Table 4.
Radiomics in studies differentiating different subtypes of RCC.
| Author | Clinical outcomes and gold standard | Imaging modality | Study design No. of patients |
Imaging method | Results |
|---|---|---|---|---|---|
| Kocak et al. 74 | - Differentiation of ccRCC, papRCC, and
chRCC - Histology (either surgery or biopsy) |
CT | Retrospective n = 68 |
Feature selection was done by three radiologists. Feature selection and model optimization has been performed. Using ANN and SVM as classifiers and a combination of three additional algorithms aimed to improve generalizability | For differentiating non-ccRCCs from ccRCCs, the best
performance was the ANN classifier (ACC of 84.6%). The
performance was poor for distinguishing ccRCC
versus papRCC versus
chRCC. Best SVM classifier with bagging algorithm (ACC of 69.2%) |
| Han et al. 75 | - Differentiation of ccRCC, papRCC, and
chRCC - Histology (biopsy) |
CT | Retrospective n = 169 |
Rectangular ROI was marked and cropped. A DL neural network has been used to identify subtypes of RCC | Deep DL method with the contouring given by radiologists for RCC subtype classification achieved an ACC 0.85, SENS 0.64–0.98, SPEC 0.83–0.93, and AUC 0.9 |
| Li et al. 76 | - Differentiation of ccRCC from non-ccRCC - Histology (not specified if surgery or biopsy) |
CT | Retrospective n = 170 (training cohort) n = 85 (validation cohort) |
Two radiomics models were built. The radiogenomics association between selected features and VHL mutation has been analyzed. All models were independently validated | The model from obtained from corticomedullary images from CT had AUC of 0.95 (ACC of 92.9%) |
| Raman et al. 67 | - Differentiation of ccRCC, papRCC, oncocytomas and renal
cysts - Histology (surgery) |
CT | Retrospective n = 99 |
ROIs were delineated in different phases of CECT images. Heterogeneity has been further assessed. A predictive model using quantitative parameters was constructed and externally validated | Various renal masses (oncocytomas, ccRCC, cysts, and papRCC) were accurately classified. The RF algorithm better categorized ccRCCs in 91% of images (SENS 91% and SPEC 97%), and papRCCs in 100% of cases (SENS 100% and SPEC 98%) |
| Leng et al. 77 | - Differentiation of ccRCC and papRCC and
AML - Histology (surgery) |
CT | Retrospective n = 139 |
A largest possible ROI was manually drawn and SD, entropy, and uniformity were analyzed. Heterogeneity indices were further assessed with a denoising algorithm | Heterogeneity indices have the ability to differentiate ccRCC from papRCC. Best AUC (0.91) for the subjective score |
| Yan et al. 78 | - Differentiation of ccRCC and papRCC and
AML - Histology (surgery or biopsy) |
CT | Retrospective n = 50 |
Native and CECT images were analyzed and classified with texture analysis software (MaZda). Tumor attenuation values and enhancement degree was determined by an ROI | For the discrimination between ccRCC and papRCC, excellent classification results were obtained with nonlinear discriminant analysis; on comparison of the three scanning phases, better lesion classification was observed with corticomedullary and nephrographic phase’s images |
| Hoang et al. 70 | - Differentiation of - benign and cancerous kidney lesions (ONC versus ccRCC and papRCC) and different RCC subtypes (ccRCC versus papRCC) - Histology (surgery) |
MRI | Retrospective n = 41 |
The features obtained from native a contrast MRI images have been analyzed. Lasso regression used for false rate results | PapRCC was distinguished from ccRCC with an ACC of 77.9% (SENS 65.5% and SPEC 88.0%) |
| Li et al. 79 | - differentiation of ccRCC, papRCC, chrRCC, AML and
oncocytoma - Histology (surgery) |
MRI | Retrospective n = 92 |
ADC maps were constructed from FOV DWI images to identify the histogram parameters | ADC histogram parameters differentiated eight of 10 pairs of renal tumors |
| Paschall et al. 71 | - Differentiation of ccRCC versus papRCC
and oncocytoma - Histology (not specified if surgery or biopsy) |
MRI | Retrospective n = 55 |
WL measurements were performed and ADC map constructed from WL histogram | WL ADC features could discriminate papRCC from ONC. Best percentile ROC analysis demonstrated AUC of 95.2 (sensitivity of 84.5% and specificity of 93.1%) |
2D, two-dimensional; 3D, three-dimensional; ACC, accuracy; ADC, apparent diffusion coefficient; AML, angiomyolipoma; ANN, artificial neural network; AUC, area under the curve; ccRCC, clear cell RCC; chRCC, chromophobe RCC; CECT, contrast-enhanced computed tomography; CT, computed tomography; DL, deep learning; DWI, diffusion-weighted imaging; FOV, field of view; IQR, inter-quartile range; LASSO, least absolute shrinkage and selection operator; ML, machine learning; MRI, magnetic resonance imaging; mRMR, minimum redundancy maximum relevance; ONC, oncocytoma; papRCC, papillary RCC; PPV, positive predictive value; RCC, renal cell carcinoma; RF, random forest; ROC, receiver operator characteristics; ROI, region of interest; SD, standard deviation; SENS, sensitivity; SPEC, specificity; SVM, support vector machine; VOI, volume of interest; WL, whole lesion.