Table 1.
The diagnostic performance (accuracy or AUC) of mp-MRI in the detection of PCa with supervised machine learning algorithms was mentioned in several studies.
| Author | Studied Algorithm | The Value of Accuracy |
The Value of AUC |
|---|---|---|---|
| Iyama et al. [41] | LR | - | 0.97 |
| Kan et al. [42] | LR | 0.862 | 0.735 |
| Alam et al. [43] | LR | 0.969 | - |
| Tang et al. [44] | LR | 0.754 | 0.82 |
| Niaf et al. [40] | SVM | - | 0.89 |
| Tang et al. [44] | SVM | 0.749 | 0.82 |
| Gravina et al. [46] | SVM | 0.725 | 0.727 |
| Anderson et al. [48] | KNN | 0.77 | 0.82 |
| Alam et al. [43] | KNN | 0.787 | - |
| Niaf et al. [40] | KNN | - | 0.88 |
| Kan et al. [42] | RF | 0.860 | 0.832 |
| Alam et al. [43] | DT | 0.779 | - |
| Alam et al. [43] | RF | 0.928 | - |
| Gravina et al. [46] | RF | 0.779 | 0.833 |
| Alfano et al. [53] | NB | - | 0.80 |
| Niaf et al. [40] | NB | - | 0.88 |
| Kiraly et al. [55] | DCNN | - | 0.83 |
| Wang et al. [56] | CNN | 0.85 | - |
Abbreviations; LR: logistic regression, SVM: support vector machines, RF: random forests, cDT: decision tree, KNN: K nearest neighbors, NB: naive Bayes, DNN: deep neural network, DL: Deep learning, CNN: convolutional neural network.