Chromophobe Renal Cell Carcinoma with Radiologic-Pathologic Correlation

Urologic Imaging of the Kidneys: Cancers and Mimics

Renal cell carcinoma (RCC) is a growing problem in global oncology, with a steadily increasing incidence, especially in developed regions. Its different histologic subtypes present different challenges in diagnosis and management. Advanced imaging techniques have a crucial role in distinguishing between these subtypes by highlighting unique radiographic features such as exophytic growth patterns, cystic components, and enhancement patterns. Practical suggestions discussed in this review include using chemical shift MRI to differentiate fat-poor angiomyolipomas from RCC, recognizing specific imaging markers such as pseudo-capsule in papillary RCC, and understanding the implications of negative pixel count in computed tomography scans.

Low-grade oncocytic tumor of the kidney: imaging features of a novel tumor entity

PURPOSES

Low-grade oncocytic tumor (LOT) is a rare renal tumor that has emerged from the spectrum of eosinophilic/oncocytic renal tumors and poses a diagnostic challenge due to its similarity to chromophobe renal cell carcinoma (CHRCC) and renal oncocytoma (RO). The imaging features of this novel tumor entity have not yet been clearly described. The purpose of this study was to describe the imaging features of LOT with radiologic-pathologic correlation.

METHODS

We conducted a retrospective observational study involving two expert centers. We identified 12 pathologically proven LOT with preoperative imaging available, including at least computed tomography (CT) or magnetic resonance imaging (MRI), from the past 12 years. Three experienced radiologists performed the imaging analysis independently.

RESULTS

All tumors presented well-defined borders. Nine of the 12 LOT exhibited an early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape. Three showed a homogeneous contrast enhancement. Macroscopic fat and calcifications were not observed in any of the tumors.

CONCLUSION

Early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape suggests a LOT diagnosis. Further analyses involving larger studies are needed to fully confirm these imaging characteristics. To date, a percutaneous biopsy should be performed before considering management.

Relevance of Positive Surgical Margins in Localized Renal Cell Carcinoma After Surgical Resection: Predictive Factors and Survival Implications

INTRODUCTION

The implications of positive surgical margins (PSM) after surgery for renal cell carcinoma (RCC) remain subject of discussion. This study aimed to identify risk factors for PSM, assess its effect on overall survival (OS), and determine predictors of OS.

PATIENTS AND METHODS

Data from RCC surgeries at Mannheim University Medical Center between 2010 and 2023 was analyzed. Propensity score matching balanced PSM and control groups using age, surgical approach, tumor stage, histological subtype, and American Association of Anesthesiologists (ASA) score. Logistic and cox regression models predict PSM and OS, respectively. Kaplan-Meier analysis compared OS of PSM patients and controls.

RESULTS

A total of 1066 RCC patients were included. Propensity score matching yielded 32 PSM patients and 96 controls. Multivariable logistic regression identified tumor stage ≥ T3a (odds ratio [OR] = 2.74, 95% confidence interval [CI] = 1.0-6.8, P = .04) and chromophobe, compared to clear cell, RCC (OR = 3.19, 95% CI = 1.0-8.7, P = .03) as independent predictors of PSM. Multivariable cox regression found age > 65 years (hazard ratio [HR] = 2.65, 95% CI = 1.7-4.2, P < .01) and tumor stage ≥ T3a (HR = 2.25, 95% CI = 1.4-3.7, P < .01) to predict shorter OS. Partial vs. radical nephrectomy was associated with improved OS (HR = 0.49, 95% CI = 0.3-0.9, P = .02). Kaplan-Meier analysis revealed no OS difference between PSM patients and controls (P = .49) over a 45-month median follow-up.

CONCLUSION

PSM is not a primary determinant of inferior survival, while age and tumor stage play a more prominent role. A well-calibrated follow-up protocol for PSM patients, combining PSM with coinciding factors such as tumor stage, grade, size, or PSM extent, is crucial for adequate surveillance while preventing excessive interventions.

Low-grade oncocytic tumor: a review of radiologic and clinical features

PURPOSE

The 2022 World Health Organization classification of renal neoplasia expanded the spectrum of oncocytic neoplasms to encompass newly established and emerging entities; one of the latter is the low-grade oncocytic tumor (LOT). This study reports the radiologic appearance and clinical behavior of LOT.

METHODS

In this IRB-approved, HIPPA-compliant retrospective study, our institution's pathology database was searched for low-grade oncocytic tumors or neoplasms. Patient age, gender, and comorbidities were obtained from a review of electronic medical records, and imaging characteristics of the tumors were assessed through an imaging platform.

RESULTS

The pathology database search yielded 14 tumors in 14 patients. Four patients were excluded, as radiologic images were not available in three, and one did not fulfill diagnostic criteria after pathology re-review. The resulting cohort consisted of 10 tumors (median diameter 2.3 cm, range 0.7-5.1) in 10 patients (median age 68 years, range 53-91, six women). All tumors presented as a solitary, well-circumscribed, mass with solid components. All enhanced as much or almost as much as adjacent renal parenchyma; all but one enhanced heterogeneously. None had lymphadenopathy, venous invasion, or metastatic disease at presentation or at clinical follow-up (median, 22.2 months, range 3.4-71.6). Among five tumors undergoing active surveillance, mean increase in size was 0.4 cm/year at imaging follow-up (median 16.7 months, range 8.9-25.4).

CONCLUSION

LOT, a recently described pathologic entity in the kidney, can be considered in the differential diagnosis of an avidly and typically heterogeneously enhancing solid renal mass in an adult patient.

