Differentiation of renal oncocytoma and renal clear cell carcinoma using relative CT enhancement ratio

CT-based radiomics signature for differentiating pyelocaliceal upper urinary tract urothelial carcinoma from infiltrative renal cell carcinoma

Objectives

To develop a CT-based radiomics model and a combined model for preoperatively discriminating infiltrative renal cell carcinoma (RCC) and pyelocaliceal upper urinary tract urothelial carcinoma (UTUC), which invades the renal parenchyma.

Materials and methods

Eighty patients (37 pathologically proven infiltrative RCCs and 43 pathologically proven pyelocaliceal UTUCs) were retrospectively enrolled and randomly divided into a training set (n = 56) and a testing set (n = 24) at a ratio of 7:3. Traditional CT imaging characteristics in the portal venous phase were collected by two radiologists (SPH and ZXL, who have 4 and 30 years of experience in abdominal radiology, respectively). Patient demographics and traditional CT imaging characteristics were used to construct the clinical model. The radiomics score was calculated based on the radiomics features extracted from the portal venous CT images and the random forest (RF) algorithm to construct the radiomics model. The combined model was constructed using the radiomics score and significant clinical factors according to the multivariate logistic regression. The diagnostic efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC).

Results

The RF score based on the eight validated features extracted from the portal venous CT images was used to build the radiomics model. Painless hematuria as an independent risk factor was used to build the clinical model. The combined model was constructed using the RF score and the selected clinical factor. Both the radiomics model and combined model showed higher efficacy in differentiating infiltrative RCC and pyelocaliceal UTUC in the training and testing cohorts with AUC values of 0.95 and 0.90, respectively, for the radiomics model and 0.99 and 0.90, respectively, for the combined model. The decision curves of the combined model as well as the radiomics model indicated an overall net benefit over the clinical model. Both the radiomics model and the combined model achieved a notable reduction in false-positive and false-negativerates, resulting in significantly higher accuracy compared to the visual assessments in both the training and testing cohorts.

Conclusion

The radiomics model and combined model had the potential to accurately differentiate infiltrative RCC and pyelocaliceal UTUC, which invades the renal parenchyma, and provide a new potentially non-invasive method to guide surgery strategies.

Four-phase computed tomography helps differentiation of renal oncocytoma with central hypodense areas from clear cell renal cell carcinoma

PURPOSE

To explore the utility of four-phase computed tomography (CT) in distinguishing renal oncocytoma with central hypodense areas from clear cell renal cell carcinoma (ccRCC).

METHODS

Eighteen patients with oncocytoma and 63 patients with ccRCC presenting with central hypodense areas were included in this study. All patients underwent four-phase CT imaging including the excretory phases later than 20 min after contrast injection. Two blinded experienced radiologists visually reviewed the enhancement features of the central hypodense areas in the excretory phase images and selected the area demonstrating the greatest degree of enhancement of the tumor in the corticomedullary phase images. Regions of interest (ROIs) were placed in the same location in each of the three contrast-enhanced imaging phases. Additionally, ROIs were placed in the adjacent normal renal cortex for normalization. The ratio of the lesion to cortex attenuation (L/C) for the three contrast-enhanced imaging phases and absolute de-enhancement were calculated. The receiver operating characteristic curve was used to obtain the cut-off values.

RESULTS

Complete enhancement inversion of the central areas was observed in 12 oncocytomas (66.67%) and 16 ccRCCs (25.40%) (P = 0.003). Complete enhancement inversion combined with L/C in the corticomedullary phase lower than 1.0 (P < 0.001) or absolute de-enhancement lower than 42.5 HU (P < 0.001) provided 86.42% and 85.19% accuracy, 61.11% and 55.56% sensitivity, 93.65% and 93.65% specificity, 73.33% and 71.43% positive predictive value (PPV), and 89.39% and 88.06% negative predictive value (NPV), respectively, for the diagnosis of oncocytomas. Combined with complete enhancement inversion, L/C in the corticomedullary phase lower than 1.0 and absolute de-enhancement lower than 42.5 HU provided 87.65%, 55.56%, 96.83%, 83.33%, and 88.41% of accuracy, sensitivity, specificity, PPV, and NPV, respectively, for the diagnosis of oncocytomas.

