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乳腺癌影像组学的研究进展(4)
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摘要:[22] Liu MJ, Mao N, Ma H, et al. Radiomics based on DCE-MRI for the preoperative prediction of SLN metastasis in breast cancer. Chin Imaging J Integr Tradit West Med,2020,18(3)::10.3969/刘梅婕,毛宁
[22] Liu MJ, Mao N, Ma H, et al. Radiomics based on DCE-MRI for the preoperative prediction of SLN metastasis in breast cancer. Chin Imaging J Integr Tradit West Med,2020,18(3)::10.3969/刘梅婕,毛宁,马恒,等.基于影像组学构建乳腺癌前哨淋巴结转移预测模型的研究. 中国中西医结合影像学杂志, 2020, 18(3)::10.3969/
[23] Giuliano AE,Ballman KV,McCall L,et of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (Alliance) randomized clinical trial. JAMA, 2017, 318(10)::10.1001/
[24] Guo X, Liu Z, Sun C, et al. Deep learning radiomics of ultrasonography:identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer. EBioMedicine, 2020, 60: . DOI:10.1016/
[25] Gradishar WJ, Anderson BO, Balassanian R, et al. Breast cancer,version 4.2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2018, 16(3): 310-320. DOI: 10.6004/
[26] Rivenbark AG, O'Connor SM, and Coleman WB. Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine. Am J Pathol, 2013, 183(4): 1113-1124. DOI: 10.1016/
[27] Liu Z, Li Z, Qu J, 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(12)::10.1158/
[28] 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(1): 57.DOI:10.1186/s-020-01291-w
[29] Nam KJ, Park H, Ko ES, et al. Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptorpositive invasive breast cancers: preliminary results for correlation with oncotype DX recurrence scores. Medicine (Baltimore), 2019,98(23):e.DOI:10.1097/MD.5871
[30] Shao L, Liu Z, Feng L, et al. Multiparametric MRI and whole slide image-based pretreatment prediction of pathological response to neoadjuvant chemoradiotherapy in rectal cancer: a multicenter radiopathomic study. Ann Surg Oncol, 2020, 27(11): 4296-4306. DOI: 10.1245/s-020-08659-4
[31] Mao N, Yin P, Li Q, et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol, 2020, 30(12): :10.1007/s00330-020-07016-z
[32] Lin F, Wang Z, Zhang K, et al. Contrast-enhanced spectral mammography-based radiomics nomogram for identifying benign and malignant breast lesions of Sub-1 cm. Front Oncol, 2020, 10: .DOI:10.3389/
[33] Mao N, Yin P, Wang Q, et al. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol, 2019, 16(4 Pt A): 485-491. DOI:10.1016/
[34] Sun Q,Lin X,Zhao Y,et al.Deep learning for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region. Front Oncol, 2020, 10: 53.DOI:10.3389/00053
[35] Qu YH, Zhu HT, Cao K, et al. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method. Thorac Cancer, 2020, 11(3): 651-658. DOI:10.1111/1759-7714.
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