Table 2.
Studies on Prediction of Axillary Lymph Node Metastasis.
| First Author, Year | Study Design | Number of Patients | MRI Modality | Magnetic Field | Radiomics Features | Outcomes |
|---|---|---|---|---|---|---|
| Chai et al (2019)16 | Retrospective | 120 | DCE-MRI | 3.0 T | Morphological and texture features. | The accuracy/AUC of the 4 sequences was 79%/0.87, 77%/0.85, 74%/0.79, and 79%/0.85 for the T1WI, CE2, T2WI, and DWI, respectively. When CE2 was augmented by adding kinetic features, the model achieved the highest performance (accuracy = 0.86 and AUC = 0.91). |
| Cui et al (2019)17 | Retrospective | 102 | DCE-MRI | 3.0 T | Morphological, NGLDM, GLRLM, GLCM, GLGCM, Tamura, and grayscale histogram features. | The SVM classifier performed best, with the highest accuracy of 89.54%, and obtained an AUC of 0.8615 for identifying the lymph node status. |
| Dong et al (2018)18 | Retrospective | 146 | T2FS, DWI | 1.5 T | Nontexture and texture parameter features. | Model of T2-FS yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. Model of DWI reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. Combination of T2-FS and DWI features yielded an AUC of 0.863 in the training set and 0.805 in the validation set. |
| Han et al (2019)19 | Retrospective | 411 | DCE-MRI | 1.5 T | Shape features, first-order features, textural features | The AUC of radiomic signature was 0.76 and 0.78 in training and validation cohorts, respectively. Another radiomic signature was constructed to distinguish the number of metastatic LNs, which also showed moderate performance (AUC = 0.79). |
| Liu et al (2019)20 | Retrospective | 163 | DCE-MRI | 1.5 T | Shape features, histogram features, texture features, and Laws features. | In the independent validation set, combining radiomics features and clinicopathologic characteristics, AUC was 0.869. Using radiomic features alone in the same procedure, the validation set AUC was 0.806. |
| Liu et al (2019)21 | Prospective | 149 | DCE-MRI | 1.5 T/3.0 T | First-order statistics, shape- and size-based features, wavelet-based features, and texture-based features. | The value of AUC for a combined model (0.763) was higher than that for MRI ALN status alone (0.665; P = .029) and similar to that for the radiomics signature (0.752; P = .857). |
Abbreviations: ALN, axillary lymph node; AUC, area under the curve; CE2, second postcontrast phase; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run length matrix; LNs, lymph nodes; MRI, magnetic resonance imaging; SVM, support vector machine; T1WI, T1-weighted image; T2-FS, T2-weighted fat suppression; T2WI, T2-weighted image; NGLDM, Neighboring Gray-Level Dependence Matrix; GLGCM, Gray Level-Gradient Co-occurrence Matrix.