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