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Table 3 Applications of radiomics-based classification of malignant versus benign tumors

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

MRI

130

HNSCC

Classify benign and malignant tumors, differentiate ENE

89/6

3D Slicer, Segmentation Wizard, Python

ML: Adam optimization algorithm

SM: t-test

DL: Multilayer perceptron neural network

[21]

CT

285

HCC and hepatic hemangioma

Classify benign and malignant tumors

13

Matlab

ML: LR, LASSO, SVM, Multiple-regression

[22]

MRI

69

Parotid lesions

Classify benign and malignant tumors

4

Matlab, S-IBEX

ML: SVM, NCA, CV

SM: Chi-square test, Mann–Whitney test, Spearman correlation coefficient, Z-score

[23]

  1. MRI magnetic resonance imaging, CT computed tomography, ML machine learning, SM statistical method, DL deep learning, HNSCC head and neck squamous cell carcinoma, HCC hepatocellular carcinoma, ENE extra-nodal extension, LR logistic regression, LASSO least absolute shrinkage and selection operator, SVM support vector machine, NCA neighborhood component analysis, CV cross validation
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Military Medical Research

ISSN: 2054-9369