Solid renal cortical tumors: differentiation with CT

Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study

BACKGROUND

To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors.

MATERIALS AND METHODS

In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach.

RESULTS

A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original_shape_Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method.

CONCLUSIONS

The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance.

Renal glomus tumor: A case report and literature review

Glomus tumors are rare mesenchymal tumors involving cells from the glomus body, smooth muscle, and vasculature, typically found in distal extremities' skin. This case describes a 54-year-old woman with a history of hypothyroidism and hyperlipidemia, incidentally discovered to have a four-centimeter calcified renal tumor. Surgery was performed due to suspected malignancy. Immunohistochemical staining confirmed a renal glomus tumor, positive for muscle actin and smooth muscle actin (SMA). The tumor was benign, and no adjuvant therapy was needed. The patient remained recurrence-free during follow-up. Renal glomus tumors are predominantly benign, with surgical resection as the primary treatment.

Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann-Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting

OBJECTIVES

To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT).

MATERIAL AND METHODS

Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center.

RESULTS

The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify.

CLINICAL RELEVANCE STATEMENT

Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model

BACKGROUND

To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients.

METHODS

A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created.

RESULTS

Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively.

CONCLUSION

The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

Artificial Intelligence-Based Non-invasive Differentiation of Distinct Histologic Subtypes of Renal Tumors With Multiphasic Multidetector Computed Tomography

INTRODUCTION

With rising cases of renal cell carcinoma (RCC), precise identification of tumor subtypes is essential, particularly for detecting small, heterogenous lesions often overlooked in traditional histopathological examinations. This study demonstrates the non-invasive use of deep learning for Histopathological differentiation of renal tumors through quadriphasic multidetector computed tomography (MDCT).

PATIENTS AND METHODS

This prospective longitudinal study includes 50 subjects (32 males, 18 females) with suspected renal tumors. A deep neural network (DNN) is developed to predict RCC subtypes using peak attenuation values measured in Hounsfield Units (HUs) obtained from quadriphasic MDCT scans. The network then generates confidence scores for each of the four primary subtypes of renal tumors, effectively distinguishing between benign oncocytoma and various malignant subtypes.

RESULTS

Our neural network accurately distinguishes Renal tumor subtypes, including clear cell, papillary, chromophobe, and benign oncocytoma, with a confidence score of 68% with the network's diagnosis aligning with Histopathological examinations. Our network was also able to accurately classify RCC subtypes on a synthetically generated dataset with 20,000 samples.

CONCLUSION

We developed an artificial intelligence-based RCC subtype classification technique. Our approach is non-invasive and has the potential to transform the methodology in Renal oncology by providing accurate and timely diagnostic information and enhancing clinical decisions.

Quantitative Measurement on Contrast-Enhanced CT Distinguishes Small Clear Cell Renal Cell Carcinoma From Benign Renal Tumors: A Multicenter Study

RATIONALE AND OBJECTIVES

To evaluate the potential of quantitative measurements on contrast-enhanced CT (CECT) in differentiating small (≤4 cm) clear cell renal cell carcinoma (ccRCC) from benign renal tumors, including fat-poor angiomyolipoma (fpAML) and renal oncocytoma (RO).

MATERIALS AND METHODS

244 patients with pathologically confirmed ccRCC (n = 184) and benign renal tumors (fpAML, n = 50; RO, n = 10) were randomly assigned into training cohort (n = 193) and test cohort 1 (n = 51), while external test cohort 2 (n = 50) was from another hospital. Quantitative parameters were obtained from CECT (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP; excretory phase, EP) by measuring attenuation of renal mass and cortex and subsequently calculated. Univariable and multivariable logistic regression analyses were performed to evaluate the association between these parameters and ccRCC. Finally, the constructed models were compared with radiologists' diagnoses.

RESULTS

In univariable analysis, UP-related parameters, particularly UPC-T (cortex minus tumor attenuation on UP), demonstrated AUC of 0.766 in training cohort, 0.901 in test cohort 1, 0.805 in test cohort 2. The heterogeneity-related parameter SD (standard deviation) showed AUC of 0.781, 0.834, and 0.875 respectively. In multivariable analysis, model 1 incorporating UPC-T, NPC-T (cortex minus tumor attenuation on NP), CMPT-UPT (tumor attenuation on CMP minus UP), and SD yielded AUC of 0.866, 0.923, and 0.949 respectively. When compared with radiologists, multivariate models demonstrated higher accuracy (0.800-0.860) and sensitivity (0.794-0.971) than radiologists' assessments (accuracy: 0.700-0.720, sensitivity: 0.588-0.706).

CONCLUSION

Quantitative measurements on CECT, particularly UP- and heterogeneity-related parameters, have potential to discriminate ccRCC and benign renal tumors (fpAML, RO).

Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study

PURPOSE

There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC).

METHODS

This two-center retrospective study included 320 patients from the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU) and 132 patients from the Sun Yat-Sen University Cancer Center (SYSUCC). Data from patients at FAHSYSU were divided into a development dataset (n = 267) and a hold-out dataset (n = 53). The development dataset was used to obtain the optimal combination of CT modality and input channel. The hold-out dataset and SYSUCC dataset were used for independent internal and external validation, respectively.

RESULTS

In the development phase, models trained on unenhanced CT images performed significantly better than those trained on enhanced CT images based on the fivefold cross-validation. The best patient-level performance, with an average area under the receiver operating characteristic curve (AUC) of 0.951 ± 0.026 (mean ± SD), was achieved using the "unenhanced CT and 7-channel" model, which was finally selected as the optimal model. In the independent internal and external validation, AUCs of 0.966 (95% CI 0.919-1.000) and 0.898 (95% CI 0.824-0.972), respectively, were obtained using the optimal model. In addition, the performance of this model was better on large tumors (≥ 40 mm) in both internal and external validation.

CONCLUSION

The promising results suggest that our multichannel deep learning classifier based on unenhanced whole-tumor CT images is a highly useful tool for differentiating fp-AML from RCC.

Mucinous tubular and spindle cell carcinoma of the kidney: a report of seven cases

OBJECTIVE

To further analyse the imaging features and tumour outcomes of mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney.

MATERIALS AND METHODS

The current study retrospectively reviewed the clinical information of seven patients diagnosed with MTSCC at our institution from January 2011 to March 2023.

