Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

Table 2 Radiomics-based multiomics combinations study for cancer prognosis

Tumor type	Sample size	Evaluation indicators	Multiomics approaches	Model	Training set	Test set	References
HGSOC	444	OS	Histopathological CT radiologics Genomics Clinical data	Cox model	N/A	0.61	[141]
NSCLC	247	Immunotherapy response	Pathological CT radiomics Genomics Clinical data	DyAM model	0.8	N/A	[142]
Glioma	176	OS	Pathological MRI radiomics Genomics Clinical data	DOF model	0.788	N/A	[145]

HGSOC high-grade serous ovarian cancer, NSCLC non-small cell lung cancer, OS overall survival, CT computed tomography, MRI magnetic resonance imaging, DyAM dynamic deep attention-based multiple-instance learning model with masking, DOF deep orthogonal fusion, N/A not applicable

ISSN: 1479-5876