Table 5.

Radiomics in studies investigating treatment response in metastatic RCC undergoing TKIs or T-cell immunotherapy.

Author	Treatment, ROI and end-point	Imaging modality	Study design No. of patients	Imaging method	Results
Antunes et al. 105	- Sunitinib - Primary tumor - Modifications of radiomics features after TKI therapy	FLT-PET/MRI	Prospective n = 2	Image-related features before and after treatment using [18 F] FLT-PET, T2w, and DWI protocols, with DWI reporting an ADC map	The best radiomics yielded a modification of 63% within the RCC region and 17% in a distinct normal region
Bharwani et al. 106	- Sunitinib - Primary tumor - Changes in histogram parameters and correlation with OS (changes prior and after treatment)	MRI	Retrospective analysis of prospective study n = 20	ADC maps and histograms have been assessed. Mean ADC and proportion of the tumor with ADC values < 25th percentile of ADC histogram were recorded. ROI were manually delineated. Changes prior and after therapy in surviving patients have been compared for OS	Outcomes did not correlate to features. High baseline AUC low and greater median AUC low have been associated with poor OS (p = 0.038). OS had no correlation with MRI features
Boos et al. 107	- TKI (Sunitinib n = 18, Sorafenib n = 1) - Measurable soft tissue lesion - Change in CT intensity distribution curves	CT	Retrospective n = 19	Histograms delineated from ROI. Shift was used to classify response of lesions to therapy and any modifications on scans, using the Choi, MASS, and RECIST criteria	Changes in histograms appeared in 58% of lesions, and a significant difference between mean and median lesion attenuation (p < 0.001). There has been an increased in changes of the accurate classification of tumors when Choi and Mass criteria were evaluated (63–68% and 74–79%)
Goh et al. 108	- TKI (26 patients with sunitinib, 6 patients with cedirinib and 4 patients with pazopanib, and 3 patients with regorafenib) - metastases. - Change in histogram parameters (entropy and uniformity) and correlation of texture parameters with PFS	CT	Retrospective n = 39	A CAD software algorithm appreciated the changes in entropy and uniformity of metastasis. RECIST, Choi, and modified Choi criteria evaluated the response. The correlations of texture parameters and standard criteria with PFS have been assessed	Tumor entropy decreased and uniformity increased following TKI therapy. Kaplan–Meier curves of patients without disease progression reported better outcomes compared with standard response assessment (p = 0.008 versus 0.267, p = 0.053, and p = 0.042 for RECIST, Choi, and modified Choi criteria, respectively). Texture uniformity was an independent predictor of time to progression (p = 0.005)
Haider et al. 109	- Sunitinib - Measurable lesion - Correlation of texture parameters with OS and PFS in ccRCC	CT	Retrospective n = 40	Measurable lesions on CECT before and 2 months after therapy. TexRAD software (TexRAD Ltd, Cambridge, UK) has been employed to analyze textures. Cox regression model assessed changes in texture features and PFS/OS	Size-normalized SD (nSD) alone is good predictor of OS (p = 0.01). Entropy modifications are a good predictor for OS (p = 0.02 and p = 0.04) and nSD can prognoses PFS (p = 0.01 and p = 0.003)
Mains et al. 110	- Various treatments, not specified - Large artery - Association between OS and PFS with functional CT parameters	CT	Retrospective analysis of prospective study n = 69	Scans performed at prior and after therapy (after 5 and 10 weeks). BVdeconv, BFdeconv, SPVdeconv, blood flow and standardized perfusion values (BFmax and SPVmax), were evaluated using the Patlak model (BVpatlak and PS)	The strongest association was found for BVdeconv, BVpatlak, and BFdeconv prior and after therapy (p < 0.05). PS seemed to have opposite associations dependent on treatment. Inter-observer correlations were excellent (r ⩾ 0.9, p < 0.001) with good agreement for BFdeconv, BFmax, SPVdeconv and SPVmax
Khene et al. 111	- Nivolumab - Predict response to treatment	CT	Retrospective n = 48	K-nearest neighbor, RF, logistic regression, and SVM algorithms have been used. Classification of patients: complete or partial response or stable disease and non-responders	95% of patients received nivolumab. 60.4% of patients were nivolumab responders. The ACC (0.71 till 0.91) and the AUC (0.67 till 0.92). RF reported the worse accuracy, while logistic regression the highest

ACC, accuracy; ADC, apparent diffusion coefficient; AUC, area under the curve; CAD, computer-aided diagnosis; CECT, contrast-enhanced computed tomography; CT, computed tomography; DWI, diffusion-weighted imaging; FLT-PET/MRI = F18 fluorothymidine-positron emission tomography/MRI; HR, hazard ratio; HU, Hounsfield unit; MASS, morphology, attenuation, size, and structure; MRI, magnetic resonance imaging; nSD, size-normalized SD; OR, odds ratio; OS, overall survival; PFS, progression-free survival; RCC, renal cell carcinoma; RECIST, response evaluation criteria in solid tumors; RF, random forest; ROI, region of interest; SD, standard deviation; SVM, support vector machine; T2w, T2-weighted; TKI, tyrosine kinase inhibitor; VOI, volume of interest; WL, whole lesion.