS One,2017,12:e171683.
[27]Just N.Improving tumour heterogeneity MRI assessment with histograms.Br J Cancer,2014,111:2205-2213.
[28]Wu J,Sun X,Wang J,et al.Identifying relations between imaging phenotypes and molecular subtypes of breast cancer:model discovery and external validation.J Magn Reson Imaging,2017,46:1017-1027.
[29]Austin D,Hamilton N,Elshimali,et al.Estrogen receptor-beta is apotential target for triple negative breast cancer treatment.Oncotarget,2018,9:33912-33930.
[30]吴佩琪,赵可,吴磊,等.基于扩散加权成像和动态增强MRI的影像组学特征与乳腺癌分子分型的关系初探.中华放射学杂志,2018,52:338-343.
[31]Choi Y,Kim SH,Youn IK,et al.Rim sign and histogram analysis of apparent diffusion coefficient values on diffusionweighted MRI in triple-negative breast cancer:comparison with ER-positive subtype.PLoS One,2017,12:e0177903.
[32]Fox MJ,Gibbs P,Pickles MD.Minkowski functionals:an MRItexture analysis tool for determination of the aggressiveness of breast cancer.J Magn Reson Imaging,2016,43:903-910.
[33]Grimm LJ,Zhang J,Mazurowski MA.Computational approach to radiogenomics of breast cancer:luminal A and luminal Bmolecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.J Magn Reson Imaging,2015,42:902-907.
[34]Falato C,Lorent J,Tani E,et al.Ki67 measured in metastatic tissue and prognosis in patients with advanced breast cancer.Breast Cancer Res Treat,2014,147:407-414.
[35]Li H,Han X,Liu Y,et al.Ki67 as a predictor of poor prognosis in patients with triple-negative breast cancer.Oncol Lett,2015,9:149-152.
[36]Bustreo S,Osella-Abate S,Cassoni P,et al.Optimal Ki67 cutoff for luminal breast cancer prognostic evaluation:a large case series study with a long-term follow-up.Breast Cancer Res Treat,2016,157:363-371.
[37]Goldhirsch A,Wood WC,Coates AS,et al.Strategies for subtypes-dealing with the diversity of breast cancer:highlights of the St.Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011.Ann Oncol,2011,22:1736-1747.
[38]Liu Y,Xu L,Xin L,et al.Interpretation of top issues of the2013 St Gallen International Expert Consensus.Zhonghua Wai Ke Za Zhi,2014,52:401-404.
[39]Holli-Helenius K,Salminen A,Rinta-Kiikka I,et al.MRItexture analysis in differentiating luminal A and luminal Bbreast cancer molecular subtypes-a feasibility study.BMC Med Imaging,2017,17:69.
[40]Juan MW,Yu J,Peng GX,et al.Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer.Oncol Lett,2018,16:5084-5090.
[41]Nelson DA,Tan TT,Rabson AB,et al.Hypoxia and defective apoptosis drive genomic instability and tumorigenesis.Genes Dev,2004,18:2095-2107.
[42]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,2011,35:853-860.
[43]Kim JH,Ko ES,Lim Y,et al.Breast cancer heterogeneity:MRimaging texture analysis and survival outcomes.Radiology,2017,282:665-675.
[44]Pickles MD,Lowry M,Gibbs P.Pretreatment prognostic value of dynamic contrast-enhanced magnetic resonance imaging vascular,texture,shape,and size parameters compared with traditional survival indicators obtained from locally advanced breast cancer patients.Invest Radiol,2016,51:177-185.
[45]Siegel R,DeSantis C,Virgo K,et al.Cancer treatment and survivorship statistics,2012.CA Cancer J Clin,2012,62:220-241.
[46]Liu C,Ding J,Spuhler K,et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.J Magn Reson Imaging,2019,49:131-140.
[47]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 diffusionweighted MRI.Eur Radiol,2018,28:582-591.
[48]Semiglazov V.RECIST for response(clinical and imaging)in neoadjuvant clinical trials in operable breast cancer.J Natl Cancer Inst Monogr,2015,51:21-23.
[49]Thibault G,Tudorica A,Afzal A,et al.DCE-MRI texture features for early prediction of breast cancer therapy response.Tomography,2017,3:23-32.
[50]曹崑,刘慧,赵博,等.早期增强MRI纹理特征分析对乳腺癌新辅助化疗后病理完全缓解的判断能力.中华放射学杂志,2018,52:523-527.
[51]金观桥,苏丹柯,罗殿中,等.表观扩散系数直方图预测局部晚期乳腺癌患者新辅助化疗疗效的价值.中华放射学杂志,2015,7:491-494.
[52]Von Minckwitz G,Untch M,Blohmer JU,et al.Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes.J Clin Oncol,2012,30:1796-1804.
[53]Ahmed A,Gibbs P,Pickles M,et al.Texture analysis in assessment and prediction of chemotherapy response in breast cancer.J Magn Reson Imaging,2013,38:89-101.
[54]Michoux N,Van den Broeck S,Lacoste L,et al.Texture analysis on MR images helps predicting non-response to NACin breast cancer.BMC Cancer,2015,15:574.
[55]Gibbs P,Turnbull LW.Textural analysis of contrast-enhanced MR images of the breast.Magn Reson Med,2003,50:92-98.
[56]Ahmed A,Gibbs P,Pickles M,et al.Texture analysis in assessment and prediction of chemotherapy response in breast cancer.J Magn Reson Imaging,2013,38:89-101.
[57]Braman NM,Etesami M,Prasanna P,et al.Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.Breast Cancer Res,2017,19:57.
[58]Haralick RM,Shanmugan K,Dinstein I.Textural features for image classification.IEEE Trans Syst Man Cybern,1973,SMC-3:610-621.
[59]Zhang Y,Moore GR,Laule C,et al.Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis.Ann Neurol,2013,74:91-99.