[1]
|
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66: 115-132. doi: 10.3322/caac.21338 |
[2]
|
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48: 441-446. doi: 10.1016/j.ejca.2011.11.036 |
[3]
|
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278: 563-577. doi: 10.1148/radiol.2015151169 |
[4]
|
Hooley RJ, Scoutt LM, Philpotts LE. Breast ultrasonography: state of the art[J]. Radiology, 2013, 268: 642-659. doi: 10.1148/radiol.13121606 |
[5]
|
Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: A review[J]. J Magn Reson Imaging, 2019, 49: 927-938. doi: 10.1002/jmri.26556 |
[6]
|
Jiang Y, Edwards AV, Newstead GM. Artificial Intelli-gence Applied to Breast MRI for Improved Diagnosis[J]. Radiology, 2021, 298: 38-46. doi: 10.1148/radiol.2020200292 |
[7]
|
Li J, Sang T, Yu WH, et al. The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis[J]. Med Ultrason, 2020, 22: 211-219. doi: 10.11152/mu-2402 |
[8]
|
Mao N, Yin P, Wang Q, et al. Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study[J]. J Am Coll Radiol, 2019, 16: 485-491. doi: 10.1016/j.jacr.2018.09.041 |
[9]
|
McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening[J]. Nature, 2020, 577: 89-94. doi: 10.1038/s41586-019-1799-6 |
[10]
|
Li H, Mendel KR, Lan L, et al. Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma[J]. Radiology, 2019, 291: 15-20. doi: 10.1148/radiol.2019181113 |
[11]
|
Zhou J, Zhang Y, Chang KT, et al. Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue[J]. J Magn Reson Imaging, 2020, 51: 798-809. doi: 10.1002/jmri.26981 |
[12]
|
Lee SE, Han K, Kwak JY, et al. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma[J]. Sci Rep, 2018, 8: 13546. doi: 10.1038/s41598-018-31906-4 |
[13]
|
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: 11921. doi: 10.1038/s41598-019-48488-4 |
[14]
|
Hao W, Gong J, Wang S, et al. Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment[J]. Front Oncol, 2020, 10: 531476. doi: 10.3389/fonc.2020.531476 |
[15]
|
Zhang X, Liang M, Yang Z, et al. Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classifica-tion[J]. Front Oncol, 2020, 10: 1621. doi: 10.3389/fonc.2020.01621 |
[16]
|
Wilke LG, McCall LM, Posther KE, et al. Surgical complications associated with sentinel lymph node biopsy: results from a prospective international cooperative group trial[J]. Ann Surg Oncol, 2006, 13: 491-500. doi: 10.1245/ASO.2006.05.013 |
[17]
|
Hindié E, Groheux D, Brenot-Rossi I, et al. The sentinel node procedure in breast cancer: nuclear medicine as the starting point[J]. J Nucl Med, 2011, 52: 405-414. doi: 10.2967/jnumed.110.081711 |
[18]
|
Cui X, Wang N, Zhao Y, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI[J]. Sci Rep, 2019, 9: 2240. doi: 10.1038/s41598-019-38502-0 |
[19]
|
Liu J, Sun D, Chen L, et al. Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer[J]. Front Oncol, 2019, 9: 980. doi: 10.3389/fonc.2019.00980 |
[20]
|
Schiano C, Franzese M, Pane K, et al. Hybrid (18)F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study[J]. Cancers (Basel), 2019, 11: 1444. doi: 10.3390/cancers11101444 |
[21]
|
Yang J, Wang T, Yang L, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method[J]. Sci Rep, 2019, 9: 4429. doi: 10.1038/s41598-019-40831-z |
[22]
|
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[J]. Eur Radiol, 2020, 30: 6732-6739. doi: 10.1007/s00330-020-07016-z |
[23]
|
Giuliano AE, Ballman KV, McCall L, et al. Effect 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[J]. JAMA, 2017, 318: 918-926. doi: 10.1001/jama.2017.11470 |
[24]
|
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: 1236. doi: 10.1038/s41467-020-15027-z |
[25]
|
Gao Y, Luo Y, Zhao C, et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients[J]. Eur Radiol, 2021, 31: 928-937. doi: 10.1007/s00330-020-07181-1 |