CONCLUSION

The combination of enhancement features of the central hypodense areas and the peripheral tumor parenchyma can help distinguish oncocytoma with central hypodense areas from ccRCC.

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer

Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.

CT differentiation of the oncocytoma and renal cell carcinoma based on peripheral tumor parenchyma and central hypodense area characterisation

BACKGROUND

Although the central scar is an essential imaging characteristic of renal oncocytoma (RO), its utility in distinguishing RO from renal cell carcinoma (RCC) has not been well explored. The study aimed to evaluate whether the combination of CT characteristics of the peripheral tumor parenchyma (PTP) and central hypodense area (CHA) can differentiate typical RO with CHA from RCC.

METHODS

A total of 132 tumors on the initial dataset were retrospectively evaluated using four-phase CT. The excretory phases were performed more than 20 min after the contrast injection. In corticomedullary phase (CMP) images, all tumors had CHAs. These tumors were categorized into RO (n = 23), clear cell RCC (ccRCC) (n = 85), and non-ccRCC (n = 24) groups. The differences in these qualitative and quantitative CT features of CHA and PTP between ROs and ccRCCs/non-ccRCCs were statistically examined. Logistic regression filters the main factors for separating ROs from ccRCCs/non-ccRCCs. The prediction models omitting and incorporating CHA features were constructed and evaluated, respectively. The effectiveness of the prediction models including CHA characteristics was then confirmed through a validation dataset (8 ROs, 35 ccRCCs, and 10 non-ccRCCs).

RESULTS

The findings indicate that for differentiating ROs from ccRCCs and non-ccRCCs, prediction models with CHA characteristics surpassed models without CHA, with the corresponding areas under the curve (AUC) being 0.962 and 0.914 versus 0.952 and 0.839 respectively. In the prediction models that included CHA parameters, the relative enhancement ratio (RER) in CMP and enhancement inversion, as well as RER in nephrographic phase and enhancement inversion were the primary drivers for differentiating ROs from ccRCCs and non-ccRCCs, respectively. The prediction models with CHA characteristics had the comparable diagnostic ability on the validation dataset, with respective AUC values of 0.936 and 0.938 for differentiating ROs from ccRCCs and non-ccRCCs.

CONCLUSION

The prediction models with CHA characteristics can help better differentiate typical ROs from RCCs. When a mass with CHA is discovered, particularly if RO is suspected, EP images with longer delay scanning periods should be acquired to evaluate the enhancement inversion characteristics of CHA.

The Role of CT Imaging in Characterization of Small Renal Masses

Small renal masses (SRM) are increasingly detected incidentally during imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This review focuses on CT characterization of small renal masses, perhaps helping us understand small renal masses. The following aspects were considered for the review: (a) assessing the presence of fat, (b) assessing the enhancement, (c) differentiating renal tumor subtype, and (d) identifying valuable CT signs.

Characteristics of renal oncocytomas and clinical novelties: Single center experience of 17 years

Objective:

To assess the characteristics of renal oncocytomas and the clinical outcomes of patients in the last 17 years in our institution.

Methodology:

The medical records of the patients who underwent partial and radical nephrectomy from May 2004 to December 2021 were evaluated retrospectively. Radiology and pathology results were evaluated. Patients diagnosed with oncocytoma after surgery were included in the study.

Results:

Out of 791 patients who were operated for renal masses, 55 patients with the diagnosis of oncocytoma were included in the study, 17 of them were female. The mean age of the patients was 64.77 ± 10.58 years. Open and laparoscopic methods were applied to patients. Partial nephrectomy was performed in 25 patients (46.2%). It was observed that none of the patients with a mean follow-up of 76 months developed recurrence or death due to oncocytoma.

Conclusion:

Oncocytoma is a benign and rare tumor of the kidney which distinguishing it from malign tumors preoperatively with recent techniques is impossible. Especially in small sized tumors, considering the possibility of oncocytoma, nephron sparing surgery should be preferred in terms of patients’ benefit. Further research is needed for the novel imaging techniques and biomarkers proposed to be used in routine use to distinguish oncocytoma.