RESULTS

The median age at diagnosis was 52 years (range, 32-66 years) and the majority of patients were female (71.4%). On conventional abdominal ultrasound, the majority of the tumours (5/7) were heterogeneous hypoechoic or slightly hypoechoic. Colour Doppler flow imaging showed blood flow within the tumour in 2 cases and peripheral blood flow signal in 1 case. On non-enhanced CT, all tumours had a spherical or ovoid shape, with an expansile growth mode, and had clear or unclear boundaries with the surrounding renal parenchyma. The tumours were either partially exophytic (n = 4) or parenchymal (n = 3), while no cases of completely exophytic tumour was observed (n = 0). On contrast-enhanced CT, the majority of tumours (5/7) showed a heterogenous pattern of enhancement and the mean tumour diameter was 6.7 ± 4.4 cm (range, 2.1-16.8 cm). All patients underwent partial or radical nephrectomy for pT1a (42.9%), pT1b (28.5%), pT2 (14.3%) or pT3b (14.3%) stage. Among these, 1 patient (14.3%) had a level I tumour thrombus at diagnosis and died of disease 24.5 months later. The remaining patients had no recurrence or metastasis.

CONCLUSION

MTSCC is not universally indolent, which tends to occur in female patients of a broad range of ages. MTSCC is a hypovascular renal tumour, which is different from clear cell renal cell carcinoma (RCC); however, it is difficult to distinguish MTSCC from other hypovascular RCC subtypes because of the overlap of their imaging characteristics.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Comparative diagnostic performance of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging for differentiating clear cell and non-clear cell renal cell carcinoma

OBJECTIVE

To compare the diagnostic efficiency of contrast-enhanced ultrasound (CEUS) with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the differential diagnosis of clear and non-clear cell renal cell carcinoma, as confirmed by subsequent pathology.

METHODS

A total of 181 patients with 184 renal lesions diagnosed by both CEUS and DCE-MRI were enrolled in the study, including 136 clear cell renal cell carcinoma (ccRCC) and 48 non-clear cell renal cell carcinoma (non-ccRCC) tumors. All lesions were confirmed by histopathologic diagnosis after surgical resection. Interobserver agreement was estimated using a weighted kappa statistic. Diagnostic efficiency in evaluating ccRCC and non-ccRCC was compared between CEUS and DCE-MRI.

RESULTS

The weighted kappa value for interobserver agreement was 0.746 to 0.884 for CEUS diagnosis and 0.764 to 0.895 for DCE-MRI diagnosis. Good diagnostic performance in differential diagnosis of ccRCC and non-ccRCC was displayed by both CEUS and DCE-MRI: sensitivity was 89.7% and 91.9%, respectively; specificity was 77.1% and 68.8%, respectively; and area under the receiver operating curve was 0.834 and 0.803, respectively. No statistically significant differences were present between the two methods (p = 0.54).

CONCLUSIONS

Both CEUS and DCE-MRI imaging are effective for the differential diagnosis of ccRCC and non-ccRCC. Thus, CEUS could be an alternative to DCE-MRI as a first test for patients at risk of renal cancer, particularly where DCE-MRI cannot be carried out.

KEY POINTS

• CEUS and DCE-MRI features can help differentiate ccRCC and non-ccRCC. • The differential diagnosis of ccRCC and non-ccRCC by CEUS is comparable to that of DCE-MRI. • Interobserver agreement is generally high using CEUS and DCE-MRI.

Evaluation of renal space occupying lesions with multiparametric MRI and its correlation with histopathology findings- an observational study

The term multiparametric MRI, is a useful tool in reference to an approach that takes advantage of the added value of different MR imaging acquisitions to yield anatomic and pathophysiologic information about renal space occupying lesions and to evaluate patients with different tumors, including genitourinary malignancies. The role of multiparametric MRI is continuously growing because of its ability to detect and characterize renal space occupying lesions as well as to assess response to treatment. An observational study was carried out in 50 patients who presented with renal mass, based on clinical suspicion and prior imaging diagnosis of neoplastic renal space occupying lesion. Total renal space occupying lesions were 50, of which, 38 were males & 12 were females. The age range of the study population was 30-80 years. In our study, Agreement analysis between mpMRI diagnosis and HPE diagnosis of different RCC subtypes was statistically significant. So, multiparametric MRI had a role in differentiating the subtypes of RCC which had fair agreement with HPE. The present study results state that the renal mass lesions has different ADC values for different lesions because of the change in tissue contents and there was a statistically significant difference in ADC values between low and high-stage RCCs. Histologic and radiologic profiles of renal space occupying lesions and diverse subtypes of RCC can be used as biologic indicators of clinical behavior, response to treatment, and prognosis.

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.

Differentiation of renal masses with multi-parametric MRI: the de Silva St George classification scheme

Purpose

To develop a system for multi-parametric MRI to differentiate benign from malignant solid renal masses and assess its accuracy compared to the gold standard of histopathological diagnosis.

Methods

This is a retrospective analysis of patients who underwent 3 Tesla mpMRI for further assessment of small renal tumours with specific scanning and reporting protocol incorporating T2 HASTE signal intensity, contrast enhancement ratios, apparent diffusion coefficient and presence of microscopic/macroscopic fat. All MRIs were reported prior to comparison with histopathologic diagnosis and a reporting scheme was developed. 2 × 2 contingency table analysis (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)), Fisher Exact test were used to assess the association between suspicion of malignancy on mpMRI and histopathology, and descriptive statistics were performed.

Results

67 patients were included over a 5-year period with a total of 75 renal masses. 70 masses were confirmed on histopathology (five had pathognomonic findings for angiomyolipomas; biopsy was therefore considered unethical, so these were included without histopathology). Three patients were excluded due to a non-diagnostic result, non-standardised imaging and one found to be an organising haematoma rather than a mass. Therefore 72 cases were included in analysis (in 64 patients, with seven patients having multiple tumours). Unless otherwise specified, all further statistics refer to individual tumours rather than patients. 52 (72.2%) were deemed ‘suspicious or malignant’ and 20 (27.8%) were deemed ‘benign’ on mpMRI. 51 cases (70.8%) had renal cell carcinoma confirmed. The sensitivity, NPV, specificity and PPV for MRI for detecting malignancy were 96.1%, 90%, 85.7% and 94.2% respectively, Fisher’s exact test demonstrated p < 0.0001 for the association between suspicion of malignancy on MRI and histopathology.

Conclusion

The de Silva St George classification scheme performed well in differentiating benign from malignant solid renal masses, and may be useful in predicting the likelihood of malignancy to determine the need for biopsy/excision. Further validation is required before this reporting system can  be recommended for clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01082-9.

A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC

Purpose

A nomogram was constructed by combining clinical factors and a CT-based radiomics signature to discriminate between high-grade clear cell renal cell carcinoma (ccRCC) and type 2 papillary renal cell carcinoma (pRCC).

