Table 2.
Description of pan-cancer radiogenomic studies focused on clinical applications.
Cancer type | Study objective/genomic feature | Radiomic feature | Imaging modality | Machine-learning algorithm | Patients | Study type | Author |
---|---|---|---|---|---|---|---|
Breast cancer | |||||||
Oncotype Dx RS | Handcraft | MRI | None | 43 | Retrospective study | Thakur et al. [120] | |
Oncotype Dx RS | Deep | Mammographic and MR | Linear regression models | 556 | Retrospective study | Woodard et al. [98] | |
Oncotype Dx RS | Handcraft | MRI | Convolutional neural network (CNN) algorithm | 134 | Retrospective study | Ha et al. [121] | |
Oncotype Dx RS | Deep learning | MRI | None | 382 | Retrospective study | Li et al. [122] | |
Predicting molecular subtypes | Deep | MRI | Convolutional neural network (CNN) algorithm | 213 | Retrospective study | Ha et al. [123] | |
Molecular subtypes | Deep | MRI | None | 132 | Prospective study | Iima et al. [124] | |
Molecular subtypes | Handcraft | MRI | Linear regression | 306 | Prospective study | Tsai et al. [125] | |
Molecular subtypes | Deep | MRI | Convolutional neural network (CNN) algorithm | 244 | Retrospective study | Zhang et al. [126] | |
Clear cell renal cell carcinoma | |||||||
Molecular subtypes | Handcraft | PET and MRI | multivariate logistic regression analysis | 77 | Retrospective study | Wang et al. [127] | |
Molecular subtypes | Handcraft | Enhanced CT | Multivariate logistic regression analysis | 131 | Retrospective study | Gao et al. [128] | |
Medulloblastoma | |||||||
Molecular subtypes | Handcraft | MRI | Multivariate logistic regression analysis | 111 | A combined retrospective and prospective study | Dasgupta et al. [129] | |
Gliomas | |||||||
Molecular subtypes | Handcraft | MRI | Cross-validation model | 110 | Retrospective study | Buda et al. [130] | |
Molecular subtypes | Handcraft | 18F-Fluorocholine PET/CT | ML was not used to build the predictive model | 35 | Prospective study | Garcia et al. [131] | |
Molecular subtypes | Handcraft | MRI | Multivariate logistic regression analysis | 272 | Retrospective study | Nam et al. [132] | |
Molecular subtypes | Handcraft | CT/MRI | Logistic regression analysis | 189 | Retrospective study | Zhao et al. [133] | |
molecular subtypes | Deep | MRI | Convolutional neural network (CNN) algorithm | 1016 | Retrospective study | Li et al. [134] | |
Hepatocellular carcinoma | |||||||
Identifying microvascular invasion (MVI) | CT | Multivariate logistic regression analysis | 495 | Retrospective study | Dasgupta et al. [135] | ||
Identifying microvascular invasion (MVI) | Handcraft | CT | LASSO logistic regression and logistic regression analysis | 145 | Retrospective study | He et al. [136] | |
Identifying microvascular invasion (MVI) | Handcraft | CT | Logistic regression analysis | 185 | Retrospective study | Liu et al. 2021 [137] |