Use of specific contrast-enhanced CT regions of interest to differentiate renal oncocytomas from small clear cell and chromophobe renal cell carcinomas

PURPOSE We aimed to examine the usefulness of utilizing a specific contrast-enhanced computed tomog raphy (CT) region of interest (ROI) to differentiate renal oncocytoma (RO) from small clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC). METHODS A retrospective analysis of pre-contrast phase (PCP), corticomedullary phase (CMP), and nephro graphic phase (NP) contrast-enhanced CT images of the histopathologically confirmed initial cohort (27 ROs, 74 ccRCCs, and 36 chRCCs) was conducted. Small, medium, large, and whole ROIs (S-ROI, M-ROI, L-ROI, and W-ROI, respectively) were utilized for CT attenuation value of tumor (AVT), lesion-to-cortex attenuation (L/C), and heterogeneous degree of tumor (HDT) calcula tions. Differences in these parameters were then compared between RO and ccRCC/chRCC, with receiver operating characteristic (ROC) curves being utilized to gauge the diagnostic utility of the statistically significant parameters. Logistic regression analyses were employed to identify key factors capable of differentiating RO and ccRCC/chRCC, with predictive models further being established. A validation cohort (6 ROs, 30 ccRCCs, and 12 chRCCs) was then employed to vali date the performance of the predictive models. RESULTS Of the parameters evaluated using different ROIs, L/C-CMP (S-ROI) (0.88 ± 0.15 vs. 1.13 ± 0.25, P < .001) and HDT-CMP (W-ROI) (23.02 (12.00-51.21) vs. 37.81 (16.09-89.45), P < .001) were best suited to differentiating RO and ccRCC, yielding respective area under the curve (AUC) values of 0.803 and 0.834. AVT-NP (S-ROI) (122.85 ± 18.87 vs. 86.50 ± 18.65, P < .001) and AVT-NP (M-ROI) (119 (86-167) vs. 81.5 (53-142), P < .001) were better able to differentiate RO and chRCC, yielding respective AUC values of 0.918 and 0.906. Logistic regression analyses revealed that L/C-CMP (S-ROI) and HDT-PCP, as well as AVT-NP (S-ROI) and HDT-CMP, were the primary factors capable of differentiating RO from ccRCC and chRCC, respectively. The predictive model developed to dif ferentiate between RO and ccRCC exhibited a sensitivity of 66.67% and 55.14% in the initial and validation cohorts, respectively, with corresponding specificity of 94.59% and 93.55%, accuracy of 87.13% and 86.84%, and AUC of 0.908 and 0.876. The predictive model developed to differ entiate between RO and chRCC exhibited a sensitivity of 85.19% and 100.00% in the initial and validation cohorts, respectively, with corresponding specificity of 94.59% and 92.86%, accuracy of 87.30% and 95.24%, and AUC of 0.944 and 0.959. CONCLUSION These data demonstrate that a combination of quantitative parameters measured with particu lar ROIs can enable the efficient and reliable differentiation of RO from ccRCC and chRCC for use in routine patient differential diagnosis.

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.

Renal epithelioid angiomyolipoma: computed tomography manifestation and radiologic-pathologic correlation depending on different epithelioid component percentages

BACKGROUND

Renal epithelioid angiomyolipoma (EAML) is a rare and potentially malignant mesenchymal lesion mainly composed of epithelioid cells. Although some case reports or small case series have been published, the computed tomography (CT) manifestations and radiologic-pathologic correlation depending on different epithelioid component percentages have not been studied before.

OBJECTIVE

To investigate the CT manifestation and radiologic-pathologic correlation between renal EAML and angiomyolipoma (AML) with epithelioid component.

METHODS

The clinicopathologic and imaging data of 53 patients with an original diagnosis of EAML or AML with epithelioid component were retrospectively collected from three hospitals. All tissue specimens were re-sectioned and re-observed under the microscope. Samples were divided into an EAML group (≥ 80% epithelioid component, n = 25) and AML with epithelioid component group (5% ≤ epithelioid component < 80%, n = 28). Two radiologists reviewed the images in consensus, describing and comparing the CT manifestation, including the long diameter of the tumor, morphology, presence of necrosis or cystic change, hemorrhage, fat, calcification, enlarged blood vessels, and dynamic enhancement pattern according to the Hounsfield unit value of each CT phase between the two groups. The radiologic-pathologic correlation depending on the different percentages of epithelioid component were studied.