Methods

A total of 142 patients with 71 in high-grade ccRCC and seventy-one in type 2 pRCC were enrolled and split into a training cohort (n = 98) and a testing cohort (n = 44). A clinical factor model containing patient demographics and CT imaging characteristics was designed. By extracting the radiomics features from the precontrast phase, corticomedullary phase (CMP), and nephrographic phase (NP) CT images, a radiomics signature was established, and a Rad-score was computed. By combining the Rad-score and significant clinical factors using multivariate logistic regression analysis, a clinical radiomics nomogram was subsequently developed. The diagnostic performance of these three models was evaluated by using data from both the training and testing groups using a receiver operating characteristic (ROC) curve analysis.

Results

The radiomics signature contained eight validated features from the CT images. The relative enhancement value of CMP (REV1) was an independent risk factor in the clinical factor model. The area under the curve (AUC) value of the clinical radiomics nomogram was 0.974 and 0.952 in the training and testing cohorts, respectively. In the training cohort, the decision curves of the nomogram demonstrated an added overall net advantage compared to the clinical factor model.

Conclusion

A noninvasive prediction tool termed radiomics nomogram, combining clinical criteria and the radiomics signature, may accurately predict high-grade ccRCC and type 2 pRCC before surgery. It also has some importance in assisting clinicians in determining future treatment strategies.

Using Peritumor and Intratumor Vascularity on Preoperative Imaging to Predict Fuhrman Grade Histology of Renal Tumors

Objective To investigate if peri-tumor and/or intra-tumor vasculature is associated with high grade tumor histology for renal cell carcinoma. Methods A retrospective review at a tertiary care facility was performed of patients who underwent radical nephrectomy or partial nephrectomy for a renal tumor between January 2015 to December 2020. Data of tumor characteristics was collected from final pathology reports. A single radiologist specializing in genitourinary imaging reviewed all pre-operative cross-sectional imaging for peri-tumor vessels and intra-tumor vessels. Single and multivariable logistic regression was utilized to identify variables associated with high grade tumor histology. Results The average tumor size on final pathology was 6.4 cm (Range 3.0-17.0 cm). Ninety-two patients (56.1%) had either an enlarged peri-tumor vessel (n=72), an intra-tumor vessel (n=3), or both a peri-tumor vessel and an intra-tumor vessel (n=17). Of the 92 patients with either a peri-tumor vessel or both a peri-tumor vessel and intra-tumor vessel, 60.9% of these patients had high Fuhrman grade histology on final pathology (60.9% vs 39.1%, p<0.001). Pathologic stage T1a tumors with an enlarged peri-tumor vessel on pre-operative imaging were associated with high Fuhrman grade histology (58.3% vs 41.7%, p=0.015). Across all stages, the presence of an enlarged peritumor vessel was significantly associated with high Fuhrman grade (OR: 2.37, 95% CI 1.17 - 4.9, p = 0.01). Conclusion Findings suggest that vessels surrounding small renal tumors and large renal tumors is associated with high tumor grade (FG > 3). Further research is needed to support the association of peri-tumor vessels with high tumor grade.

Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses

Background Solid small renal masses (SRMs) (≤4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation. Purpose To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods This retrospective multicenter cross-sectional study included patients with consecutive solid (≥25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for ccLS was evaluated with the Fleiss κ statistic. Results A total of 241 patients (mean age, 60 years ± 13 [SD]; 174 men) with 250 solid SRMs were evaluated. The mean size was 25 mm ± 8 (range, 10-39 mm). Of the 250 SRMs, 119 (48%) were ccRCC. The sensitivity, specificity, and positive predictive value for the diagnosis of ccRCC when ccLS was 4 or higher were 75% (95% CI: 68, 81), 78% (72, 84), and 76% (69, 81), respectively. The negative predictive value of a ccLS of 2 or lower was 88% (95% CI: 81, 93). The percentages of ccRCC according to the ccLS were 6% (range, 0%-18%), 38% (range, 0%-100%), 32% (range, 60%-83%), 72% (range, 40%-88%), and 81% (range, 73%-100%) for ccLSs of 1-5, respectively. The mean interobserver agreement was moderate (κ = 0.58; 95% CI: 0.42, 0.75). Conclusion The clear cell likelihood score applied to multiparametric MRI had moderate interobserver agreement and differentiated clear cell renal cell carcinoma from other solid renal masses, with a negative predictive value of 88%. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mileto and Potretzke in this issue.

Mucinous Tubular and Spindle Cell Carcinoma of the Kidney: A Study of Clinical, Imaging Features and Treatment Outcomes

Purpose

To describe the clinical, imaging, pathological features and oncologic outcomes of mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney.

Patients and Methods

Twenty-two cases of MTSCC were pathologically identified between January 2004 and April 2021 at our institution. The clinical and imaging findings, pathological features, treatment methods and outcomes of the patients were reviewed.

Results

These cases included 17 women and 5 men, with a median age at diagnosis of 52.5 years. On contrast-enhanced CT, MTSCC was less enhanced than the adjacent renal parenchyma. Tumor attenuation values were 33.3 ± 6.8HU, 44.0 ± 9.1HU, 54.4 ± 13.9HU and 67.1 ± 11.8HU in the non-contrast, corticomedullary, nephrographic and excretory phases of CT, respectively. Contrast-enhanced ultrasonography and MRI also showed hypovascular features of the masses. On MRI, the tumors were isointense on T1-weighted images and slightly hypo- or hyperintense on T2-weighted images. Diffusion-weighted imaging revealed a low apparent diffusion coefficient of the tumor. The patients were managed with laparoscopic partial nephrectomy (n=5), radical nephrectomy (n=16), or robotic-assisted laparoscopic partial nephrectomy (n=1). The median follow-up time was 59.5 months. All the patients were free of local recurrence or distant metastasis.

Conclusions

MTSCC is generally indolent and has favorable outcomes. The imaging features of MTSCC are generally hypovascular, which is significantly different from clear cell renal cell carcinoma. However, it is still difficult to distinguish MTSCC from other hypovascular renal tumors preoperatively because their imaging features overlap. Further studies are essential to fully characterize the features of this rare RCC variant.

