| 1 |
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249.
|
| 2 |
Lambin P, Rios-velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer, 2012, 48(4): 441-6.
|
| 3 |
Luo WQ, Huang QX, Huang XW, et al. Predicting breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: a nomogram combining radiomics and BI-RADS[J]. Sci Rep, 2019, 9(1): 11921.
|
| 4 |
Du Y, Zha HL, Wang H, et al. Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma[J]. Br J Radiol, 2022, 95(1133): 20210598.
|
| 5 |
Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography[J]. Ultrasonics, 2016, 72: 150-157.
|
| 6 |
Zhang Q, Xiao Y, Suo J, et al. Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography[J]. Ultrasound Med Biol, 2017, 43(5): 1058-1069.
|
| 7 |
王世界, 刘华清, 张建兴, 等. 基于自动乳腺全容积成像影像组学的机器学习模型鉴别BI-RADS 4类病灶良恶性的临床价值[J]. 中华超声影像学杂志, 2023, 32(2): 136-143.
|
| 8 |
龚萱桐. 常规超声联合超声造影影像组学在诊断乳腺肿瘤良恶性及预测乳腺癌分子分型中的价值研究 [D]. 北京：中国医学科学院北京协和医学院, 2020.
|
| 9 |
Nicosia L, Pesapane F, Bozzini AC, et al. Prediction of the malignancy of a breast lesion detected on breast ultrasound: radiomics applied to clinical practice[J]. Cancers (Basel), 2023, 15(3): 964.
|
| 10 |
Bonacho T, Rodrigues F, Liberal J. Immunohistochemistry for diagnosis and prognosis of breast cancer: a review[J]. Biotech Histochem, 2020, 95(2): 71-91.
|
| 11 |
Juan MW, Yu J, Peng GX, et al. Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer[J]. Oncol Lett, 2018, 16(4): 5084-5090.
|
| 12 |
Ferre R, Elst J, Senthilnathan S, et al. Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes[J]. Breast Dis, 2023, 42(1): 59-66.
|
| 13 |
Guo Y, Wu J, Wang Y, et al. Development and validation of an ultrasound-based radiomics nomogram for identifying HER2 status in patients with breast carcinoma[J]. Diagnostics (Basel), 2022, 12(12): 3130.
|
| 14 |
Liu J, Wang X, Hu M, et al. Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer[J]. Front Oncol, 2022, 12: 963925.
|
| 15 |
Wu J, Ge L, Jin Y, et al. Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma[J]. Front Oncol, 2022, 12: 993466.
|
| 16 |
许荣, 欧阳秋芳, 林晴, 等. 超声影像组学预测雌激素受体及孕激素受体双阴性乳腺癌[J]. 中国医学影像技术, 2023, 39(9): 1346-1349.
|
| 17 |
Li N, Song C, Huang X, et al. Optimized radiomics nomogram based on automated breast ultrasound system: a potential tool for preoperative prediction of metastatic lymph node burden in breast cancer[J]. Breast Cancer (Dove Med Press), 2023, 15: 121-132.
|
| 18 |
Qiu X, Jiang Y, Zhao Q, et al. Could ultrasound-based radiomics noninvasively predict axillary lymph node metastasis in breast cancer?[J]. J Ultrasound Med, 2020, 39(10): 1897-1905.
|
| 19 |
Zhou LQ, Wu XL, Huang SY, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning[J]. Radiology, 2020, 294(1): 19-28.
|
| 20 |
Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J]. Nat Commun, 2020, 11(1): 1236.
|
| 21 |
刘晗, 徐楠, 吴杰, 等. 基于灰阶超声联合剪切波弹性成像的影像组学模型诊断乳腺癌腋窝淋巴结转移的临床价值[J]. 临床超声医学杂志, 2023, 25(4): 277-283.
|
| 22 |
Gu J, Tong T, Xu D, et al. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: a multicenter study[J]. Cancer, 2023, 129(3): 356-366.
|
| 23 |
Sun Q, Lin X, Zhao Y, et al. Deep learning vs radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region[J]. Front Oncol, 2020, 10: 53.
|
| 24 |
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[J]. Cancer Med, 2020, 9(16): 5798-5806.
|
| 25 |
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[J]. Eur J Cancer, 2021, 147: 95-105.
|
| 26 |
Yang M, Liu H, Dai Q, et al. Treatment response prediction using ultrasound-based pre-, post-early, and delta radiomics in neoadjuvant chemotherapy in breast cancer[J]. Front Oncol, 2022, 12: 748008.
|
| 27 |
Huang JX, Shi J, Ding SS, et al. Deep learning model based on dual-modal ultrasound and molecular data for predicting response to neoadjuvant chemotherapy in breast cancer[J]. Acad Radiol, 2023, 30Suppl 2: S50-S61.
|
| 28 |
Xiong L, Chen H, Tang X, et al. Ultrasound-based radiomics analysis for predicting disease-free survival of invasive breast cancer[J]. Front Oncol, 2021, 11: 621993.
|
| 29 |
Yu F, Hang J, Deng J, et al. Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study[J]. Br J Radiol, 2021, 94(1126): 20210188.
|
| 30 |
Choi BB. Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer[J]. World J Surg Oncol, 2021, 19(1): 76.
|
| 31 |
Ryu YJ, Kang SJ, Cho JS, et al. Lymphovascular invasion can be better than pathologic complete response to predict prognosis in breast cancer treated with neoadjuvant chemotherapy[J]. Medicine (Baltimore), 2018, 97(30): e11647.
|
| 32 |
Kayadibi Y, Kocak B, Ucar N, et al. MRI radiomics of breast cancer: machine learning-based prediction of lymphovascular invasion status[J]. Acad Radiol, 2022, 29Suppl 1: S126-S134.
|
| 33 |
查海玲, 潘加珍, 刘薇, 等. 基于超声影像组学模型预测浸润性乳腺癌淋巴管血管侵犯状态[J]. 肿瘤影像学, 2021, 30(1): 6-15.
|
| 34 |
范莉芳, 黄磊, 赵劲松, 等. 基于ABVS影像组学联合VTQ术前预测浸润性乳腺癌淋巴血管侵犯[J]. 放射学实践, 2023, 38(3): 342-348.
|