Table 1.

Published radiogenomics studies in genitourinary tumor

References	Tumor	Application	Results
Shinagare et al. [24]	ccRCC	Association between imaging features and gene mutation	BAP1 mutation was associated with ill-defined margins and calcification. MUC4 mutation correlated with exophytic growth pattern of the tumor
Karlo et al. [26]	ccRCC	Association between imaging features and gene mutation	KDM5C and BAP1 mutations were associated with renal vein invasion. Mutations of VHL were associated with well-defined tumor margins, nodular tumor enhancement and intratumoral vascularity gross appearance
Ghosh et al. [16]	ccRCC	Predicting BAP1 mutation status	CT-based model achieved AUC values of 0.71, 0.66, 0.62, 0.52 for the nephrographic, unenhanced, cortico-medullary and excretory CT images, respectively
Chen et al. [27]	ccRCC	Predicting mutation status of VHL, PBRM1 and BAP1.	The multi-classifier model achieved a AUC value of more than 0.85 in predicting these gene mutations
Kocal et al. [21]	ccRCC	Predicting BAP1 mutation status	Texture analysis based on unenhanced CT achieved a high specificity, sensitivity and precision in predicting BAP1 mutation status
Kocal et al. [17]	ccRCC	Predicting PBRM1 mutation status	The RF algorithm outperformed the ANN algorithm with an accuracy of 95.0% and an AUC of 0.987
Marigliano et al. [13]	ccRCC	Association between miRNAs and texture features	miR-21-5p and entropy showed good correlation in ccRCC
Lee et al. [36]	RCC	Predicting postoperative metastasis of RCC	Four radiomics features extracted from the nephrographic phase of postcontrast CT could predict postoperative metastasis of pT1 RCC patients and these features were correlated with heterogenous-trait-associated gene signatures
Jamshidi et al. [38]	ccRCC	Predicting prognosis	Radiogenomic risk score (RRS) could stratify radiological rPFS of patients with metastatic RCC treating with bevacizumab before surgery
Lin et al. [41]	BCa	Predicting prognosis	The nomogram incorporating contrast-enhanced CT radiomics, RNA sequencing data and clinical data showed an excellent ability for predicting progression-free interval in BCa patients
McCann et al. [45]	PCa	Association between imaging features and gene expression	There existed a weak negative correlation between the quantitative mp-MRI imaging feature Kep and PTEN expression in PCa
Sun et al. [49]	PCa	Association between imaging features and gene expression	16 T2W texture features were associated with hypoxia gene expressions in PCa
Stoyanova et al. [50]	PCa	Association between imaging features and gene expression	There were significant correlations between quantitative imaging features and genes in PCa
Kesch et al. [51]	PCa	Predicting tumor aggressiveness	A strong correlation between imaging features and genomic index lesions was detected
Jamshidi et al. [52]	PCa	Prostate microenvironment evaluation	Whole-exome radiogenomics analysis and mp-MRI imaging shows a continuum of mutations across regions that were found to be high grade and normal grade by histological assessment.
Wibmer et al. [53]	PCa	Association between imaging features and gene expression	Patients with extracapsular extension (ECE) on MRI imaging had a higher mean cell cycle risk scores
Fischer et al. [2]	PCa	Prediction of pathological stage	The radiogenomics model has high potential to reveal the molecular mechanisms underlying tumor aggressiveness and predict tumor pathological stage