Renal Cell Carcinoma or Oncocytoma? The Contribution of Diffusion-Weighted Magnetic Resonance Imaging to the Differential Diagnosis of Renal Masses

Background and Objectives: Renal Cell Carcinoma (RCC) accounts for 85% and oncocytomas constitute 3–7% of solid renal masses. Oncocytomas can be confused, especially with hypovascular RCC. The purpose of this research was to evaluate the contribution of diffusion-weighted imaging (DWI) and contrast-enhanced MRI sequences in the differential diagnosis of RCC and oncocytoma Materials and Methods: 465 patients with the diagnosis of RCC and 45 patients diagnosed with oncocytoma were retrospectively reviewed between 2009 to 2020. All MRI acquisitions were handled by a 1.5 T device (Achieva, Philips Healthcare, Best, The Netherlands) and all images were evaluated by the consensus of two radiologists with 10–15 years’ experience. The SPSS package program version 15.0 software was used for statistical analysis of the study. Chi-square test, Mann–Whitney U test or the Kruskal–Wallis tests were used in the statistical analysis. A receiver operating characteristic (ROC) curve was used to calculate the cut-off values Results: The results were evaluated with a 95% confidence interval and a significance threshold of p < 0.05. ADC values (p < 0.001) and enhancement index (p < 0.01) were significantly lower in the RCC group than the oncocytoma group. Conclusion: DWI might become an alternative technique to the contrast-enhanced MRI in patients with contrast agent nephropathy or with a high risk of nephrogenic systemic fibrosis, calculation of CI of the oncocytoma and RCCs in the contrast-enhanced acquisitions would contribute to the differential diagnosis.

How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score

The widespread use of cross-sectional imaging has led to a continuous increase in the number of incidentally detected indeterminate renal masses. Frequently, these clinical scenarios involve an older patient with comorbidities and a small renal mass (≤4 cm). Despite aggressive treatment in early stages of the disease, a clear positive effect in reducing kidney cancer-specific mortality is lacking, indicating that many renal cancers exhibit an indolent oncologic behavior. Furthermore, in general, one in five small renal masses is histologically benign and may not benefit from aggressive treatment. Although active surveillance is increasingly recognized as a management option for some patients, the absence of reliable clinical and imaging predictive biologic markers of aggressiveness can contribute to patient anxiety and limit its use in clinical practice. A standardized approach to the image interpretation of solid renal masses has not been broadly implemented. The clear cell likelihood score (ccLS) derived from multiparametric MRI is useful in noninvasively identifying the clear cell subtype, the most common and aggressive form of kidney cancer. Herein, a review of the ccLS is presented, including a step-by-step guide for image interpretation and additional guidance for its implementation in clinical practice.

Role of multidetector ct in quantitative enhancement- washout analysis of solid renal masses

Background

Enhancement washout technique in solid renal masses using multidetector computed tomography (MDCT) can differentiate different type of lesions. 99 Patients who are presenting with suspected renal masses or renal tumour for staging are included in this study. CT examination are carried out at urology and nephrology centre using MDCT. The attenuation values (Hounsfield Unit) will be assesed for each lesion on the pre enhanced, corticomedullary, nephrographic and delayed phases. Washout ratio will be calculated for each phase of enhancement in comparison to the unenhanced attenuation value. The characteristics of enhancement-washout will be correlated with the final histopathological diagnosis.

Results

Early enhancement and washout pattern was noted in 54 renal lesions (54.5%) representing 4 types of renal lesions; Oncocytoma (n = 13), clear cell renal cell carcinoma (n = 16), Chromophobe renal cell carcinoma (n = 15) and unclassified renal cell carcinoma (n = 10).Prolonged enhancement pattern was noted 45 lesions (45.4%); PRCC (n = 14), 10 case of lipid poor AML (n = 10), metanephric adenoma (n = 10) and Xp11 RCC (n = 11). High pre-contrast attenuation was noted in Xp 11RCC showing attenuation value 41.7 ± 6.823HU. The highest CMP values were noted in CCRCC (151.9 ± 20.4) followed by oncocytomas (137.6 ± 19.15HU) and then CHRCC (123.6 ± 16.6 HU)while the lowest values were noted in Metanephric adenoma)57.1 ± 17.4HU)and followed by PRCC (59.9 ± 4.8)and followed by lipid poor AML (79.17 ± 13.666) and RCC unclassified (89.06 ± 18.1).

Conclusions

Four-phase MDCT (the unenhanced, corticomedullary, nephrographic, and excretory phases) evaluate role of MDCT in differentiation of solid renal masses.

Active Surveillance of Renal Masses: The Role of Radiology

Active surveillance of renal masses, which includes serial imaging with the possibility of delayed treatment, has emerged as a viable alternative to immediate therapeutic intervention in selected patients. Active surveillance is supported by evidence that many benign masses are resected unnecessarily, and treatment of small cancers has not substantially reduced cancer-specific mortality. These data are a call to radiologists to improve the diagnosis of benign renal masses and differentiate cancers that are biologically aggressive (prompting treatment) from those that are indolent (allowing treatment deferral). Current evidence suggests that active surveillance results in comparable cancer-specific survival with a low risk of developing metastasis. Radiology is central in this. Imaging is used at the outset to estimate the probability of malignancy and degree of aggressiveness in malignant masses and to follow up masses for growth and morphologic change. Percutaneous biopsy is used to provide a more definitive histologic diagnosis and to guide treatment decisions, including whether active surveillance is appropriate. Emerging applications that may improve imaging assessment of renal masses include standardized assessment of cystic and solid masses and radiomic analysis. This article reviews the current and future role of radiology in the care of patients with renal masses undergoing active surveillance.

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

CT differentiation of fat-poor angiomyolipomas from papillary renal cell carcinomas: development of a predictive model

PURPOSE

To identify specific contrast-enhanced CT (CECT) findings and develop a predictive model with logistic regression to differentiate fat-poor angiomyolipomas (fpAML) from papillary renal cell carcinomas (pRCC).

METHODS

This is a single-institution retrospective study that assess CT features of histologically proven 67 pRCC and 13 fpAML. CECT variables were studied by means of univariate logistic regression. Variables included patients' demographics, tumor attenuation (unenhanced and at arterial, venous and excretory post-contrast phases), type of enhancement, morphological features (axial long and short diameters, long-short axis ratio (LSR) and tumor to kidney angle interface) and presence of visible calcifications or vessels. Those variables with a p ≤ 0.05 underwent standard stepwise logistic regression to find predictive combinations of clinical variables. Best models were evaluated by AUROC curves and were subjected to Leave-one-out cross validation to assess their robustness.

RESULTS

Odds ratio (OR) between pRCC and fpAML was statistically significant for patient's gender, tumor attenuation in arterial, venous and excretory phases, tumor's long diameter, short diameter, LSR, type of enhancement, presence of intratumoral vessels and tumor-kidney angle interface. The best predictive model resulted in an area under the curve (AUC) of 0.971 and included gender, tumor-kidney angle interface and venous attenuation with the following equation: Log(p/1 - p) = - 2.834 + 4.052 * gender +  - 0.066 * AngleInterface + 0.074 * VenousphaseHU.