RESULTS

The long diameter of the tumor, presence of necrosis or cystic change, fat, enhancement pattern, and tumor-to-cortex enhancement ratio of the cortical phase between the two groups were significantly different (z =  - 2.932, P = 0.003; χ² = 18.020, P < 0.001; χ² = 16.377, P < 0.001; P = 0.020; and T =  - 3.944, P < 0.001, respectively). In multivariate logistic regression analysis, the significant predictive factors of EAML included the presence of necrosis or cystic change [odds ratio (OR) 11.864, P = 0.001] and absence of fat (OR 0.095, P = 0.003). Correlation analysis found that the presence of necrosis or cystic change (r = 0.679, P < 0.001) and fat (r =  - 0.603, P < 0.001) were both moderately related to the epithelioid component percentage. The combined model based on the presence of necrosis or cystic change and absence of fat yielded the best diagnostic performance in discriminating EAML and AML with epithelioid component with the highest area under the curve (0.887).

CONCLUSION

EAML has characteristic CT signs; these characteristic CT signs are closely related to the epithelioid component percentage. The presence of necrosis or cystic change and the absence of fat were independent predictors of EAML.

A Concise Review of the Multimodality Imaging Features of Renal Cell Carcinoma

The evaluation of renal cell carcinoma (RCC) is routinely performed using the multimodality imaging approach, including ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Ultrasonography is the most frequently used imaging modality for the initial diagnosis of renal masses. The modality of choice for the characterization of the renal mass is multiphasic CT. Recent advances in CT technology have led to its widespread use as a powerful tool for preoperative planning, reducing the need for catheter angiography for the evaluation of vascular invasion. CT is also the standard imaging modality for staging and follow-up. MRI serves as a problem-solving tool in selected cases of undefined renal lesions. Newer MRI techniques, such as arterial spin labeling and diffusion-weighted imaging, have the potential to characterize renal lesions without contrast media, but these techniques warrant further investigation. PET may be a useful tool for evaluating patients with suspected metastatic disease, but it has modest sensitivity in the diagnosis and staging of RCC. The newer radiotracers may increase the accuracy of PET for RCC diagnosis and staging. In summary, the main imaging modality used for the characterization, staging, and surveillance of RCC is multiphasic CT. Other imaging modalities, such as MRI and PET, are used for selected indications.

Small renal masses (≤ 4 cm): differentiation of oncocytoma from renal clear cell carcinoma using ratio of lesion to cortex attenuation and aorta-lesion attenuation difference (ALAD) on contrast-enhanced CT

PURPOSE

We investigate the use of ratio of lesion to cortex (L/C) attenuation and aorta-lesion attenuation difference (ALAD) on multiphase contrast-enhanced CT to help distinguish oncocytoma from clear cell RCC in small renal masses (diameter < 4 cm).

METHODS

We retrospectively identified 76 patients that undergo CT before surgery for a suspicious small renal mass between January 2014 and December 2018 with pathological diagnosis of 21 oncocytomas (ROs), 25 clear cell RCCs, 7 chromophobe RCCs, 7 papillary RCCs, 7 multilocular cystic RCCs, 7 angiomyolipomas and 2 leiomyomas. CT attenuation values were obtained for the tumor, the normal renal cortex and the aorta, placing a circular region of interest (ROI) in the same slice by two radiologists, independently.

RESULTS

In the corticomedullary phase, ROs showed isodense enhancement to the renal cortex (ratio L/C 0.92 ± 0.12), while clear cell RCCs appeared hypodense to the renal cortex (ratio L/C 0.69 ± 0.20; p < 0.01) with an accuracy of 80% for diagnosing RO. In nephrographic phase, the ratio L/C attenuation was lower than the corticomedullary phase in ROs (0.78 ± 0.11) showing an early washout pattern, while the ratio L/C was similar to the corticomedullary phase in clear cell RCCs (0.69 ± 0.13; p = 0.025, with an accuracy of 65% for diagnosing RO). The ratio L/C attenuation showed considerable overlap between ROs and clear cell RCCs in the excretory phase (p = 0.27). Mean ALAD values in the nephrographic phase were 21.95 ± 16.24 for ROs and 36.96 ± 30.53 for clear cell RCCs (p = 0.049).

CONCLUSION

The ratio L/C attenuation in corticomedullary phase may be useful to differentiate RO from clear cell RCC.

