Can renal oncocytoma be distinguished from chromophobe renal cell carcinoma by the presence of fibrous capsule?

Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma

OBJECTIVES

To investigate different radiomics models based on single phase and the different phase combinations of radiomics features from 3D tri-phasic CT to distinguish RO from chRCC.

METHODS

A total of 96 patients (30 RO and 66 chRCC) were enrolled in this study. Radiomics features were extracted from unenhanced phase (UP), corticomedullary phase (CMP), and nephrographic phase (NP) CT images. Feature selection was based on the least absolute shrinkage and selection operator regression (LASSO) method. The selected features were used to develop different radiomics models using logistic regression (LR) analysis, including model 1 (UP), model 2(CMP), model 3(NP), model 4(UP+CMP), model 5(UP+NP), model 6(CMP+NP), and model 7(UP+CMP+NP). The radiomics model demonstrating the highest discrimination performance was utilized to construct the combined model (model 8) with clinical factors. A nomogram based on the model 8 was established. To evaluate the diagnostic performance of the different models, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used. Delong's test was utilized to assess the statistical significance of the AUC improvement across the models.

RESULTS

Among the seven radiomics models, model 7 exhibited the highest AUC of 0.84 (95% CI 0.69, 0.99), and model 7 demonstrated a significantly superior AUC compared to the other radiomics models (all P < 0.05). The AUC values of radiomics models based on two phases (model4, mode5, mode6) were greater than the models based on single phase (model1, mode2, mode3) (all P < 0.05). Model 3 illustrated the best performance of the three radiomics models based on single phase with an AUC of 0.76 (95% CI 0.57, 099). Model 6 illustrated the best performance of the three radiomics models based on two-phases combination with an AUC of 0.83 (0.66, 0.99). Model 8 achieved an AUC of 0.93 (95% CI 0.83, 1.00) which is higher than those all radiomics models.

CONCLUSION

Radiomics models based on combination of radiomics features from UP, CMP, and NP can be a useful and promising technique to differentiate RO from chRCC. Moreover, the model combining clinical factors and radiomics features showed better classification performance to distinguish them.

Differential diagnosis of renal oncocytoma and chromophobe renal cell carcinoma using CT features: a central scar-matched retrospective study

BACKGROUND

Renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) have a common cellular origin and different clinical management and prognosis.

PURPOSE

To explore the utility of computed tomography (CT) in the differentiation of RO and chRCC.

MATERIAL AND METHODS

Twenty-five patients with RO and 73 patients with chRCC presenting with the central scar were included retrospectively. Two experienced radiologists independently reviewed the CT imaging features, including location, tumor size, relative density ratio, segmental enhancement inversion (SEI), necrosis, and perirenal fascia thickening, among others. Interclass correlation coefficient (ICC, for continuous variables) or Kappa coefficient test (for categorical variables) was used to determine intra-observer and inter-observer bias between the two radiologists.

RESULTS

The inter- and intra-reader reproducibility of the other CT imaging parameters were nearly perfect (>0.81) except for the measurements of fat (0.662). RO differed from chRCC in the cortical or medullary side (P = 0.005), relative density ratio (P = 0.020), SEI (P < 0.001), and necrosis (P = 0.045). The logistic regression model showed that location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were highly predictive of RO. The combined indicators from logistic regression model were used for ROC analysis. The area under the ROC curve was 0.923 (P < 0.001). The sensitivity and specificity of the four factors combined for diagnosing RO were 88% and 86.3%, respectively. The correlation coefficient between necrosis and tumor size in all tumors including both of RO and chRCC was 0.584, indicating a positive correlation (P < 0.001).

CONCLUSION

The CT imaging features of location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were valuable indicators in distinguishing RO from chRCC.

A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study

OBJECTIVE

Pre-operative differentiation between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is critical due to their different clinical behavior and different clinical treatment decisions. The aim of this study was to develop and validate a CT-based radiomics nomogram for the pre-operative differentiation of RO from chRCC.

METHODS

A total of 141 patients (84 in training data set and 57 in external validation data set) with ROs (n = 47) or chRCCs (n = 94) were included. Radiomics features were extracted from tri-phasic enhanced-CT images. A clinical model was developed based on significant patient characteristics and CT imaging features. A radiomics signature model was developed and a radiomics score (Rad-score) was calculated. A radiomics nomogram model incorporating the Rad-score and independent clinical factors was developed by multivariate logistic regression analysis. The diagnostic performance was evaluated and validated in three models using ROC curves.

RESULTS

Twelve features from CT images were selected to develop the radiomics signature. The radiomics nomogram combining a clinical factor (segmental enhancement inversion) and radiomics signature showed an AUC value of 0.988 in the validation set. Decision curve analysis revealed that the diagnostic performance of the radiomics nomogram was better than the clinical model and the radiomics signature.

CONCLUSIONS

The radiomics nomogram combining clinical factors and radiomics signature performed well for distinguishing RO from chRCC.

ADVANCES IN KNOWLEDGE

Differential diagnosis between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is rather difficult by conventional imaging modalities when a central scar was present.A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of RO from chRCC with improved diagnostic efficacy.The CT-based radiomics nomogram might spare unnecessary surgery for RO.

A CT-based radiomics nomogram for differentiation of small masses (< 4 cm) of renal oncocytoma from clear cell renal cell carcinoma

PURPOSE

Renal oncocytoma (RO) is the most commonly resected benign renal tumor because of misdiagnosis as renal cell carcinoma. This misdiagnosis is generally owing to overlapping imaging features. This study describes the building of a radiomics nomogram based on clinical data and radiomics signature for the preoperative differentiation of RO from clear cell renal cell carcinoma (ccRCC) on tri-phasic contrast-enhanced CT.