CONCLUSIONS

The combination of patients' gender, tumor to kidney angle interface and venous enhancement helps to distinguish fpAML from pRCC.

CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma

Background

The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs).

Methods

A total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p < 0.001, Model 2 contains texture scores, and Model 3 contains the above two types of features.

Results

The three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748–0.823, 0.776–0.887 and 0.864–0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p < 0.001).

Conclusions

The predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options.

Clinical and radiological findings for small renal masses under active surveillance

OBJECTIVE

To analyze the experience performing active surveillance (AS) of small renal masses (SRMs) in our center and to correlate the evolution of SRMs under AS with clinical and radiological findings.

METHODS

Patients on AS between January 2012 until May 2020 for SRMs in our center have been included. Growth rate (GR) per year was analyzed and correlated with radiographic features. Patients with growth kinetics higher than 5mm/year during follow up were offered active treatment.

RESULTS

73 patients were included in AS: the mean age was 75.7 years, a mean initial tumour size of 21.2 mm, and a mean growth rate of 2.05 mm/year. Around 60 % had an ASA score of 3. The tumor size did not change over time in 43% of cases; in 4% we noticed a regression in size and in 52% of cases growth during follow-up (38% 1-5mm/year and 14% more than 5 mm/year). Delayed active treatment was indicated in 16 (21%) of cases. Treatment applied was as following: 2 radiofrequency ablations, 6 radical and 8 partial nephrectomies. A weak correlation was found between initial size and growth rate (r = 0.38, P = 0.02). No significant association was detected regarding any of the analyzed radiological findings and GR. With a mean follow up time of 33 months none of the patients presented metastatic progression.

CONCLUSION

Active surveillance is a feasible option for management of SRMs in selected patients without jeopardizing oncological safety. In our series, no clinical or radiological characteristics for predicting tumour growth were found.

Imaging analysis of 13 rare cases of renal collecting (Bellini) duct carcinoma in northern China: a case series and literature review

Background

Collecting (Bellini) duct carcinoma (CDC) is a highly malignant and rare kidney tumor. We report our 12-year experience with CDC and the results of a retrospective analysis of patients and tumor characteristics, clinical manifestations, and imaging features by computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)/CT.

Methods

Retrospective examination of tumors between January 2007 and December 2019 identified 13 cases of CDC from three medical centers in northern China. All 13 patients underwent CT scan, among which eight underwent dynamic enhanced CT scan, two underwent PET/CT scan, and one underwent magnetic resonance cholangiopancreatography (MRCP) examination. The lesions were divided into nephritis type and mass type according to the morphology of the tumors.

Results

The study group included ten men and three women with an average age of 64.23 ± 10.74 years. The clinical manifestations were gross hematuria, flank pain, and waist discomfort. The mean tumor size was 8.48 ± 2.48 cm. Of the 13 cases, six (46.2%) were cortical-medullary involved type and seven (53.8%) were cortex–medullary–pelvis involved type. Eleven (84.6%) cases were nephritis type and two (15.4%) were mass type. The lesions appeared solid or complex solid and cystic on CT and MRI. The parenchymal area of the tumors showed isodensity or slightly higher density on unenhanced CT scan in the 13 cases. PET/CT in two cases showed increased radioactivity intake. Evidence of intra-abdominal metastatic disease was present on CT in nine (69.2%) cases.

Conclusions

The imaging characteristics of CDC differ from those of other renal cell carcinomas. In renal tumors located in the junction zone of the renal cortex and medulla that show unclear borders, slight enhancement, and metastases in the early stage, a diagnosis of CDC needs to be considered. PET/CT provides crucial information for the diagnosis of CDC, as well as for designing treatment strategies including surgery.

Imaging Characterization of Renal Masses

The detection of a renal mass is a relatively frequent occurrence in the daily practice of any Radiology Department. The diagnostic approaches depend on whether the lesion is cystic or solid. Cystic lesions can be managed using the Bosniak classification, while management of solid lesions depends on whether the lesion is well-defined or infiltrative. The approach to well-defined lesions focuses mainly on the differentiation between renal cancer and benign tumors such as angiomyolipoma (AML) and oncocytoma. Differential diagnosis of infiltrative lesions is wider, including primary and secondary malignancies and inflammatory disease, and knowledge of the patient history is essential. Radiologists may establish a possible differential diagnosis based on the imaging features of the renal masses and the clinical history. The aim of this review is to present the contribution of the different imaging techniques and image guided biopsies in the diagnostic management of cystic and solid renal lesions.

Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma

OBJECTIVE

The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC).

MATERIALS AND METHODS

Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated.

RESULTS

In the comparison of the Radiomics values by stages, "minimum", "Long Run Low Gray-level Emphasis" values were higher in the stage 1-2 group, while "Energy", "Total energy", "Range", "Joint Average", "Sum Average", "Gray-Level Non-Uniformity", "Short-Run High Gray-level Emphasis ", "Run Length Non-Uniformity "and "High Gray-Level Run Emphasis "values were higher in the stage 3-4 group. Of these parameters, only "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity'' values were significantly lower in tumors with low Fuhrman grade (1-2) and low TNM stage (1-2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage.

CONCLUSION

This study demonstrates that "Gray-Level Non-Uniformity" and "Run Length Non-Uniformity "parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.

Differentiation of Clear Cell Renal Cell Carcinoma from other Renal Cell Carcinoma Subtypes and Benign Oncocytoma Using Quantitative MDCT Enhancement Parameters

Background and objectives: The use of non-invasive techniques to predict the histological type of renal masses can avoid a renal mass biopsy, thus being of great clinical interest. The aim of our study was to assess if quantitative multiphasic multidetector computed tomography (MDCT) enhancement patterns of renal masses (malignant and benign) may be useful to enable lesion differentiation by their enhancement characteristics. Materials and Methods: A total of 154 renal tumors were retrospectively analyzed with a four-phase MDCT protocol. We studied attenuation values using the values within the most avidly enhancing portion of the tumor (2D analysis) and within the whole tumor volume (3D analysis). A region of interest (ROI) was also placed in the adjacent uninvolved renal cortex to calculate the relative tumor enhancement ratio. Results: Significant differences were noted in enhancement and de-enhancement (diminution of attenuation measurements between the postcontrast phases) values by histology. The highest areas under the receiver operating characteristic curves (AUCs) of 0.976 (95% CI: 0.924–0.995) and 0.827 (95% CI: 0.752–0.887), respectively, were demonstrated between clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC)/oncocytoma. The 3D analysis allowed the differentiation of ccRCC from chromophobe RCC (chrRCC) with a AUC of 0.643 (95% CI: 0.555–0.724). Wash-out values proved useful only for discrimination between ccRCC and oncocytoma (43.34 vs 64.10, p < 0.001). However, the relative tumor enhancement ratio (corticomedullary (CM) and nephrographic phases) proved useful for discrimination between ccRCC, pRCC, and chrRCC, with the values from the CM phase having higher AUCs of 0.973 (95% CI: 0.929–0.993) and 0.799 (95% CI: 0.721–0.864), respectively. Conclusions: Our observations point out that imaging features may contribute to providing prognostic information helpful in the management strategy of renal masses.

