MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy
Tóm tắt
Từ khóa
Tài liệu tham khảo
Holmes, 2015, Performance and practice guideline for the use of neoadjuvant systemic therapy in the management of breast cancer, Ann Surg Oncol, 22, 3184, 10.1245/s10434-015-4753-3
Mougalian, 2016, Ten-year outcomes of patients with breast cancer with cytologically confirmed axillary lymph node metastases and pathologic complete response after primary systemic chemotherapy, JAMA Oncol, 2, 508, 10.1001/jamaoncol.2015.4935
Mieog JS, van der Hage JA, van de Velde CJ. Preoperative chemotherapy for women with operable breast cancer. Cochrane Database Syst Rev 2007:CD005002. http://doi.org/10.1002/14651858.CD005002.pub2
Cortazar, 2014, Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis, Lancet, 384, 164, 10.1016/S0140-6736(13)62422-8
Symmans, 2007, Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy, J Clin Oncol, 25, 4414, 10.1200/JCO.2007.10.6823
Symmans, 2017, Long-term prognostic risk after neoadjuvant chemotherapy associated with residual cancer burden and breast cancer subtype, J Clin Oncol, 35, 1049, 10.1200/JCO.2015.63.1010
Fan, 2017, Radiomics analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients, Eur J Radiol, 94, 140, 10.1016/j.ejrad.2017.06.019
Thibault, 2017, DCE-MRI texture features for early prediction of breast cancer therapy response, Tomography, 3, 23, 10.18383/j.tom.2016.00241
Machireddy, 2019, Early prediction of breast cancer therapy response using multiresolution fractal analysis of DCE-MRI parametric maps, Tomography, 5, 90, 10.18383/j.tom.2018.00046
Wu, 2016, Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy, J Magn Reson Imaging, 44, 1107, 10.1002/jmri.25279
Chamming's, 2018, Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy, Radiology, 286, 412, 10.1148/radiol.2017170143
Teruel, 2014, Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer, NMR Biomed, 27, 887, 10.1002/nbm.3132
Henderson, 2017, Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer, Eur Radiol, 27, 4602, 10.1007/s00330-017-4850-8
Eun, 2020, Texture analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer, Radiology, 294, 31, 10.1148/radiol.2019182718
Drukker, 2019, Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients, J Med Imaging (Bellingham), 6
Aghaei, 2015, Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy, Med Phys, 42, 6520, 10.1118/1.4933198
Aghaei, 2016, Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy, J Magn Reson Imaging, 44, 1099, 10.1002/jmri.25276
Braman, 2019, Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer, JAMA Netw Open, 2, 10.1001/jamanetworkopen.2019.2561
Liu, 2019, Radiomics of multiparametric mri for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study, Clin Cancer Res, 25, 3538, 10.1158/1078-0432.CCR-18-3190
Braman, 2017, Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI, Breast Cancer Res, 19, 57, 10.1186/s13058-017-0846-1
Parikh, 2014, Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy, Radiology, 272, 100, 10.1148/radiol.14130569
Ahmed, 2013, Texture analysis in assessment and prediction of chemotherapy response in breast cancer, J Magn Reson Imaging, 38, 89, 10.1002/jmri.23971
Chitalia, 2019, Role of texture analysis in breast MRI as a cancer biomarker: A review, J Magn Reson Imaging, 49, 927, 10.1002/jmri.26556
Jiang, 2013, Blood oxygenation level-dependent (BOLD) contrast magnetic resonance imaging (MRI) for prediction of breast cancer chemotherapy response: a pilot study, J Magn Reson Imaging, 37, 1083, 10.1002/jmri.23891
Li, 2015, Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer, Invest Radiol, 50, 195, 10.1097/RLI.0000000000000100
Chan Tony F, 2001, Active contours without edges, IEEE Trans. Image Process., 10, 266, 10.1109/83.902291
Goldhirsch, 2011, 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, 22, 1736, 10.1093/annonc/mdr304
Furnival, 1974, Regression by Leaps and Bounds, Technometrics, 16, 499, 10.1080/00401706.1974.10489231
Kim, 2017, Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes, Radiology, 282, 665, 10.1148/radiol.2016160261
Koren, 2015, Breast Tumor Heterogeneity: Source of Fitness, Hurdle for Therapy, Mol Cell, 60, 537, 10.1016/j.molcel.2015.10.031
Januskeviciene, 2019, Heterogeneity of breast cancer: The importance of interaction between different tumor cell populations, Life Sci, 239, 10.1016/j.lfs.2019.117009
Li, 2016, Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set, NPJ Breast Cancer, 2, 10.1038/npjbcancer.2016.12
Ko, 2016, Assessment of invasive breast cancer heterogeneity using whole-tumor magnetic resonance imaging texture analysis: correlations with detailed pathological findings, Medicine (Baltimore), 95, e2453, 10.1097/MD.0000000000002453
Bianchini, 2014, The immune system and response to HER2-targeted treatment in breast cancer, Lancet Oncol, 15, e58, 10.1016/S1470-2045(13)70477-7
Denkert, 2018, Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy, Lancet Oncol, 19, 40, 10.1016/S1470-2045(17)30904-X
Grimm, 2015, Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms, J Magn Reson Imaging, 42, 902, 10.1002/jmri.24879
Michishita, 2015, Prediction of pathological complete response to neoadjuvant chemotherapy by magnetic resonance imaging in breast cancer patients, Breast, 24, 159, 10.1016/j.breast.2015.01.001