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新辅助化疗(NAC)已经成为乳腺癌规范化治疗方案之一,可降低肿瘤分期,提高保乳率。如何早期预测患者能否从NAC中获益尤为重要。影像组学能够深度挖掘肿瘤影像的异质特征,在肿瘤临床诊断和综合治疗中具有指导性作用,是实现肿瘤精准医疗的一种重要研究方法。本文通过回顾相关研究,对基于常见影像学检查(包括乳腺X线、CT、PETCT、US及MRI)的影像组学分析预测乳腺癌NAC疗效的研究进展进行综述。
Abstract:[1]Fahad Ullah M. Breast cancer:current perspectives on the disease status. Adv Exp Med Biol, 2019,1152:51-64.
[2]Dialani V, Chadashvili T, Slanetz PJ. Role of imaging in neoadjuvant therapy for breast cancer. Ann Surg Oncol,2015,22:1416-1424.
[3]Mamounas EP. Impact of neoadjuvant chemotherapy on locoregional surgical treatment of breast cancer. Ann Surg Oncol, 2015,22:1425-1433.
[4]Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer:the CTNeoBC pooled analysis. Lancet, 2014,384:164-172.
[5]邓子晴,刘超,鲁际. MR影像组学在乳腺癌术前评估中的应用进展.影像诊断与介入放射学, 2019,28:225-229.
[6]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.
[7]Kumar V, Gu Y, Basu S, et al. Radiomics:the process and the challenges. Magn Reson Imaging, 2012,30:1234-1248.
[8]Wang H, Mao XY. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer. Drug Des Devel Ther, 2020,14:2423-2433.
[9]Shuai YJ, Ma L. Prognostic value of pathologic complete response and the alteration of breast cancer immunohistochemical biomarkers after neoadjuvant chemotherapy. Pathol Res Pract,2019,215:29-33.
[10]Hayashi M, Yamamoto Y, Iwase H. Clinical imaging for the prediction of neoadjuvant chemotherapy response in breast cancer. Chin Clin Oncol, 2020,9:31.
[11]Vaidya JS, Massarut S, Vaidya HJ, et al. Rethinking neoadjuvant chemotherapy for breast cancer. BMJ, 2018,360:j5913.
[12]Caudle AS, Gonzalez-Angulo AM, Hunt KK, et al. Predictors of tumor progression during neoadjuvant chemotherapy in breast cancer. J Clin Oncol, 2010,28:1821-1828.
[13]Park J, Chae EY, Cha JH, et al. Comparison of mammography,digital breast tomosynthesis, automated breast ultrasound,magnetic resonance imaging in evaluation of residual tumor after neoadjuvant chemotherapy. Eur J Radiol, 2018,108:261-268.
[14]Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst, 2000,92:205-216.
[15]Xiong QQ, Zhou XZ, Liu ZY, et al. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy. Clin Transl Oncol, 2020,22:50-59.
[16]Montemurro F, Nuzzolese I, Ponzone R. Neoadjuvant or adjuvant chemotherapy in early breast cancer? Expert Opin Pharmacother, 2020,21:1071-1082.
[17]Jain P, Doval DC, Batra U, et al. Ki-67 labeling index as a predictor of response to neoadjuvant chemotherapy in breast cancer. Jpn J Clin Oncol, 2019,49:329-338.
[18]Kim T, Han W, Kim MK, et al. Predictive significance of p53,Ki-67, and Bcl-2 expression for pathologic complete response after neoadjuvant chemotherapy for triple-negative breast cancer.J Breast Cancer, 2015,18:16-21.
[19]Rauch GM, Adrada BE, Kuerer HM, et al. Multimodality imaging for evaluating response to neoadjuvant chemotherapy in breast cancer. Am J Roentgenol, 2017,208:290-299.
[20]James JJ, Tennant SL. Contrast-enhanced spectral mammography(CESM). Clin Radiol, 2018,73:715-723.
[21]Wang ZY, Lin F, Ma H, et al. Contrast-enhanced spectral mammography-based radiomics nomogram for the prediction of neoadjuvant chemotherapy-insensitive breast cancers. Front Oncol,2021,11:605230.