Clinical Importance of Incidental Homogeneous Renal Masses That Measure 10-40 mm and 21-39 HU at Portal Venous Phase CT: A 12-Institution Retrospective Cohort Study

BACKGROUND. Incidental homogeneous renal masses are frequently encountered at portal venous phase CT. The American College of Radiology Incidental Findings Committee's white paper on renal masses recommends additional imaging for incidental homogeneous renal masses greater than 20 HU, but single-center data and the Bosniak classification version 2019 suggest the optimal attenuation threshold for detecting solid masses should be higher. OBJECTIVE. The purpose of this article is to determine the clinical importance of small (10-40 mm) incidentally detected homogeneous renal masses measuring 21-39 HU at portal venous phase CT. METHODS. We performed a 12-institution retrospective cohort study of adult patients who underwent portal venous phase CT for a nonrenal indication. The date of the first CT at each institution ranged from January 1, 2008, to January 1, 2014. Consecutive reports from 12,167 portal venous phase CT examinations were evaluated. Images were reviewed for 4529 CT examinations whose report described a focal renal mass. Eligible masses were 10-40 mm, well-defined, subjectively homogeneous, and 21-39 HU. Of these, masses that were shown to be solid without macroscopic fat; classified as Bosniak IIF, III, or IV; or confirmed to be malignant were considered clinically important. The reference standard was renal mass protocol CT or MRI, ultrasound of definitively benign cysts or solid masses, single-phase contrast-enhanced CT or unenhanced MRI showing no growth or morphologic change for 5 years or more, or clinical follow-up 5 years or greater. A reference standard was available for 346 masses in 300 patients. The 95% CIs were calculated using the binomial exact method. RESULTS. Eligible masses were identified in 4.2% of patients (514/12,167; 95% CI, 3.9-4.6%). Of 346 masses with a reference standard, none were clinically important (0%; 95% CI, 0-0.9%). Mean mass size was 17 mm; 72% (248/346) measured 21-30 HU, and 28% (98/346) measured 31-39 HU. CONCLUSION. Incidental small homogeneous renal masses measuring 21-39 HU at portal venous phase CT are common and highly likely benign. CLINICAL IMPACT. The change in attenuation threshold signifying the need for additional imaging from greater than 20 HU to greater than 30 HU proposed by the Bosniak classification version 2019 is supported.

Usefulness of multidetector computed tomography to differentiate between renal cell carcinoma and oncocytoma. A model validation

OBJECTIVE

The purpose of this study is to validate a multivariable predictive model previously developed to differentiate between renal cell carcinoma (RCC) and oncocytoma using CT parameters.

METHODS AND MATERIALS

We included 100 renal lesions with final diagnosis of RCC or oncocytoma studied before surgery with 4-phase multidetector CT (MDCT). We evaluated the characteristics of the tumors and the enhancement patterns at baseline, arterial, nephrographic and excretory MDCT phases.

RESULTS

Histopathologically 15 tumors were oncocytomas and 85 RCCs. RCCs were significantly larger (median 4.4 cm vs 2.8 cm, p = 0.006). There were significant differences in nodule attenuation in the excretory phase compared to baseline (median: 31 vs 42, p = 0.015), with RCCs having lower values. Heterogeneous enhancement patterns were also more frequent in RCCs (85.9% vs 60%, p = 0.027).Multivariable analysis showed that the independent predictors of malignancy were the enhancement pattern, with oncocytomas being more homogeneous in the nephrographic phase [Odds Ratio (OR) 0.16 (95% CI 0.03 to 0.75, p = 0.02)], nodule enhancement in the excretory phase compared to baseline, with RCCs showing lower enhancement [OR 0.96 (95% CI 0.93 to 0.99, p = 0.005)], and a size > 4 cm, with RCCs being larger [OR 5.89 (95% CI 1.10 to 31.58), p = 0.038].

CONCLUSION

The multivariable predictive model previously developed which combines different MDCT parameters, including lesion size > 4 cm, lesion enhancement in the excretory phase compared to baseline and enhancement heterogeneity, can be successfully applied to distinguish RCC from oncocytoma.

ADVANCES IN KNOWLEDGE

This study confirms that multiparametric assessment using MDCT (including parameters such as size, homogeneity and enhancement differences between the excretory and the baseline phases) can help distinguish between RCCs and oncocytomas. While it is true that this multiparametric predictive model may not always correctly classify renal tumors such as RCC or oncocytoma, it can be used to determine which patients would benefit from pre-surgical biopsy to confirm that the tumor is in fact an oncocytoma, and thereby avoid unnecessary surgical treatments.

Radiomics and Artificial Intelligence for Renal Mass Characterization

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.

Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis

OBJECTIVES

To explore whether clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (cRCC) can be distinguished using radiomics features extracted from magnetic resonance (MR) images.

METHODS

Seventy-seven patients (ccRCC = 32, pRCC = 23, cRCC = 22) underwent MRI before surgery between May 2013 and August 2018 in this retrospective study. Thirty-nine radiomics features were extracted from tumor volumes on three sequences (T2WI, EN-T1WI CMP, and EN-T1WI NP). The Kruskal-Wallis test with Bonferonni correction and variance threshold were used for feature selection among the three RCC subtypes. ROC curves for the three subtypes were generated based on radiomics features. AUC, accuracy, sensitivity, and specificity for subtype differentiation are reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics features.

RESULTS

Significant radiomics features among the three subtypes were identified, and ROC curves achieved excellent AUCs for T2WI, EN-T1WI CMP, EN-T1WI NP, and combined three MR sequences (0.631, 0.790, 0.959, and 0.959 between ccRCC and cRCC; 0.688, 0.854, 0.909, and 0.955 between pRCC and cRCC; 0.747, 0.810, 0.814, and 0.890 between ccRCC and pRCC). In addition, LDA demonstrated the three RCC subtypes were correctly classified by radiomics analysis (66.2% for EN-T1WI CMP, 71.4% for EN-T1WI NP, 55.8% for T2WI, and 71.4% for the combined three MR sequences).

