Military Medical Research

Table 5 Applications of radiomics-based recurrence prediction

From: Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Image modality	Number of patients	Cancer	Target	Number of radiomics features	Commercial or open-source software	Method	References
CT	188	HNSCC	Cancer recurrence rate	107	PyRadiomics, 3D Slicer, Matlab	ML: LOOCV SM: Chi-square test DL: Deep learning artificial neural networks	[28]
FDG-PET	174	OPC	The risk of local failure	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]
CT	465	OPC	Local recurrence	2	Matlab	ML: Bootstrap resampled recursive partitioning analysis, Regression model, DT, Cox proportional hazards model SM: Log-rank and Wilcoxon test, Effect likelihood ratio test, Wald test	[36]
MRI	285	HNSCC	Local tumor recurrence	20	MITK, SPM, Matlab, R	ML: LASSO, tenfold CV SM: t-test, Chi-square test or Fisher’s exact test, Delong test, Spearman correlation analysis	[37]
US	83	Breast cancer	Recurrence	4	Matlab, SPSS	ML: KNN, SVM SM: Shapiro–Wilk test, t-test, Mann–Whitney test, Kaplan–Meier product-limit method	[38]

CT computed tomography, MRI magnetic resonance imaging, FDG fluorodeoxyglucose, PET positron emission tomography, US ultrasonography, ML machine learning, SM statistical method, DL deep learning, HNSCC head and neck squamous cell carcinoma, OPC oropharyngeal cancer, LOOCV leave one out cross validation, LR logistic regression, CV cross validation, DT decision tree, LASSO least absolute shrinkage and selection operator, KNN K-nearest neighbors, SVM support vector machine

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ISSN: 2054-9369

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