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.

[Retrospective study of diagnosis and treatment of renal oncocytoma]

OBJECTIVE

To summarize the experience of diagnosis and surgical treatment of renal oncocytoma, and to evaluate the surgical results based on follow-up results, in order to find the best strategy.

METHODS

In the study, 21 cases with renal oncocytoma from December 2003 to April 2016 in Peking University Third Hospital were retrospectively analyzed, including 4 males, and 17 females, with 10 cases on the right side and 11 cases on the left side. Their age was between 15 to 80 years (average: 58 years). Ultrasound or CT examination after admission was conducted. Ultrasound examination showed solid nodules. CT manifestations were solid masses with enhancement, and the tumor size was between 1.5 cm to 6.5 cm (average: 3.3 cm). Of the 21 cases, 9 were located in the middle of kidney, 7 were located in the upper pole, and 5 were located in the lower pole. After preoperative examination, according to the size and location of the tumor, laparoscopic partial nephrectomy or laparoscopic nephrectomy was performed, respectively.

RESULTS

All the operations were successful, in which 17 cases underwent laparoscopic partial nephrectomy (including 3 cases which were converted to open surgery), and 4 cases underwent laparoscopic radical nephrectomy. The operation time ranged from 75 to 274 min (mean: 144 min), and the blood loss ranged from 10 to 1 000 mL (mean: 115 mL). The postoperative hospital stay time ranged from 6 to 13 d (average: 8.2 d). The pathological results were all renal oncocytoma. In the study, 17 cases were followed up while 4 cases were lost to follow-up. The follow-up time ranged from 12 to 175 months (mean: 44 months). One case died in 20 months after operation with unknown reason, and there were no recurrence or metastasis in the other 16 cases.

CONCLUSION

Renal oncocytoma is a benign tumor with good prognosis. Enhanced CT is an effective diagnostic method in assistant examination, but it is difficult to differentiate clear cell carcinoma only from the naked eye. It is worthwhile to measure CT value at different stages of the tumor by picture archiving and communication systems (PACS), and to compare with CT value of adjacent kidney tissue may improve the diagnostic efficiency of CT. Laparoscopic surgery is an effective treatment for renal oncocytoma. We recommend laparoscopic partial nephrectomy for the patients with renal oncocytoma as the best choice if conditions permit.

Discrimination of oncocytoma and chromophobe renal cell carcinoma using MRI

PURPOSE

We aimed to evaluate magnetic resonance imaging (MRI) features, including signal intensities, enhancement patterns and T2 signal intensity ratios to differentiate oncocytoma from chromophobe renal cell carcinoma (RCC).

METHODS

This retrospective study included 17 patients with oncocytoma and 33 patients with chromophobe RCC who underwent dynamic MRI. Two radiologists independently reviewed images blinded to pathology. Morphologic characteristics, T1 and T2 signal intensities were reviewed. T2 signal intensities, wash-in, wash-out values, T2 signal intensity ratios were calculated. Sensitivity and specificity analyses were performed.

RESULTS

Mean ages of patients with oncocytoma and chromophobe RCC were 61.0±11.6 and 58.5±14.0 years, respectively. Mean tumor size was 60.6±47.3 mm for oncocytoma, 61.7±45.9 mm for chromophobe RCC. Qualitative imaging findings in conventional MRI have no distinctive feature in discrimination of two tumors. Regarding signal intensity ratios, oncocytomas were higher than chromophobe RCCs. Renal oncocytomas showed higher signal intensity ratios and wash-in values than chromophobe RCCs in all phases. Fast spin-echo T2 signal intensities were higher in oncocytomas than chromophobe RCCs.

CONCLUSION

Signal intensity ratios, fast spin-echo T2 signal intensities and wash-in values constitute diagnostic parameters for discriminating between oncoytomas and chromophobes. In the excretory phase of dynamic enhanced images, oncocytomas have higher signal intensity ratio than chromophobe RCC and high wash-in values strongly imply a diagnosis of renal oncocytoma.

Giant cell tumour of the bone treated with denosumab: How has the blood supply and oncological prognosis of the tumour changed?

