Skip to main content
. 2020 May 29;21(7):779–792. doi: 10.3348/kjr.2019.0855

Table 1. Summary of Representative Radiomics Research in Breast Imaging.

Reference Indication Modality Patients (Number) Radiomics Features (Number) Findings
Bickelhaupt et al. (49) Malignancy prediction MRI: DWI, T2WI 222 359 Radiomics features are better than only using ADC alone
Nie et al. (50) Malignancy prediction MRI: DCE 71 18 Quantitative morphologic and texture features analysis showed reasonably high accuracy
Wang et al. (51) Malignancy prediction MRI: DCE 99 30 Radiomics features and pharmacokinetic factors differentiated benign and malignant masses
Cai et al. (52) Malignancy prediction MRI: DCE, DWI 234 28 Developed GLCM-based features from DCE-MRI with ADC as well as kinetic and morphological features
Parekh & Jacobs (53) Malignancy prediction MRI: DCE, T2WI, DWI 124 30 Entropy RFMs were found to be most reliable
Garra et al. (54) Malignancy prediction US 80 14 Sensitivity of 100% and specificity of 80% were found
Luo et al. (55) Malignancy prediction US 315 1044 Radiomics nomograms showed better discrimination than radiomics scores or BI-RADS category
Zhang et al. (56) Malignancy prediction US: conventional, 117 364 Results of sonoelastomic features showed AUC of 0.917 and accuracy of 88% in validation set
Drukker et al. (57) Malignancy prediction Mammogram: conventional, three-compartment (water, lipid, protein) image from dual energy mammogram 109 5 Combined mammography radiomics plus quantitative three-compartment image analysis prospectively showed better PPV3
Li et al. (58) Malignancy prediction Mammogram 182 32 Combining contralateral normal breast radiomic features with those of lesion showed better performance
Tagliafico et al. (59) Malignancy prediction DBT 40 104 Radiomics analysis of DBT could be used to facilitate cancer detection and characterization in multicenter prospective study
Holli et al. (60) Differentiation between ILC and IDC MRI: DCE, 20 300 Entropy-based GLCM 2020-06-09features and first subtraction were most effective
Waugh et al. (61) Differentiation between ILC and IDC MRI: DCE 200 220 Entropy was significantly different between IDC
Li et al. (62) Correlation with pathology MRI: DCE 91 38 MRI-based phenotypes were significantly associated with receptor status and heterogeneity was important feature to discriminate different subtypes
Liang et al. (63) Ki-67 correlation MRI: DCE, T2WI 318 10207 Rad-score from T2WI was significantly associated with Ki-67 status
Marino et al. (64) Correlation with pathology Mammogram: contrast-enhanced 100 300 Radiomics analysis with CEM has potential for differentiating tumors with different pathologic findings
Ahmed et al. (65) NAC response MRI: DCE 100 16 Texture features showed significant differences between non-responders and partial responders
Braman et al. (66) NAC response MRI: DCE 117 99 Peritumoral radiomics contributed to accurate response prediction
Braman et al. (67) NAC response MRI: DCE 209 495 Peritumoral radiomics were useful in characterizing HER2+ tumors and estimating response to HER2-targeted therapy
Liu et al. (68) NAC response MRI: T2WI, DWI, DCE 586 13950 Radiomics of multiparametric MRI yielded better performance to predict pCR than clinical model
Dong et al. (69) LN metastasis prediction MRI: T2WI, DWI 146 10962 Radiomics features from DWI showed higher correlation with SLN metastases than those from ADC mapping
Yang et al. (70) LN metastasis predcition Mammogram 147 45 Radiomics nomogram can predict LN metastasis
Yu et al. (71) LN metastasis prediction US 426 96 Radiomics nomogram can predict LN metastasis
Chan et al. (72) Cancer recurrence prediction MRI: DCE 563 322 Radiomics model discriminate between patients at low risk and those at high risk of recurrence
Park et al. (23) Cancer recurrence prediction MRI: DCE 294 156 Higher rad-score was correlated with worse disease-free survival

ADC = apparent diffusion coefficient, AUC = area under curve, BI-RADS = breast imaging reporting and data system, CEM = contrast-enhanced mammography, DBT = digital breast tomosynthesis, DCE = dynamic contrast-enhanced, DWI = diffusion-weighted imaging, GLCM = gray-level co-occurrence matrix, HER2 = human epidermal growth factor receptor 2, IDC = invasive ductal carcinoma, ILC = invasive lobular carcinoma, LN = lymph node, MRI = magnetic resonance imaging, NAC = neoadjuvant chemotherapy, pCR = pathologic complete response, PPV3 = positive predictive value 3, RFM = radiomics feature maps, SLN = sentinel lymph node, T1WI = T1-weighted image, T2WI = T2 weighted image, US = ultrasound