Table 3.
Radiomics in studies differentiating oncocytoma from RCC.
| Author | Clinical outcomes and gold standard | Imaging modality | Study design No. of patients |
Imaging method | Results |
|---|---|---|---|---|---|
| Baghdadi et al. 62 | - Differentiation of ccRCC from
oncocytoma - Histology (surgery) |
CT | Retrospective n = 192 |
Semi-automated method comprising two phases: (I) using DL network and manual tumor delineation; and (II) automated extraction of imaging features |
SD Dice similarity score (0.66) for CNN model. PEER had ACC of 95% in tumor type classification (100% SENS and 89% SPEC) compared with the final pathology results 63 |
| Chen et al. 64 | - Differentiation of ccRCC from renal
oncocytoma - Histology (surgery) |
CT | Retrospective n = 94 |
CT whole lesions and region of interest evaluated | WL enhancement had poor results to differentiate between ccRCC and oncocytoma (AUC of 0.78 and 0.72, respectively), Combination model (AUC of 0.86) |
| Coy et al. 5 | - Differentiation of ccRCC from renal
oncocytoma - Histology (not specified if surgery or biopsy) |
CT | Retrospective n = 179 |
CT imaging features with neural network model | The best performance was obtained in the excretory phase (ACC = 74.4%, SENS = 85.8%, and positive predictive value = 80.1%) |
| Deng et al. 65 | - Discriminating RCC from noncancerous renal
lesions - Histology (not specified if surgery or biopsy) |
CT | Retrospective n = 501 |
An ROI was drawn on venous phase axial CT. Different texture analysis parameters were compared between cohorts | Differences in entropy were helpful in differentiation chrRCC from oncocytoma |
| Li et al. 66 | - Differentiation of chrRCC and
oncocytoma - Histology (surgery) |
CT | Retrospective n = 61 |
LASSO regression algorithm was used to analyze the CT image features. ROC curve and accuracy evaluation criteria | 1029 features extracted. Diagnostic performance (AUC > 0.85); SVM classifier had the best performance (AUC 0.96, SENS 0.99, SPEC 0.80, ACC 0.94) |
| Raman et al. 67 | - Differentiation of ccRCC, papRCC, oncocytomas and renal
cysts - Histology (surgery) |
CT | Retrospective n = 99 |
ROIs were drawn manually. A predictive model using quantitative parameters was constructed and externally validated | The RF model revealed 87% of ONC (SENS 89% and SPEC 99%). No AUC reported |
| Sasaguri et al. 68 | - Differentiation of oncocytoma versus RCC
(papRCC and ccRCC and other subtypes) - Histology (surgery) |
CT | Retrospective n = 166 |
CT tumor attenuation values and texture parameters used in-house (Matlab (MathWorks) software. logistic regression model used for differentiating types of renal lesions | AUC 0.91 for differentiating ccRCC and other subtype RCCs from papillary RCCs |
| Varghese et al. 35 | - Differentiation of malignant and benign renal masses
(various subtypes) - Histology (surgery) |
CT | Retrospective n = 174 |
WL were manually segmented and co-registered from CECT scans | Texture model had AUC of 0.87 (p < 0.05) for discriminating benign from cancerous kidney lesions |
| Varghese et al. 69 | - Differentiation of malignant and benign renal masses
(various subtypes) - Histology (surgery) |
CT | Retrospective n = 156 |
Manually segmentation of WL CT images (1) benign versus cancerous kidney lesions, (2) ONC versus ccRCC, and (3) ONC versus AML |
ROC analysis (AUC curve > 0.7, p < 0.05) between three groups |
| Yu et al. 29 | - Differentiation of ccRCC, papRCC, chrRCC and
oncocytoma Histology (surgery) |
CT | Retrospective n = 119 |
Manual segmentation of tumors. SVM method used for classification | ML applied to texture analysis to differentiate oncocytoma from other tumors (AUC of 0.86) |
| Hoang et al. 70 | - Differentiation of - benign and cancerous kidney lesions (oncocytoma versus ccRCC and papRCC) and RCC subtypes (ccRCC versus papRCC) - Histology (surgery) |
MRI | Retrospective n = 41 |
Texture features from WL MRI slides | ONCs were distinguished from ccRCCs (SENS 67.3%, SPEC 88.9%, and ACC 79.3%), and from papRCC and ccRCCs (SENS 64.7%, SPEC 85.9%, and ACC 77.9%). No AUC reported |
| 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. ROC curve analysis with optimal cutoff points was used to test the ability to the different groups | WL ADC values were very different between papRCC and oncocytoma. Best AUC = 95.8 for oncocytoma versus papRCC; SENS/SPEC 88.5% and 93.1% for oncocytoma versus papRCC, respectively |
2D, two-dimensional; 3D, three-dimensional; ACC, accuracy; ADC, apparent diffusion coefficient; AML, angiomyolipoma; AUC, area under the curve; ccRCC, clear cell RCC; chRCC, chromophobe RCC; CECT, contrast-enhanced computed tomography; CNN, convolutional neural network; CT, computed tomography; DL, deep learning; DWI, diffusion-weighted imaging; IQR, inter-quartile range; LASSO, least absolute shrinkage and selection operator; ML, machine learning; MRI, magnetic resonance imaging; 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.