Background

Denosumab is gradually applied to refractory or unresectable giant cell tumour of the bone. Whether denosumab can effectively reduce the blood supply of tumour and bring benefit is worthy of study. The aim of the study is to evaluate the related changes after treatment: blood supply, surgical plan downstaging, surgical difficulty and oncological prognosis.

Methods

A self-case-control study was performed from June 2014 to November 2016, and 18 patients were enrolled. Patients received subcutaneous denosumab 120 mg every 4 weeks preoperatively, with additional doses administered on Days 8 and 15 during the first month of therapy. The initial treatment duration was 12 weeks. After 12 weeks treatment, enhanced CT examination was performed for evaluating whether surgical treatment was practicable. The patients received preoperative denosumab treatment for 5 (median 3, range 3-12) months in average. The microvessel density of tumour samples was calculated for evaluating tumour blood supply. The computed tomography (CT) enhancement rate was compared before and after treatment. The related changes of parameters were recorded as the following: clinical benefits, serious side effects, enhancement rate of CT, surgical plans, intraoperative blood loss, operative time, surgical difficulty, histological changes and local recurrence. The patients were followed up every 3 months postoperatively.

Results

The average CT enhancement rate of lesions was 2.08 and 1.40 before and after treatment (p = 0.000), respectively. The unenhanced CT value was significantly increased after treatment (p = 0.038). The CT enhancement rate changed more significantly in pelvic or sacral lesions than that in limb lesions (p = 0.024). Sixteen cases underwent final surgery, and surgical plan was downstaged. The histological examination showed tumour cells were significantly reduced or even disappeared after treatment. The microvessel density decreased significantly after treatment. The mean postoperative follow-up was 18.8 (10-31) months, and five patients had local recurrence. The high local recurrence rate (4/6) in sacral tumours may be related to the increased difficulty of curettage.

Conclusion

Denosumab treatment can reduce the blood supply of giant cell tumour. The sacral or pelvic lesions changed more significantly than limb lesions. The surgical plan downstaging can also be achieved. The clear margin after denosumab treatment facilitated tumour resection but, increased difficult in curettage surgery, and high recurrence rate of sacral tumour is being concerned.

The Translational Impact of this Article

Denosumab is a new type of humanized monoclonal antibody which showed some effect in the treatment giant cell tumor of bone. Pre-operative treatment with denosamub can reduce intra-operative blood loss and down-stage surgical plan in suitable cases.

Value of Triphasic MDCT in the Differentiation of Small Renal Cell Carcinoma and Oncocytoma

Introduction

Although differentiation between benign and malignant small renal tumors (≤4 cm) is still difficult, it is a demand for decision making and determining the treatment strategy. Our aim is to evaluate the role of multidetector row computed tomography (MDCT) in the differentiation of small renal clear cell carcinoma (RCC) and renal oncocytoma (RO).

Methods

We reviewed triphasic computed tomographic (CT) scans performed in 43 patients diagnosed with RCC (n = 23) and RO (n = 21). After an unenhanced CT phase of the upper abdomen, triple-phase acquisition included a cortico-medullary phase (CMP), a nephrographic phase (NP), and a pyelographic phase (PP), and lesions were evaluated both qualitatively and quantitatively.

Results

RCCs were hypervascular in 13 cases and hypovascular in 10 cases, while ROs were hypervascular in nine cases and hypovascular in 12 cases. Mean attenuation values (MAVs) for hypervascular RCCs and hypervascular ROs on unenhanced examination were 34.0 ± 7.1 and 31.3 ± 8.1 HU, respectively. Enhancement in CMP was 173.1 ± 45.2 HU for RCCs and 151.1 ± 36.0 HU for ROs and a gradual wash-out in NP (148.8 ± 34.3 and 137.1 ± 33.9 HU for RCCs and ROs, respectively) and in PP (98.2 ± 36.0 HU for RCCs and 79.4 ± 21.5 HU for ROs) was observed. MAV for hypovascular RCCs and hypovascular ROs on unenhanced examination were 32.4 ± 12.0 and 28.9 ± 8.0 HU, respectively. Both hypovascular RCCs and ROs showed a statistically significant difference in each post contrastographic phase.

Conclusions

Absolute attenuation and the quantitative amount of the enhancement were not strong predictors for RO and RCC differentiation.