[22]Huang XM, Mai JH, Huang YQ, et al. Radiomic nomogram for pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer:predictive value of staging contrast-enhanced CT. Clin Breast Cancer, 2020:S1526-8209(20)30332-3.
[23]Fuster D, Duch J, Paredes P, et al. Preoperative staging of large primary breast cancer with[18F] fluorodeoxyglucose positron emission tomography/computed tomography compared with conventional imaging procedures. J Clin Oncol, 2008,26:4746-4751.
[24]Dialani V, Chadashvili T, Slanetz PJ. Role of imaging in neoadjuvant therapy for breast cancer. Ann Surg Oncol,2015,22:1416-1424.
[25]Li PL, Wang XY, Xu CR, et al.18F-FDG PET/CT radiomic predictors of pathologic complete response(pCR)to neoadjuvant chemotherapy in breast cancer patients. Eur J Nucl Med Mol Imaging, 2020,47:1116-1126.
[26]Antunovic L, De Sanctis R, Cozzi L, et al. PET/CT radiomics in breast cancer:promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging, 2019,46:1468-1477.
[27]Lee H, Lee DE, Park S, et al. Predicting response to neoadjuvant chemotherapy in patients with breast cancer:combined statistical modeling using clinicopathological factors and FDG PET/CT texture parameters. Clin Nucl Med, 2019,44:21-29.
[28]Molina-García D, García-Vicente AM, Pérez-Beteta J, et al.Intratumoral heterogeneity in18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate.Ann Nucl Med, 2018,32:379-388.
[29]Lee MC, Gonzalez SJ, Lin H, et al. Prospective trial of breast MRI versus 2D and 3D ultrasound for evaluation of response to neoadjuvant chemotherapy. Ann Surg Oncol, 2015,22:2888-2894.
[30]李蔓英,李彬,罗佳,等.基于灰阶超声的影像组学模型预测乳腺癌新辅助化疗效果.中国医学影像技术, 2019,35:1331-1335.
[31]Jiang M, Li CL, Luo XM, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.Eur J Cancer, 2021,147:95-105.
[32]Gu JJ, Polley EC, Denis M, et al. Early assessment of shear wave elastography parameters foresees the response to neoadjuvant chemotherapy in patients with invasive breast cancer. Breast Cancer Res, 2021,23:52.
[33]Zhang Q, Yuan CC, Dai W, et al. Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos. Phys Med, 2017,39:156-163.
[34]DiCenzo D, Quiaoit K, Fatima K, et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer:results from multi-institutional study. Cancer Med, 2020,9:5798-5806.
[35]Marino MA, Helbich T, Baltzer P, et al. Multiparametric MRI of the breast:a review. J Magn Reson Imaging, 2018,47:301-315.
[36]Liu ZY, Li ZL, Qu JR, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer:a multicenter study.Clin Cancer Res, 2019,25:3538-3547.
[37]Chen XG, Chen XF, Yang JD, et al. Combining dynamic contrast-enhanced magnetic resonance imaging and apparent diffusion coefficient maps for a radiomics nomogram to predict pathological complete response to neoadjuvant chemotherapy in breast cancer patients. J Comput Assist Tomogr, 2020,44:275-283.
[38]Fan M, Chen H, You C, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer. Front Mol Biosci, 2021,8:622219.
[39]Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res, 2020,22:57.
[40]Eun NL, Kang D, Son EJ, et al. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer. Radiology, 2020,294:31-41.
[41]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.
[42]Fusco R, Granata V, Maio F, et al. Textural radiomic features and time-intensity curve data analysis by dynamic contrastenhanced MRI for early prediction of breast cancer therapy response:preliminary data. Eur Radiol Exp, 2020,4:8.
基本信息:
DOI:
中图分类号:R737.9;R445
引用信息:
[1]代青立,杨敏,段庆红.影像组学在预测乳腺癌新辅助化疗疗效的研究进展[J].影像诊断与介入放射学,2021,30(04):293-298.
基金信息: