Table 6.

Prediction of clinical outcome by radiomics in cancer patients undergoing immune-checkpoint blockade.

Reference	Author	Tumor Entity	Findings
[187]	Sun et al.	solid tumors	OS prediction based on radiomics CD8+ cell score
[195]	Bensch et al.	bladder cancer, NSCLC, TNBC	↑ ORR/PFS/OS prediction by PET evaluation with zirconium-89-labeled atezolizumab compared to IHC or RNA-sequencing based PD-L1 assessment
[196]	Khorrami et al.	NSCLC	ORR and OS prediction based on changes in radiomic texture (“DelRADx”)
[197]	Trebeschi et al.	melanoma, NSCLC	Response prediction of individual metastases and OS prediction based on multiple radiomic features
[198]	Himoto et al.	ovarian cancer	Prediction of clinical benefit by intratumoral heterogeneity (radiomic feature) and by number of disease sites
[199]	Ligero et al.	solid tumors	↑ ORR prediction by clinical-radiomics signature score
[200]	Tunali et al.	NSCLC	Prediction of hyperprogressive disease based on clinical-radiomic models
[201]	Dercle et al.	non-squamous NSCLC	PFS prediction based on tumor volume reduction, infiltration of tumor boundaries or spatial heterogeneity
[202]	Korpics et al.	solid tumors	Prediction of local tumor failure, PFS and OS in cancer patients receiving SBRT and anti-PD-1 Tx based on a radiomics score

PET: positron emission tomography; PFS: progression-free survival; SBRT: stereotactic body radiotherapy, Tx: therapy; NSCLC: non-small cell lung cancer; TNBC: triple negative breast cancer; OS: overall survival; ORR: overall response rate; IHC: immunohistochemistry; PD-L1: programmed cell death-ligand 1.