Primary study · included by 2 reviews
The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients.
Liu 2024 · Liu Sheng, Zhang Aihua, Xiong Jianjun, et al. · Head & neck · 2024
Included by
The review does not say which of its outcomes this study informs.
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Sensitivity AI-based algorithms (radiomics machine learning or deep learning) applied to MRI vs Histopathology reference standard (cervical lymph node status) | Sensitivity 0.71 | 7 studies | GRADE not reported |
Sensitivity AI-based algorithms (radiomics machine learning or deep learning) applied to MRI vs Histopathology reference standard (cervical lymph node status) | Sensitivity 0.72 [0.62, 0.80] | 7 studies · 548 participants | Low |
Sensitivity AI-based algorithms (radiomics machine learning or deep learning) applied to MRI vs Histopathology reference standard (cervical lymph node status) | Sensitivity 0.725 | 6 studies | GRADE not reported |
2 including reviews in total.