CONCLUSIONS

Radiomics analysis can be used to differentiate among ccRCC, pRCC, and cRCC based on radiomics features extracted from multiple-sequence MRI and may help diagnose and treat RCC patients in the future, while further study is still needed.

KEY POINTS

• Radiomics features on multiple-sequence MRI can help differentiate the three subtypes of renal cell carcinoma (clear cell, papillary renal cell, and chromophobe renal cell carcinoma). • Radiomics features based on MRI indicate greater textural heterogeneity on ccRCCs than pRCCs and cRCCs (the highest AUCs on EN-T1WI NP are 0.814 for ccRCCs vs pRCCs and 0.959 for ccRCCs vs cRCCs, respectively). • There is a significant difference in the textural heterogeneity of radiomics features between pRCCs and cRCCs (the AUC is 0.909, 0.854, and 0.688 on EN-T1WI NP, EN-T1WI CMP, and T2WI, respectively).

Weakly-supervised convolutional neural networks of renal tumor segmentation in abdominal CTA images

Background

Renal cancer is one of the 10 most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) is an effective way to treat renal cancer. Localization and delineation of the renal tumor from pre-operative CT Angiography (CTA) is an important step for LPN surgery planning. Recently, with the development of the technique of deep learning, deep neural networks can be trained to provide accurate pixel-wise renal tumor segmentation in CTA images. However, constructing the training dataset with a large amount of pixel-wise annotations is a time-consuming task for the radiologists. Therefore, weakly-supervised approaches attract more interest in research.

Methods

In this paper, we proposed a novel weakly-supervised convolutional neural network (CNN) for renal tumor segmentation. A three-stage framework was introduced to train the CNN with the weak annotations of renal tumors, i.e. the bounding boxes of renal tumors. The framework includes pseudo masks generation, group and weighted training phases. Clinical abdominal CT angiographic images of 200 patients were applied to perform the evaluation.

Results

Extensive experimental results show that the proposed method achieves a higher dice coefficient (DSC) of 0.826 than the other two existing weakly-supervised deep neural networks. Furthermore, the segmentation performance is close to the fully supervised deep CNN.

Conclusions

The proposed strategy improves not only the efficiency of network training but also the precision of the segmentation.

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning

OBJECTIVE. This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. MATERIALS AND METHODS. This retrospective study included 1807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 patients between 2012 and 2016. Masses were classified as malignant (n = 136) or benign (n = 32). The dataset was randomly divided into five subsets: four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images). The Inception-v3 architecture convolutional neural network (CNN) model was used. The AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Multivariate logistic regression analysis was also performed. RESULTS. Malignant and benign lesions showed no significant difference of size. The AUC value of corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022). The highest accuracy (88%) was achieved in corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, sex, and lesion size. CONCLUSION. A deep learning method with a CNN allowed acceptable differentiation of small (≤ 4 cm) solid renal masses in dynamic CT images, especially in the corticomedullary image model.

Low-Grade Malignancy Glomus Tumor in a Setting of Multiple Glomus Tumors - Case Report

BACKGROUND:

Glomus tumors are rare neoplasms accounting for less than 2% of all soft tissue tumors but multiple lesions may be seen in up to 10% of the patients. Solitary glomus tumor (GT) most frequently appears as small nodule in specific locations such as subungual region or deep dermis. However, rarely these entities have been observed in extracutaneous locations such as the gastrointestinal, cardiovascular, respiratory tracts, and other visceral organs. A small fraction of the GTs may present as tumors of uncertain malignant potential or as malignant glomus tumors.

CASE PRESENTATION:

We report a patient with multiple glomus tumors on the time of diagnosis, which was histologically diagnosed as an atypical glomus tumor following resection of a tumor thrombus in the left renal vein, inferior vena cava trombus with intracardial extension, and mitral valve specimen. The intramuscular lesion from the thigh was diagnosed as a glomus tumor of uncertain malignant potential. Further examinations revealed multiple lesions trough her body: kidneys, breast, heart and subcutaneous tissue. The diagnosis of glomus tumor of uncertain malignant potential versus glomus tumor with low malignant potential could be quite challenging, and the clinical course may be as a determining factor for final diagnosis.

CONCLUSION:

To our knowledge, this is the only known case of glomus tumor with multiple organ involvement and aggressive biological behavior at presentation.

Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists

OBJECTIVE. The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. MATERIALS AND METHODS. This retrospective study included a cohort of 254 renal cell carcinomas (RCCs) (190 clear cell RCCs [ccRCCs], 38 chromophobe RCCs [chrRCCs], and 26 papillary RCCs [pRCCs]), 26 fat-poor angioleiomyolipomas, and 10 oncocytomas with preoperative CT examinations. Lesions identified by four expert-level radiologists (> 3000 genitourinary CT and MRI studies) were manually segmented for radiologicradiomic analysis. Disease-specific support vector machine radiologic-radiomic ML models for classification of renal masses were trained and validated using a 10-fold cross-validation. Performance values for the expert-level radiologists and radiologic-radiomic ML models were compared using the McNemar test. RESULTS. The performance values for the four radiologists were as follows: sensitivity of 73.7-96.8% (median, 84.5%; variance, 122.7%) and specificity of 48.4-71.9% (median, 61.8%; variance, 161.6%) for differentiating ccRCCs from pRCCs and chrRCCs; sensitivity of 73.7-96.8% (median, 84.5%; variance, 122.7%) and specificity of 52.8-88.9% for differentiating ccRCCs from fat-poor angioleiomyolipomas and oncocytomas (median, 80.6%; variance, 269.1%); and sensitivity of 28.1-60.9% (median, 84.5%; variance, 122.7%) and specificity of 75.0-88.9% for differentiating pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas (median, 50.0%; variance, 191.1%). After a 10-fold cross-validation, the radiologic-radiomic ML model yielded the following performance values for differentiating ccRCCs from pRCCs and chrRCCs, ccRCCs from fat-poor angioleiomyolipomas and oncocytomas, and pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas: a sensitivity of 90.0%, 86.3%, and 73.4% and a specificity of 89.1%, 83.3%, and 91.7%, respectively. CONCLUSION. Expert-level radiologists had obviously large variances in performance for differentiating benign from malignant solid renal masses. Radiologic-radiomic ML can be a potential way to improve interreader concordance and performance.

Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN's) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39% and 87.34%, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 94.07%. Here, we introduced a novel support vector machine-based method that helped to break the multi-class classification task into multiple binary classification tasks which not only improved the performance of the model but also helped to deal with data imbalance. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.

Differentiating between malignant and benign renal tumors: do IVIM and diffusion kurtosis imaging perform better than DWI?

