Table 6 Applications of radiomics-based metastasis prediction
Image modality | Number of patients | Cancer | Target | Number of radiomics features | Commercial or open-source software | Method | References |
|---|---|---|---|---|---|---|---|
FDG-PET | 174 | OPC | The risk of DM | 2–3 | Matlab, Stata/MP | ML: LOOCV, Cox proportional-hazards regression, Fine and Gray’s proportional sub-hazards model, LR, fivefold CV SM: Kaplan–Meier analysis, log-rank test, Spearman correlation analysis | [29] |
MRI | 176 | NPC | DM | 7 | PyRadiomics, Python, ITK-SNAP, R, SPSS | ML: mRMR, LASSO, LR, Mutual information, Bootstrap-resampling SM: ICC, t-test, Kaplan–Meier analysis, log-rank test, Fisher's exact test, Chi-square test, or Mann–Whitney U test | [39] |
MRI | 236 | Tongue cancer | LNM | 15/17/18/25/10 | ITK-SNAP, AIMT, Python, R, SPSS | ML: PCA, SVM, Cox regression analysis, fivefold CV SM: DeLong test, Spearman correlation analysis, Kaplan–Meier analysis, log-rank test | [34] |
MRI | 346 | Rectal cancer | LNM | 4/5/10 | GE Healthcare, 3D Slicer, R, SPSS | ML: LASSO, LR, Cox analysis SM: ICC, Wilcoxon test, Hosmer–Lemeshow test, t-test, Nonparametric test, Chi-square test, and Fisher’s exact test, DeLong test | [35] |
US | 126 | Thyroid cancer | LNM | 91 | ITK-SNAP, Ultrosomics, SPSS | ML: LASSO, PCA, DT, Naive Bayes, KNN, LR, SVM, Bagging, RF, Extremely RF, AdaBoost, Gradient boosting DT SM: t-test, Chi-square test or Fisher’s exact test | [41] |
US | 205 | NPC | LNM | 7 | GE Healthcare, R, Python | ML: mRMR, LR, LASSO SM: ICC, DeLong test | [42] |
PET | 76 | Primary prostate cancer | LNM, DM | 22 | RaCaT, Python | ML: RF, CV, PCA SM: Chi-square test, DeLong test, ICC, Z-score | [43] |