Table 5.
Studies on Prediction of Survival Outcomes in Patients With Breast Cancer.
| First Author, Year | Study Design | Number of Patients | MRI Modality | Magnetic Field | Radiomics Features | Studies Directions | Outcomes |
|---|---|---|---|---|---|---|---|
| Kim et al (2017)48 | Retrospective | 203 | DCE-MRI | 1.5 T | Texture features | To determine the relationship between tumor heterogeneity assessed by means of MRI texture analysis and survival outcomes in patients with primary breast cancer. | In multivariate analysis, a higher N stage (RFS hazard ratio, 11.15 (N3 stage); P = .002, Bonferroni adjusted a = 0.0167), triple-negative subtype (RFS hazard ratio, 16.91; P = .001, Bonferroni adjusted a = 0.0167), high risk of T1 entropy (less than the cutoff values (mean, 5.057; range, 5.022-5.167], RFS hazard ratio, 4.55; P = .018), and T2 entropy (equal to or higher than the cutoff values (mean, 6.013; range: 6.004-6.035), RFS hazard ratio = 9.84; P = .001) were associated with worse outcomes. |
| Chan et al (2017)49 | Retrospective | 563 | DCE-MRI | 1.5 T |
Not mentioned | We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors. | The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. |
| Drukker et al (2018)50 | Not mentioned | 162 | DCE-MRI | 1.5 T | Not mentioned | To predict recurrence-free survival “early on” in breast cancer neoadjuvant chemotherapy. | The C-statistics for the association of METV with recurrence-free survival were 0.69 with 95% confidence interval of 0.58-0.80 at pretreatment and 0.72 (0.60-0.84) at early treatment. The hazard ratios calculated from Kaplan-Meier curves were 2.28 (1.08-4.61), 3.43 (1.83-6.75), and 4.81 (2.16-10.72) for the lowest quartile, median quartile, and upper quartile cutpoints for METV at early treatment. |
| Park et al (2018)51 | Retrospective | 294 | DCE-MRI | 1.5T | Morphological, histogram-based features, and higher-order texture features. | To develop a radiomics signature to estimate DFS in patients with invasive breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and MRI and clinicopathological findings. | The radiomics nomogram estimated DFS (C-index, 0.76; 95% confidence interval (CI): 0.74-0.77) better than the clinicopathological (C-index, 0.72; 95% CI: 0.70-0.74) or Rad-score only nomograms (C-index, 0.67; 95% CI, 0.65-0.69). |
| Pickles et al (2016)52 | Retrospective | 112 | DCE-MRI | 3.0 T | Texture, shape features | To determine if associations exist between pretreatment DCE-MRI and survival intervals and compare the prognostic value of DCE-MRI parameters against traditional pretreatment survival indicators. | Accuracy of risk stratification based on either traditional (59%) or DCE-MRI (65%) survival indicators performed to a similar level. However, combined traditional and MR risk stratification resulted in the highest accuracy (86%). |
Abbreviations: AUC, area under the curve; CI, confidence interval; DCE, dynamic contrast-enhanced; DFS, disease-free survival; MRI, magnetic resonance imaging; ROC, receive operating characteristics; RFS, recurrence-free survival; METV, the most enhancing tumor volume.