Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7–33
Gradishar WJ, Moran MS, Abraham J et al (2022) Breast cancer, version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw 20:691–722
Giuliano AE, Hunt KK, Ballman KV et al (2011) Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA 305:569–575
Article CAS PubMed PubMed Central Google Scholar
Zhu Y, Li X, Wang F et al (2018) Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging in characterization of axillary lymph nodes: preliminary animal experience. Magn Reson Imaging 52:46–52
Ahn HS, Jang M, Kim SM et al (2019) Usefulness of preoperative breast magnetic resonance imaging with a dedicated axillary sequence for the detection of axillary lymph node metastasis in patients with early ductal breast cancer. Radiol Med 124:1220–1228
Chung HL, Sun J, Leung JWT (2021) Breast cancer skip metastases: frequency, associated tumor characteristics, and role of staging nodal ultrasound in detection. AJR Am J Roentgenol 217:835–844
Pesek S, Ashikaga T, Krag LE, Krag D (2012) The false-negative rate of sentinel node biopsy in patients with breast cancer: a meta-analysis. World J Surg 36:2239–2251
Article PubMed PubMed Central Google Scholar
Li H, Yin L, He N et al (2019) Comparison of comfort between cone beam breast computed tomography and digital mammography. Eur J Radiol 120:108674
Zhu Y, O’Connell AM, Ma Y et al (2022) Dedicated breast CT: state of the art-part I. historical evolution and technical aspects. Eur Radiol 32:1579–1589
Zhu Y, O’Connell AM, Ma Y et al (2022) Dedicated breast CT: state of the art-part II. clinical application and future outlook. Eur Radiol 32:2286–2300
O’Connell AM, Karellas A, Vedantham S, Kawakyu-O’Connor DT (2018) Newer technologies in breast cancer imaging: dedicated cone-beam breast computed tomography. Semin Ultrasound CT MR 39:106–113
O’Connell AM, Marini TJ, Kawakyu-O’Connor DT (2021) Cone-beam breast computed tomography: time for a new paradigm in breast imaging. J Clin Med 10:5135
Article PubMed PubMed Central Google Scholar
Liu A, Ma Y, Yin L et al (2023) Comparison of malignant calcification identification between breast cone-beam computed tomography and digital mammography. Acta Radiol 64:962–970
He N, Wu YP, Kong Y et al (2016) The utility of breast cone-beam computed tomography, ultrasound, and digital mammography for detecting malignant breast tumors: a prospective study with 212 patients. Eur J Radiol 85:392–403
Wienbeck S, Uhlig J, Luftner-Nagel S et al (2017) The role of cone-beam breast-CT for breast cancer detection relative to breast density. Eur Radiol 27:5185–5195
Wienbeck S, Fischer U, Luftner-Nagel S, Lotz J, Uhlig J (2018) Contrast-enhanced cone-beam breast-CT (CBBCT): clinical performance compared to mammography and MRI. Eur Radiol 28:3731–3741
Uhlig J, Fischer U, Biggemann L, Lotz J, Wienbeck S (2019) Pre- and post-contrast versus post-contrast cone-beam breast CT: can we reduce radiation exposure while maintaining diagnostic accuracy? Eur Radiol 29:3141–3148
Zhu Y, Zhang Y, Ma Y et al (2020) Cone-beam breast CT features associated with HER2/neu overexpression in patients with primary breast cancer. Eur Radiol 30:2731–2739
Ma Y, Liu A, O’Connell AM et al (2021) Contrast-enhanced cone beam breast CT features of breast cancers: correlation with immunohistochemical receptors and molecular subtypes. Eur Radiol 31:2580–2589
Wienbeck S, Uhlig J, Fischer U et al (2019) Breast lesion size assessment in mastectomy specimens: correlation of cone-beam breast-CT, digital breast tomosynthesis and full-field digital mammography with histopathology. Medicine (Baltimore) 98:e17082
Wang Y, Zhao M, Ma Y et al (2023) Accuracy of preoperative contrast-enhanced cone beam breast CT in assessment of residual tumor after neoadjuvant chemotherapy: a comparative study with breast MRI. Acad Radiol 30:1805–1815
Ma Y, Cao Y, Liu A et al (2019) A reliability comparison of cone-beam breast computed tomography and mammography: breast density assessment referring to the fifth edition of the BI-RADS atlas. Acad Radiol 26:752–759
Liu A, Yin L, Ma Y et al (2022) Quantitative breast density measurement based on three-dimensional images: a study on cone-beam breast computed tomography. Acta Radiol 63:1023–1031
O’Connell A, Conover DL, Zhang Y et al (2010) Cone-beam CT for breast imaging: radiation dose, breast coverage, and image quality. AJR Am J Roentgenol 195:496–509
O’Connell AM, Kawakyu-O’Connor D (2012) Dedicated cone-beam breast computed tomography and diagnostic mammography: comparison of radiation dose, patient comfort, and qualitative review of imaging findings in BI-RADS 4 and 5 lesions. J Clin Imaging Sci 2:7
Article PubMed PubMed Central Google Scholar
Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E (2021) A deep look into radiomics. Radiol Med 126:1296–1311
Article PubMed PubMed Central Google Scholar
Vicini S, Bortolotto C, Rengo M et al (2022) A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med 127:819–836
Chen C, Qin Y, Chen H, Zhu D, Gao F, Zhou X (2021) A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients. Insights Imaging 12:156
Article PubMed PubMed Central Google Scholar
Gong X, Guo Y, Zhu T, Peng X, Xing D, Zhang M (2022) Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: a systematic review and meta-analysis. Front Oncol 12:1046005
Article PubMed PubMed Central Google Scholar
Caballo M, Hernandez AM, Lyu SH et al (2021) Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features. J Med Imaging (Bellingham) 8:024501
Caballo M, Pangallo DR, Sanderink W et al (2021) Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging. Med Phys 48:313–328
Ma J, He N, Yoon JH et al (2021) Distinguishing benign and malignant lesions on contrast-enhanced breast cone-beam CT with deep learning neural architecture search. Eur J Radiol 142:109878
Wang D, Hu Y, Zhan C, Zhang Q, Wu Y, Ai T (2022) A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer. Front Oncol 12:940655
Article PubMed PubMed Central Google Scholar
Liu Y, Li X, Zhu L et al (2022) Preoperative prediction of axillary lymph node metastasis in breast cancer based on intratumoral and peritumoral DCE-MRI radiomics nomogram. Contrast Media Mol Imaging 2022:6729473
Article PubMed PubMed Central Google Scholar
Zhang X, Yang Z, Cui W et al (2021) Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer. Eur Radiol 31:5924–5939
Qiu Y, Zhang X, Wu Z et al (2022) MRI-based radiomics nomogram: prediction of axillary non-sentinel lymph node metastasis in patients with sentinel lymph node-positive breast cancer. Front Oncol 12:811347
Article PubMed PubMed Central Google Scholar
Newell D, Nie K, Chen JH et al (2010) Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement. Eur Radiol 20:771–781
Gallego-Ortiz C, Martel AL (2016) Improving the accuracy of computer-aided diagnosis for breast MR imaging by differentiating between mass and nonmass lesions. Radiology 278:679–688
Ma Y, Liu A, Zhang Y et al (2022) Comparison of background parenchymal enhancement (BPE) on contrast-enhanced cone-beam breast CT (CE-CBBCT) and breast MRI. Eur Radiol 32:5773–5782
Article CAS PubMed Google Scholar
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107
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