## 3 Results
### Early Unsupervised Models  
Early-stage ML approaches to automatic segmentation mainly rely on the application and refinement of basic clustering techniques, such as FCM, K-means, and mean shift clustering, investigated respectively by [PERSON] et al. [31], [PERSON] et al. [32], and [PERSON] et al. [33]. In general, information from the whole DCE-MRI and its time-intensity curve (TIC) is used to generate the segmentation mask of the lesion; however, complementary information from anatomical T1w and T2w images has also been used, for instance, by [PERSON] et al. [32]. Different preprocessing and postprocessing procedures have been employed, reflecting the use of clustering both as a final step or as a baseline requiring further operations. Figure 2: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram showing the different phases of the review. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Paper** & **Magnetic Field** & **MR Field** & **Patient Technique** & **Question** & **Algorithm** & **Approation** & **Suppressed Approach** & **Validation** & **Performance** \\ \hline \hline [PERSON] et al. 2014[23] & 1.5 T & DCE, & \(\frac{\text{T}\text{T}\text{T}\text{T}\text{T}\text{T}\text{T}\text{T}}\) & 128 & detection and augmentation of suspicious tissue in breast & mean-shift clustering and graph-cuts on a region adjacency graph & No & train: 35 cases & median DCE, 1 stst set 1: 0.76 (0.08 0.17) & test set 1: 0.76 (0.06 0.16) & test set 1: 2.9 cases & test set 1: 0.75 (0.06 0.16) & test set 1: 0.25 (0.07 0.16) & test set 2: 0.75 (0.06 0.16) & mean DCE \\ \hline [PERSON] et al. 2014[24] & 3.0 T & DCE & 24 & segmentation of invasive digital carcinoma & time series analysis based on linear dynamic system and FCM & No & test 24 cases & vs. radiologist (0.77) & mean DCE \\ \hline [PERSON] et al. 2015[24] & 1.5 T, 3.0 T & DCE & 36 & segmentation of breast lesions & image manifold prevailing and k-means clustering & No & test 36 cases & mean accuracy (0.97 \(\pm\) 0.01) & mean specificity (0.98 \(\pm\) 0.01) & mean sensitivity: 0.90 \(\pm\) 0.04 \\ \hline [PERSON] et al. 2019[24] & 3.0 T & DCE, DWI, + cylinder-PEL & 34 & segmentation of breast lesions and characterization (bm, vs. mall.) & yeast value random forest classifier based on local local similarity-based, and kinetic features & Yes & train: 1000 voxels & test set 2: 0.65 (0.236) & mean 0.66 (0.04 0.189) & mean 0.757 (0.06 0.189) & mean 0.757 (0.06 0.198) & mean 0.757 (0.06 0.198) & mean 0.757 (0.06 0.198) & mean DCE \\ \hline [PERSON] et al. 2020[24] & 1.5 T & DCE, Tinv,Tinv & 117 & detection, segmentation and characterization of breast lesions (bm, vs. mall.) & lesion candidates generated by 1000 voxels & Yes & train: 58 cases & DSC & mean 0.72 (0.15 0.07 0.18) & mean 0.72 (0.74 0.18) & mean 0.74 \\ \hline [PERSON] et al. 2019[24] & 1.5 T & DCE & 317 & segmentation of breast lesions, prediction of sentinel lymph node status & U-Net & Yes & train: 253 cases & mean DSC (Net: 54 cases & vs. Radiologist : 0.71 0.16 0.17 0.18 0.18 0.17 0.18 0.18 0.18 0.07 0.18 0.07 0.18 0.07 0.18 0.07 0.18 & mean DSC (0.70 0.07 0.07) & mean DSC (0.70 0.07) & mean DSC (0.70 0.07) & mean DSC (0.70 0.07) \\ \hline [PERSON] et al. 2022[24] & 1.5 T & DCE & 46 & breast lesion segmentation & ensemble of 3.0 D patch-based modified U-Net & Yes & 5-fold CV & mean DSC (0.88 0.07) & mean lesion only: 0.80 \\ \hline [PERSON] et al. 2023[24] & - & DCE, TIC & 141 & segmentation of breast lesions & visual ensemble of CNN & Yes & train: 111 cases & mean DSC (0.79 \(\pm\) 0.16 0.05) & mean DSC (0.79 \(\pm\) 0.16 0.05) & mean DSC (0.70 0.07) & mean DSC (0.70 0.07) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of breast lesion segmentation studies. Model performances are presented using mean Dice scores (DSC) or equivalent metrics. \begin{table}
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