METHODS

A total of 122 patients (85 in training set and 37 in external validation set) with ROs (n = 46) or ccRCCs (n = 76) were enrolled. Patient characteristics and tri-phasic contrast-enhanced CT imaging features were evaluated to build a clinical factors model. A radiomics signature was constructed by extracting radiomics features from tri-phasic contrast-enhanced CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to a multivariate logistic regression analysis. The diagnostic performance of the above three models was evaluated in training and validation sets.

RESULTS

Central stellate area and perirenal fascia thickening were selected to build the clinical factors model. Eleven radiomics features were combined to construct the radiomics signature. The AUCs of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.960 and 0.898 in the training and validation sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the validation set indicated an overall net benefit over the clinical factors model.

CONCLUSION

Our radiomics nomogram can effectively predict the preoperative diagnosis of ROs and may therefore be of assistance in sparing unnecessary surgery and tailoring precise therapy. The ROC curves of the clinical model, the radiomics signature and the radiomics nomogram for the validation set. RO = Renal oncocytoma; ccRCC = Clear cell renal cell carcinoma.

Urinary Metabolomics Validates Metabolic Differentiation Between Renal Cell Carcinoma Stages and Reveals a Unique Metabolic Profile for Oncocytomas

Renal cell carcinoma (RCC) is a heterogeneous malignancy which often develops and progresses asymptomatically. Benign oncocytomas are morphologically similar to malignant chromophobe RCC and distinguishing between these two forms on cross-sectional imaging remains a challenge. Therefore, RCC-specific biomarkers are urgently required for accurate and non-invasive, pre-surgical diagnosis of benign lesions. We have previously shown that dysregulation in glycolytic and tricarboxylic acid cycle intermediates can distinguish benign lesions from RCC in a stage-specific manner. In this study, preoperative fasting urine samples from patients with renal masses were assessed by ¹H nuclear magnetic resonance (NMR). Significant alterations in levels of tricarboxylic acid cycle intermediates, carnitines and its derivatives were detected in RCC relative to benign masses and in oncocytomas vs. chromophobe RCC. Orthogonal Partial Least Square Discriminant Analysis plots confirmed stage discrimination between benign vs. pT1 (R2 = 0.42, Q2 = 0.27) and benign vs. pT3 (R2 = 0.48, Q2 = 0.32) and showed separation for oncocytomas vs. chromophobe RCC (R2 = 0.81, Q2 = 0.57) and oncocytomas vs. clear cell RCC (R2 = 0.32, Q2 = 0.20). This study validates our previously described metabolic profile distinguishing benign tumors from RCC and presents a novel metabolic signature for oncocytomas which may be exploited for diagnosis before cross-sectional imaging.

TGF-β1 expression in chromophobe renal cell carcinoma and renal oncocytoma

Distinguishing renal oncocytoma (RO) from the eosinophilic variant of chromophobe renal cell carcinoma (ChRCC) under the light microscope is a common diagnostic problem. Our recent research has shown significant difference between the presence of tumor fibrous capsule in ChRCCs and ROs. Transforming growth factor beta 1 (TGF-β1) is a potent cytokine involved in regulating a number of cellular processes. Two main purposes of this research were to investigate whether the TGF-β1 staining could be related to the presence of tumor fibrous capsule and if it could be used in the differential diagnosis between ChRCC and RO. We investigated 34 cases: 16 ChRCCs (8 eosinophilic and 8 classic) and 18 ROs. All available slides of each tumor, routinely stained with hematoxylin and eosin (H&E) were first analyzed to note the presence of tumor fibrous capsule. One paraffin embedded tissue block matching the representative H&E slide was selected for the immunohistochemical analysis. TGF-β1 expression was analyzed semiquantitatively in the tumor tissue, the tumor fibrous capsule, if present and the peritumoral renal parenchyma. Intensity of TGF-β1 expression was weaker in ChRCCs than the one observed in ROs (P<0.05). The type of reaction in ChRCCs was predominantly membranous unlike in ROs, which exhibited a predominantly cytoplasmic reaction (P<0.05). Moreover, none of the ROs showed membranous type of reaction for TGF-β1. In the group of ChRCCs, tumors with capsule had statistically significant higher quantity of TGF-β1 expression in tumor tissue and in peritumoral renal parenchyma compared to the tumors without capsule (P<0.05). Our results showed different types of TGF-β1 expression in ChRCCs and ROs: ChRCCs had predominantly membranous type of reaction, and ROs predominantly cytoplasmic. Furthermore, ChRCCs with capsule had statistically significant higher quantity of TGF-β1 expression in tumor tissue and in peritumoral renal parenchyma compared to the tumors without capsule. Based on these findings we can speculate that it could be possible that TGF-β1 plays a role in the formation of fibrous capsule in ChRCCs.

Selected common diagnostic problems in urologic pathology: perspectives from a large consult service in genitourinary pathology

CONTEXT

Several common differential diagnoses are encountered in urologic pathology, frequently causing patient referrals for a second opinion.

OBJECTIVES

To review 3 common differential diagnoses encountered in a large consultation service in genitourinary pathology, including partial atrophy versus prostatic acinar adenocarcinoma, oncocytoma versus chromophobe renal cell carcinoma, and urothelial carcinoma in situ versus normal urothelium and reactive atypia. We will discuss the detailed, morphologically distinctive features and the usefulness of immunohistochemistry.

DATA SOURCES

Personal experience and review of the current literature.

CONCLUSIONS

Careful morphologic assessment and awareness of diagnostic pitfalls are fundamental in reaching a definitive diagnosis in most cases. Immunohistochemistry is useful but should be used only in conjunction with the morphologic impression.