[26]
|
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[J]. EBioMedicine, 2020, 60: 103018. doi: 10.1016/j.ebiom.2020.103018 |
[27]
|
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]. J Magn Reson Imaging, 2019, 49: 131-140. doi: 10.1002/jmri.26224 |
[28]
|
Goetz MP, Gradishar WJ, Anderson BO, et al. NCCN Guidelines Insights: Breast Cancer, Version 3.2018[J]. J Natl Compr Canc Netw, 2019, 17: 118-126. doi: 10.6004/jnccn.2019.0009 |
[29]
|
Li P, Wang X, Xu C, et al. (18)F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients[J]. Eur J Nucl Med Mol Imaging, 2020, 47: 1116-1126. doi: 10.1007/s00259-020-04684-3 |
[30]
|
Chen S, Shu Z, Li Y, et al. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients[J]. Front Oncol, 2020, 10: 1410. doi: 10.3389/fonc.2020.01410 |
[31]
|
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[J]. Clin Cancer Res, 2019, 25: 3538-3547. doi: 10.1158/1078-0432.CCR-18-3190 |
[32]
|
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[J]. Eur J Nucl Med Mol Imaging, 2019, 46: 1468-1477. doi: 10.1007/s00259-019-04313-8 |
[33]
|
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[J]. Breast Cancer Res, 2017, 19: 57. doi: 10.1186/s13058-017-0846-1 |
[34]
|
Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy[J]. Breast Cancer Res, 2020, 22: 57. doi: 10.1186/s13058-020-01291-w |
[35]
|
Bitencourt AGV, Gibbs P, Rossi Saccarelli C, et al. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer[J]. EBioMedicine, 2020, 61: 103042. doi: 10.1016/j.ebiom.2020.103042 |
[36]
|
Moscoso A, Ruibal A, Dominguez-Prado I, et al. Texture analysis of high-resolution dedicated breast (18) F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer[J]. Eur J Nucl Med Mol Imaging, 2018, 45: 196-206. doi: 10.1007/s00259-017-3830-1 |
[37]
|
Antunovic L, Gallivanone F, Sollini M, et al. [(18)F]FDG PET/CT features for the molecular characterization of primary breast tumors[J]. Eur J Nucl Med Mol Imaging, 2017, 44: 1945-1954. doi: 10.1007/s00259-017-3770-9 |
[38]
|
Fan M, Yuan W, Zhao W, et al. Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics[J]. IEEE J Biomed Health Inform, 2020, 24: 1632-1642. doi: 10.1109/JBHI.2019.2956351 |
[39]
|
Yang X, Wu L, Zhao K, et al. Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features[J]. Chin J Cancer Res, 2020, 32: 175-185. doi: 10.21147/j.issn.1000-9604.2020.02.05 |
[40]
|
Leithner D, Bernard-Davila B, Martinez DF, et al. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes[J]. Mol Imaging Biol, 2020, 22: 453-461. doi: 10.1007/s11307-019-01383-w |
[41]
|
Parekh VS, Jacobs MA. Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging[J]. Breast Cancer Res Treat, 2020, 180: 407-421. doi: 10.1007/s10549-020-05533-5 |
[42]
|
Leithner D, Mayerhoefer ME, Martinez DF, et al. Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics[J]. J Clin Med, 2020, 9: 1853. doi: 10.3390/jcm9061853 |
[43]
|
Xie T, Wang Z, Zhao Q, et al. Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer[J]. Front Oncol, 2019, 9: 505. doi: 10.3389/fonc.2019.00505 |
[44]
|
Huang SY, Franc BL, Harnish RJ, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis[J]. NPJ Breast Cancer, 2018, 4: 24. doi: 10.1038/s41523-018-0078-2 |
[45]
|
Park H, Lim Y, Ko ES, et al. Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer[J]. Clin Cancer Res, 2018, 24: 4705-4714. doi: 10.1158/1078-0432.CCR-17-3783 |
[46]
|
Kim S, Kim MJ, Kim EK, et al. MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer[J]. Sci Rep, 2020, 10: 3750. doi: 10.1038/s41598-020-60822-9 |
[47]
|
Koh J, Lee E, Han K, et al. Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence[J]. Sci Rep, 2020, 10: 2976. doi: 10.1038/s41598-020-59923-2 |