Military Medical Research

Table 1 Applications of radiomics-based tumor grading

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	206	HNSCC	Tumor grading	74	Matlab, Python, IBM SPSS software	ML: KPCA, RF, VT selection SM: DeLong test, t-test, Chi-square test	[13]
CT	284	HNSCC	Tumor grading, extracapsular spread, perineural invasion, lymphovascular invasion, human papillomavirus status	25–35	Matlab, R	ML: PCA, LR, LASSO, Hierarchic clustering, tenfold CV SM: Fisher exact test	[14]
CT	878	Lung cancer, HNC	Tumor grading	Unspecified	Matlab, R	ML: LR, consensus clustering, hierarchical clustering SM: Jaccard index, Pearson correlation analysis	[15]
CT	211	Laryngeal cancer	Preoperative T category (T₃ vs. T₄)	8	ITK-SNAP, PyRadiomics, R, Python	ML: LASSO, SVM, Grid search, CV SM: t-test (or Mann–Whitney U test), Chi-square (or Fisher’s exact) test, ICC	[16]

CT computed tomography, ML machine learning, SM statistical method, HNSCC head and neck squamous cell carcinoma, HNC head and neck cancer, KPCA kernel principal component analysis, RF random forest, VT variance-threshold, PCA principal component analysis, LR logistic regression, LASSO least absolute shrinkage and selection operator, CV cross validation, SVM support vector machine, ICC intraclass correlation coefficients

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ISSN: 2054-9369

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