Table 3.
Studies on Predicting Molecular Subtypes of Breast Cancer.
| First Author, Year | Study Design | Number of Patients | MRI Modality | Magnetic Field | Radiomics Features | Studies Directions | Outcomes |
|---|---|---|---|---|---|---|---|
| Kirsi Holli-Helenius et al (2017)22 | Not mentioned | 27 | DCE-MRI, DWI-MRI | 1.5 T | Texture features | To assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes. | The 2 most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (P = .003). The AUCs were 0.828 for sum entropy (P = .004), 0.833 for sum variance (P = .003), and 0.878 for the model combining texture features sum entropy, sum variance (P = .001). |
| Fan et al, (2018)23 | Retrospective | 77 | DCE-MRI | 3.0 T | Texture features (GLCM) | To predict the Ki-67 status of patients with estrogen receptor (ER)-positive breast cancer. | Multivariate analysis showed that features from the tumor subregions associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. |
| Fan et al (2017)24 | Retrospective | 60 | DCE-MRI | 1.5 T | First-order statistics, texture features. | Prediction of the molecular subtypes of breast cancer. | The predictive model discriminated among the luminal A, luminal B, HER2, and basal-like subtypes, with AUC values of 0.867, 0.786, 0.888, and 0.923. |
| Fan et al (2019)25 | Retrospective | 211 | DCE-MRI | 3.0 T | Texture features | To predict the molecular subtypes of breast cancer. | The tumor subregion related to fast-flow kinetics showed the best performance among the subregions for differentiating between patients with 4 molecular subtypes (AUC = 0.832). |
| Grimm et al (2015)26 | Retrospective | 275 | T1WI, T2-FS | 1.5 T/3.0 T | Size, shape, gradient, texture, and dynamic features. | To characterize the relationship between breast MRI and molecular subtype. | The imaging features were associated with both luminal A and luminal B molecular subtypes. No association was found for either HER2 or basal molecular subtype and the imaging features. |
| Juan et al (2018)27 | Retrospective | 159 | DCE-MRI | 3.0 T | Morphological features, gray-scale histograms and texture features. | To investigate the association between Ki-67 expression and radiomics features in patients with invasive breast cancer. | One morphology metric (area), 3 gray-scale histogram indexes (standard deviation, skewness and kurtosis) and 3 texture features (contrast, homogeneity and inverse differential moment) demonstrated a significant difference. |
| Ko et al (2016)28 | Not mentioned | 75 | DCE-MRI | 1.5 T | Texture features. | To investigate whether texture analysis of magnetic resonance images correlates with histopathological findings | High histologic grades showed increased uniformity and decreased entropy on contrast-enhanced T1-weighted subtraction images, whereas the opposite tendency was observed on T2-weighted images. |
| Liang et al (2018)29 | Retrospective | 318 | T2WI, T1 + C | 1.5 T | Intensity, shape, texture, and wavelet features. | To predict the Ki-67 status in patients with breast cancer. | The T2WI-based radiomics classifier exhibited good discrimination for Ki-67 status, with areas under the receiver operating characteristic curves of 0.762 (95% CI: 0.685-0.838) and 0.740 (95% CI: 0.645-0.836) in the training and validation data sets. |
| Ma et al (2018)30 | Retrospective | 377 | DCE-MRI | 3.0 T | Morphological, gray scale statistic, and texture features, | To investigate whether quantitative radiomics features are associated with Ki67 expression of breast cancer. | The model that used naive Bayes classification method achieved the best performance than the other 2 methods, yielding 0.773 AUC, 0.757 accuracy, 0.777 sensitivity, and 0.769 specificity. |
| Monti et al (2018)31 | Not mentioned | 49 | DCE-MRI | 3.0 T | Shape features | To build predictive models for the discrimination of molecular receptor status (ER+/ER−, PR+/PR−, and HER2+/HER2−), TN/nontriple negative (NTN), ki67 levels, and tumor grade. | The predictive model coefficients were computed for each classification task. The AUC values obtained were 0.826 ± 0.006 for ER+/ER−, 0.875 ± 0.009 for PR+/PR−, 0.838 ± 0.006 for HER2+/HER2-, 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67 + /ki67−, and 0.895 ± 0.006 for low grade/high grade. |
| Saha et al (2018)32 | Not mentioned | 922 | DCE-MRI | 1.5 T/3.0 T | Texture features | To investigate features published in the literature as well as those developed in laboratory to find the association between molecular subtype and features. | Multivariate models were predictive of luminal A subtype with AUC = 0.697, triple-negative breast cancer with AUC = 0.654, ER status with AUC = 0.649 (95% CI: 0.591-0.705). |
| Sun et al (2018)33 | Retrospective | 107 | Axial fast spin echo (FSE) T1WI, T2WI- FS, DWI | 1.5 T/3.0 T | Texture features | To investigate the molecular subtypes of breast cancer. | The differentiation accuracies of Fisher discriminant analysis on the enhanced high-resolution T1WI were 82.8% and 86.4% for 1.5 T and 3.0 T imaging. Fisher discriminant analysis on DWI texture features were achieved with a classification ability of 73.4% and 88.6%. The combined discriminant results for 2 kinds of magnetic resonance images were 95.0%, 97.7% in 1.5 T, and 3.0 T imaging, respectively. |
| Wang et al (2015)34 | Not mentioned | 88 | DCE-MRI | 3.0 T | Morphologic, densitometric, and statistical texture measures of enhancement | To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on DCE-MRI in identifying TN breast cancer. | Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (P < .01). |
| Xie et al (2019)35 | retrospective | 134 | DCE-MRI, DWI-MRI | 3.0 T | Histogram analysis | To identify TN breast cancer imaging biomarkers in comparison to other molecular subtypes using multiparametric MR imaging maps and whole tumor histogram analysis. | The significant parameters on the univariate analysis achieved an AUC of 0.710 with a sensitivity of 63.6% and a specificity of 73.1% at the best cutoff point for differentiating TN cancers from luminal A cancer. An AUC of 0.763 (95% CI: 0.608-0.917) with a sensitivity of 86.4% and a specificity of 72.2% was achieved for differentiating TN cancers from HER2 positive cancers. Also, an AUC of 0.683 with a sensitivity of 54.5% and a specificity of 83.9% was achieved for differentiating TN cancers from non-TN cancers. |
Abbreviations: AUC, area under the curve; CI, confidence interval; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; ER+, positive estrogen receptor; ER−, negative estrogen receptor; GLCM, gray-level co-occurrence matrix; HER-2+, positive human epidermal growth factor receptor 2; HER-2+, negative human epidermal growth factor receptor 2; MRI, magnetic resonance imaging; PR+, positive progesterone receptor; PR−, negative progesterone receptor; T2-FS, T2-weighted fat suppression; T2WI, T2-weighted image; TN, triple negative; TTP, time to peak.