Combined Clear Cell Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma: A Case Report

Renal cell carcinoma (RCC) is a diverse array of cancers arising from renal tubular epithelial cells. RCC presenting with distinct morphological subtypes, such as the simultaneous presence of chromophobe RCC (chRCC) and clear cell RCC (ccRCC) lesions within the same kidney, is rare. We present the case of a 79-year-old female with a history of breast cancer who presented to our facility with right flank pain. Further investigations using CT of the abdomen and pelvis revealed a Bosniak type 4 cyst with a mural nodule in the right kidney. Furthermore, another well-defined, solid lesion measuring 2.8 × 2.6 cm was observed in the same area. The patient underwent a right radical nephrectomy. The macroscopic examination of the kidney revealed the presence of three cysts, with the largest measuring up to 7.5 cm. Moreover, a distinctly demarcated, golden-yellow, solid mass was discerned in the superior pole of the kidney. The mass showed a heterogeneous cut surface with solid and cystic components, measuring 2.8 × 2.6 × 2.0 cm. A less extensive but well-defined, uniform tan mass was also identified within the wall of the largest cyst, which measured 1.2 × 1.0 × 0.7 cm. At this point, the diagnosis of ccRCC and chRCC was established.

Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study

BACKGROUND

Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension.

PURPOSE

To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs.

STUDY TYPE

Retrospective.

POPULATION

797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses.

FIELD STRENGTH/SEQUENCES

3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging.

ASSESSMENT

Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions.

STATISTICAL TESTS

Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05.

RESULTS

The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993).

CONCLUSION

The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Radiologic and pathologic correlation of a renal venous hemangioma

Renal hemangiomas, including the rare subtype of venous hemangioma, are typically non-cancerous, often asymptomatic, and usually discovered incidentally during imaging studies. Here, we report a unique case of a 59-year-old African-American female with a renal venous hemangioma that initially mimicked papillary-type renal cell carcinoma (RCC-pt) on imaging studies. The patient's presentation included a long history of rectal bleeding and an incidental discovery of a hypoattenuating mass in the left kidney during a contrast-enhanced CT scan. Renal MRI revealed a 3.5 cm left renal lower pole mass, presenting as heterogeneously hyperintense on T1-weighted images and hypointense on T2-weighted images, with gradual mild enhancement post-contrast. Subsequent total nephrectomy confirmed the histopathological diagnosis of a venous hemangioma. This case underlines the need for recognizing unique imaging features of renal venous hemangiomas, contributing to the differential diagnosis of T2 dark hypoenhancing renal masses. Correct interpretation may prevent unnecessary invasive procedures and operations, thereby improving patient management and outcomes.

Exploratory Analysis of the Role of Radiomic Features in the Differentiation of Oncocytoma and Chromophobe RCC in the Nephrographic CT Phase

In diagnostic imaging, distinguishing chromophobe renal cell carcinomas (chRCCs) from renal oncocytomas (ROs) is challenging, since they both present similar radiological characteristics. Radiomics has the potential to help in the differentiation between chRCCs and ROs by extracting quantitative imaging. This is a preliminary study of the role of radiomic features in the differentiation of chRCCs and ROs using machine learning models. In this retrospective work, 38 subjects were involved: 19 diagnosed with chRCCs and 19 with ROs. The CT nephrographic contrast phase was selected in each case. Three-dimensional segmentations of the lesions were performed and the radiomic features were extracted. To assess the reliability of the features, the intraclass correlation coefficient was calculated from the segmentations performed by three radiologists with different degrees of expertise. The selection of features was based on the criteria of excellent intraclass correlation coefficient (ICC), high correlation, and statistical significance. Three machine learning models were elaborated: support vector machine (SVM), random forest (RF), and logistic regression (LR). From 105 extracted features, 41 presented an excellent ICC and 6 were not highly correlated with each other. Only two features showed significant differences according to histological type and machine learning models were developed with them. LR was the better model, in particular, with an 83% precision.

Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

The increasing evidence of oncocytic renal tumors positive in ^99mTc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of ^99mTc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of ^99mTc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of ^99mTc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and ^99mTc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that ^99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with ^99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of ^99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of ^99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.

Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.

Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature

Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as ¹⁸F-fluorodeoxyglucose (FDG), ¹²⁴I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and ¹¹C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, ⁹⁹Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.

Positive surgical margin's impact on short-term oncological prognosis after robot-assisted partial nephrectomy (MARGINS study: UroCCR no 96)

The oncological impact of positive surgical margins (PSM) after robot-assisted partial nephrectomy (RAPN) is still under debate. We compared PSM and Negative Surgical Margins (NSM) in terms of recurrence-free survival (RFS), metastasis-free survival (MFS) and overall survival (OS) after RAPN, and we identified predictive factors of PSM. Multi-institutional study using the UroCCR database, which prospectively included 2166 RAPN between April 2010 and February 2021 (CNIL DR 2013-206; NCT03293563). Two groups were retrospectively compared: PSM versus NSM. Prognostic factors were assessed using Kaplan-Meyer curves with log-Rank test, cox hazard proportional risk model and logistic regression after univariate comparison. 136 patients had PSM (6.3%) and 2030 (93.7%) had NSM. During a median follow-up of 19 (9-36) months after RAPN, 160 (7.4%) recurrences were reported. Kaplan-Meier curves and analysis suggested that RFS, MFS and OS were not affected by a PSM (p = 0.68; 0.71; 0.88, respectively). In multivariate analysis predictors of PSM were a lower RENAL score (p = 0.001), longer warm ischemia time (WIT) (p = 0.003) and Chromophobe Renal Cell Carcinoma (chrRCC) (p = 0.043). This study found no impact of PSM on RFS, MFS or OS, and predictors of PSM were the RENAL score, WIT and chrRCC.