OBJECTIVE

To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) in differentiating between malignant and benign renal tumors.

METHODS

Multiple b value DWIs and DKIs were performed in 180 patients with renal tumors, which were divided into clear cell renal cell carcinoma (ccRCC), non-ccRCC, and benign renal tumor group. The apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) maps were calculated. The diagnostic efficacy of various diffusion parameters for predicting malignant renal tumors was compared.

RESULTS

The ADC, D, and MD values of ccRCCs were higher, while D*, f, and MK values were lower than those of benign renal tumors (all p < 0.025). The D* and f values of non-ccRCCs were lower than those of benign renal tumors (p = 0.002 and p < 0.001, respectively). The difference of ADC, D, MD, and MK values between non-ccRCCs and benign renal tumors was not statistically significant (p > 0.05). The ADC, D, MD, and f values of ccRCCs were higher, while MK values were lower than those of non-ccRCCs (all p < 0.001). The AUC values of ADC, D, D*, f, MK, and MD were 0.849, 0.891, 0.708, 0.656, 0.862, and 0.838 for differentiating ccRCCs from benign renal tumors, respectively. The AUC values of D* and f were 0.772 and 0.866 for discrimination between non-ccRCCs and benign renal tumors, respectively.

CONCLUSION

IVIM parameters are the best, while DWI and DKI parameters have similar performance in differentiating malignant and benign renal tumors.

KEY POINTS

• The D value is the best parameter for differentiating ccRCC from benign renal tumors. • The f value is the best parameter for differentiating non-ccRCC from benign renal tumors. • Conventional DWI and DKI have similar performance in differentiating malignant and benign renal tumors.

Update on Indications for Percutaneous Renal Mass Biopsy in the Era of Advanced CT and MRI

OBJECTIVE. The objective of this article is to review the burgeoning role of percutaneous renal mass biopsy (RMB). CONCLUSION. Percutaneous RMB is safe, accurate, and indicated for an expanded list of clinical scenarios. The chief scenarios among them are to prevent treatment of benign masses and help select patients for active surveillance (AS). Imaging characterization of renal masses has improved; however, management decisions often depend on a histologic diagnosis and an assessment of biologic behavior of renal cancers, both of which are currently best achieved with RMB.

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

PURPOSE

Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses.

METHODS

With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes.

RESULTS

We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%.

CONCLUSIONS

The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade

OBJECTIVE

CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade.

METHODS

A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis.

RESULTS

A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful.

CONCLUSION

Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy.

KEY POINTS

• CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.

Usefulness of rapid kV-switching dual energy CT in renal tumor characterization

PURPOSE

To investigate whether iodine content can discriminate between benign or malignant renal tumors, malign tumor subtypes, low-grade and high-grade tumors on rapid kv-switching dual-energy CT (rsDECT).

METHODS

This prospective study enrolled 95 patients with renal tumors who underwent rsDECT for tumor characterization between 2016 and 2018. Attenuation on true and virtual unenhanced images, absolute enhancement and enhancement ratio and iodine content of each lesion on nephrographic phase iodine density images were measured. Histopathological diagnosis was obtained following either surgery or core biopsy.

RESULTS

Eighty-five tumors were renal cell carcinoma (RCC) (56 clear cell, 20 papillary, 9 chromophobe) and 10 were benign (6 angiomyolipoma,4 oncocytoma). 46 tumors were low-grade and 23 high-grade. There was significant difference between iodine content of clear cell and non-clear cell (papillary + chromophobe) RCC (p < 0.001). However, no significant iodine content differences were found between papillary and chromophobe RCC, benign and malignant tumors, low-grade and high-grade tumors. The best cut-off iodine content for differentiating clear cell from non-clear cell RCC was 3.2 mg/ml and clear cell from papillary RCC was 2.9 mg/ml with a high sensitivity and specificity. Also, significant difference was found between attenuation values of true and virtual unenhanced images (p = 0.007). Mean iodine content, absolute enhancement and enhancement ratio were highly correlated.

CONCLUSION

rsDECT contributes to renal tumor characterization by showing higher iodine content in clear cell RCCs compared with non-clear cell RCCs.

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.

Role of Virtual Biopsy in the Management of Renal Masses

OBJECTIVE. Renal masses comprise a heterogeneous group of pathologic conditions, including benign and indolent diseases and aggressive malignancies, complicating management. In this article, we explore the emerging role of imaging to provide a comprehensive noninvasive characterization of a renal mass-so-called "virtual biopsy"-and its potential use in the management of patients with renal tumors. CONCLUSION. Percutaneous renal mass biopsy (RMB) remains a valuable method to provide a presurgical histopathologic diagnosis of renal masses, but it is an invasive procedure and is not always feasible. Accumulating data support the use of imaging features to predict histopathology of renal masses. Imaging may help address some of the inherent limitations of RMB, and in certain settings, a multimodal clinical approach may allow decreasing the need for RMB.

Clear cell renal cell carcinoma: identifying PTEN expression on multiphasic MDCT

PURPOSE

To investigate whether multiphasic MDCT enhancement profiles can help to identify PTEN expression in clear cell renal cell carcinomas (ccRCCs). Lack of PTEN expression is associated with worsened overall survival, a more advanced Fuhrman grade, and a greater likelihood of lymph mode metastasis.

METHODS

With IRB approval for this retrospective study, we derived a cohort of 103 histologically proven ccRCCs with preoperative 4-phase renal mass MDCT from 2001-2013. Following manual segmentation, a computer-assisted detection algorithm selected a 0.5-cm-diameter region of maximal attenuation within each lesion in each phase; a 0.5-cm-diameter region of interest was manually placed on uninvolved renal cortex in each phase. The relative attenuation of each lesion was calculated as [(Maximal lesion attenuation - cortex attenuation)/cortex attenuation] × 100. Absolute and relative attenuation in each phase were compared using t tests. The performance of multiphasic enhancement in identifying PTEN expression was assessed with logistic regression analysis.

RESULTS

PTEN-positive and PTEN-negative ccRCCs both exhibited peak enhancement in the corticomedullary phase. Relative corticomedullary phase attenuation was significantly greater for PTEN-negative ccRCCs in comparison to PTEN-positive ccRCCs (33.7 vs. 9.5, p = 0.03). After controlling for lesion stage and size, relative corticomedullary phase attenuation had an accuracy of 84% (86/103), specificity of 100% (84/84), sensitivity of 11% (2/19), positive predictive value of 100% (2/2), and negative predictive value of 83% (84/101) in identifying PTEN expression.

CONCLUSION

Relative corticomedullary phase attenuation may help to identify PTEN expression in ccRCCs, if validated prospectively.