Table 7.
Application of AI in MRI to diagnosis cervical cancer.
| Reference | Year | Aim of study | Number of cases | Methods | Results |
|---|---|---|---|---|---|
| Lin et al. (82) | 2020 | Cervical Cancer MRI Image segmentation and location | 169 patients (training set 144; validation set 25) | DL Radiomics | A dice coefficient: 0.82; Sensitivity: 0.89, PPV:0.92 |
| Wang et al. (83) | 2020 | Segmentation: Prediction of parametrial invasion | 137 patients (training set 91; validation set 46) | Radiomics | Training set AUC T2WI: 0.797 T2WI and DWI0.780 (95% CI)Validation set T2WI 0.946 (95% CI) T2WI and DWI 0.921 (95% CI) |
| Peng et al. (84) | 2019 | Enhancing Cervical Cancer MRI Image Segmentation | Not mention | Wireless network; DL | AUC 0.980 |
| Yu et al. (85) | 2019 | Assisting diagnosis of lymph node metastasis | 153 patients (training set 102; validation set 51) | Radiomics | Training set AUC: 0.870Validation set AUC 0.864 |
| Wu et al. (86) | 2019 | Assisting diagnosis of lymph node metastasis | 189 patients (training set 126; validation set 63) | Radiomics | Training set AUC 0.895 Sensitivity 94.3%Validation set AUC 0.847 Sensitivity 100% |
| Wang et al. (87) | 2019 | Assisting diagnosis of lymph node metastasis | 96 patients (training set 96; validation set 96) | RadiomicsSVM | Training set C-index 0.893(P=4.311*10-5)Validation set C-index 0.922(P=3.412*10-2) |
| Xiao et al. (88) | 2020 | Assisting diagnosis of lymph node metastasis | 233 patients (training set 155; validation set 78) | Radiomics | Training set C-index 0.856 (95% CI)Validation set C-index 0.883 (95% CI) |
| Wu et al. (89) | 2020 | Assisting diagnosis of lymph node metastasis | 479 patients (training set 338; validation set 141) | DL | AUC 0.933 (95% CI) |