A practical imaging classification for the non-invasive differentiation of renal cell carcinoma into its main subtypes

AIM

Renal cell carcinoma (RCC) is a heterogeneous disease which encompasses various subtypes that exhibit differing biologic behavior and imaging findings. Non-invasive subtype differentiation by imaging facilitates prognostication and treatment selection. The aim of the study was to evaluate the performance of a diagnostic imaging key based on tumor morphology, T2 signal intensity on MRI, and tumor vascularity for differentiating RCC into its subtypes.

MATERIALS AND METHODS

Using a custom-designed diagnostic imaging key, three blinded fellowship-trained abdominal radiologists independently evaluated the cross-sectional imaging of 50 histologically proven RCCs and categorized these into subtypes in two sessions. The diagnostic performance of the imaging key was evaluated and compared to the baseline performance without the key.

RESULTS

The 50 RCCs comprised 20 (40%) clear cell, 17 (34%) papillary, and 13 (26%) chromophobe tumors. All expert readers demonstrated an improvement in diagnostic accuracy by an average of 5.3% with the use of the key. The readers showed good to excellent diagnostic performance for clear cell RCC (area under the receiver operating curve, AUROC of 0.86-0.91) and papillary RCC (AUROC of 0.82-0.87), and fair performance with chromophobe RCC (AUROC of 0.67-0.77). The Reader-to-SOR (standard of reference) agreement increased from 0.53 (moderate) to 0.67 (good) with the use of the key.

CONCLUSION

The diagnostic imaging key facilitates RCC subtype characterization and can be used as a decision support tool.

Multimodality Imaging Characteristics of the Common Renal Cell Carcinoma Subtypes: An Analysis of 544 Pathologically Proven Tumors

Objectives:

The objective of this study was to define the characteristic imaging appearances of the common renal cell carcinoma (RCC) subtypes.

Materials and Methods:

The Institutional Review Board approval was obtained for this HIPAA-compliant retrospective study, and informed consent was waived. 520 patients (336 men, 184 women; age range, 22–88 years) underwent preoperative cross-sectional imaging of 544 RCCs from 2008 to 2013. The imaging appearances of the RCCs and clinical information were reviewed. Data analysis was performed using parametric and nonparametric statistics, descriptive statistics, and receiver operating characteristic analysis.

Results:

The RCC subtypes showed significant differences (P < 0.001) in several imaging parameters such as tumor margins, tumor consistency, tumor homogeneity, the presence of a central stellate scar, T2 signal intensity, and the degree of tumor enhancement. Low T2 signal intensity on magnetic resonance imaging (MRI) allowed differentiation of papillary RCC from clear cell and chromophobe RCCs with 90.9% sensitivity and 93.1% specificity. A tumor-to-cortex ratio ≥1 on the corticomedullary phase had 98% specificity for clear cell RCC.

Conclusion:

The T2 signal intensity of the tumor on MRI and its degree of enhancement are useful imaging parameters for discriminating between the RCC subtypes while gross morphological findings offer additional value in RCC profiling.

Review of renal cell carcinoma and its common subtypes in radiology

Representing 2%-3% of adult cancers, renal cell carcinoma (RCC) accounts for 90% of renal malignancies and is the most lethal neoplasm of the urologic system. Over the last 65 years, the incidence of RCC has increased at a rate of 2% per year. The increased incidence is at least partly due to improved tumor detection secondary to greater availability of high-resolution cross-sectional imaging modalities over the last few decades. Most RCCs are asymptomatic at discovery and are detected as unexpected findings on imaging performed for unrelated clinical indications. The 2004 World Health Organization Classification of adult renal tumors stratifies RCC into several distinct histologic subtypes of which clear cell, papillary and chromophobe tumors account for 70%, 10%-15%, and 5%, respectively. Knowledge of the RCC subtype is important because the various subtypes are associated with different biologic behavior, prognosis and treatment options. Furthermore, the common RCC subtypes can often be discriminated non-invasively based on gross morphologic imaging appearances, signal intensity on T2-weighted magnetic resonance images, and the degree of tumor enhancement on dynamic contrast-enhanced computed tomography or magnetic resonance imaging examinations. In this article, we review the incidence and survival data, risk factors, clinical and biochemical findings, imaging findings, staging, differential diagnosis, management options and post-treatment follow-up of RCC, with attention focused on the common subtypes.