MR classification of renal masses with pathologic correlation

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BACKGROUND

The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.

METHODS

We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis.

RESULTS

Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.

CONCLUSIONS

The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Prediction of Fuhrman grade of renal clear cell carcinoma by multimodal MRI radiomics: a retrospective study

AIM

To explore the value of multimodal magnetic resonance imaging (MRI) radiomics combined with traditional radiologist-defined semantic characteristics and conventional (cMRI) and functional MRI (fMRI) texture features in predicting Fuhrman grade of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

The data of 89 patients with histopathologically proven ccRCC (low-grade, 54; high-grade, 35) were collected. Texture features were extracted from cMRI (T1- and T2-weighted imaging) and fMRI (Dixon-MRI; blood-oxygen-level dependent [BOLD]-MRI; and susceptibility-weighted imaging [SWI]) images, and the traditional characteristics (TC) were evaluated. Logistic regression analysis was performed to develop models based on TC, cMRI, and fMRI texture features for grading. Receiver operating characteristic (ROC) curve analysis and leave-group-out cross-validation (LGOCV) were performed to test the reliability of combined models.

RESULTS

Two T2-weighted imaging-based, two Dixon_W-based, one Dixon_F-based, one BOLD-based, and three SWI-based texture features, and three TC were extracted for feature selection. TC, cMRI, fMRI, cMRI+fMRI, cMRI+TC, fMRI+TC, and cMRI+fMRI+TC models were constructed. The AUC of the cMRI+fMRI+TC model for differentiating high- from low-grade ccRCC was 0.74, with 81.42% accuracy, 75.93% sensitivity, and 91.43% specificity. The fMRI+TC model exhibited a performance similar to that of the cMRI+fMRI+TC model (p>0.05). The areas under the curve (AUCs) of the fMRI+TC and cMRI+fMRI+TC models were significantly higher than those of the other five models (all p<0.05). For the cMRI+fMRI+TC model, the mean accuracy was 85.40% after 100 LGOCV for the test sets.

CONCLUSION

Multimodal MRI radiomics combined with TC, cMRI, and fMRI texture features may be a reliable quantitative approach for differentiating high-grade ccRCC from low-grade ccRCC.

MRI for the detection of small malignant renal masses: a systematic review and meta-analysis

Objective

We aimed to review the available evidence on the diagnostic performance of magnetic resonance imaging in differentiating malignant from benign small renal masses.

Methods

An electronic literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar was performed to identify relevant articles up to 31 January 2023. We included studies that reported the diagnostic accuracy of using magnetic resonance imaging to differentiate small (≤4 cm) malignant from benign renal masses. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the bivariate model and the hierarchical summary receiver operating characteristic model. The study quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

A total of 10 studies with 860 small renal masses (815 patients) were included in the current meta-analysis. The pooled sensitivity and specificity of the studies for the detection of malignant masses were 0.85 (95% CI 0.79-0.90) and 0.83 (95% CI 0.67-0.92), respectively.

Conclusions

MRI had a moderate diagnostic performance in differentiating small malignant renal masses from benign ones. Substantial heterogeneity was observed between studies for both sensitivity and specificity.

Multimodality imaging of Xp11.2 translocation/TFE3 gene fusion associated with renal cell carcinoma: a case report

Background

Xp11.2 translocation/TFE3 gene fusion associated with renal cell carcinoma (Xp11.2 RCC) exhibits unique biological characteristics and is associated with an increased incidence of tumor thrombosis, lymph node metastasis, and advanced disease stages. Multimodality imaging, including US, contrast-enhanced CT, multi-parametric MRI, and 18F-FDG PET/CT plays a crucial role in the preoperative diagnosis and differentiation of renal tumors.

Case report

A 15-year-old female presented with lumbar pain worsened, and developed persistent painless hematuria. The CT attenuation values of the scan without contrast, corticomedullary phase, nephrographic phase, and delayed phases were 35 HU, 83 HU, 82 HU, and 75 HU, respectively. The solid component of the mass displayed heterogeneous marked enhancement. Furthermore, MRU indicated that the lesion involved the cortical medulla and infringed on the renal sinus fat. The lesion appeared isosignal in T1WI, slightly low signal in T2WI, and slightly high signal in DWI. The degree of enhancement in the three phases of enhancement scan was lower than that in the renal parenchyma, and hemorrhage and necrosis were observed within the internal part of the lesion. To further clarify the staging, the patient underwent 18F-FDG PET/CT. PET/CT images showed multiple irregular occupancies in the right kidney with unclear borders, showing a heterogeneous increase in 18F-FDG uptake, with SUVmax values ranging from 2.3 to 5.2 in the routine imaging phase (60 min post-injection), compared to SUVmax values ranging from 2.8 to 6.9 in the delayed imaging phase (160 min post-injection). Additionally, multiple enlarged and fused lymph nodes were observed in the medial part of the right kidney and the retroperitoneum, exhibiting a heterogeneous increase in 18F-FDG uptake, with SUVmax values ranging from 4.1 to 8.7 in the routine imaging phase, compared to SUVmax values ranging from 4.4 to 9.1 in the delayed imaging phase. The postoperative pathology, immunohistochemistry, and molecular analysis of histiocytes were consistent with a diagnosis of Xp11.2 RCC. One month after surgery, enhanced-CT examination of the patient revealed lung metastasis, peritoneal metastasis, and multiple lymph node metastases throughout the body, with an overall survival of 16 months.

Conclusion

Xp11.2 RCC exhibits unique biological characteristics and is associated with an increased incidence of tumor thrombosis, lymph node metastasis, and advanced disease stages. Long-term follow-up is essential to monitor the likelihood of recurrence and metastasis. 18F-FDG PET/CT examination can comprehensively visualize the lesion's location and extent, providing a basis for clinical tumor staging and aiding in treatment monitoring and follow-up. To address the limitations of FDG, the utilization of specific tracers designed for RCC or tracers that are not excreted via the urinary system would be ideal. Further advancements in molecular imaging technologies and the development of novel tracers hold great promise in advancing the diagnosis and management of RCC, ultimately contributing to better patient outcomes and overall disease management.

Risk of malignancy in T1-hyperintense Bosniak version 2019 class II and IIF cystic renal masses

BACKGROUND

Bosniak classification version 2019 includes cystic masses in class II and IIF based partly on their hyperintense appearance at T1-weighted MRI. The prevalence of malignancy in non-enhancing heterogeneously T1-hyperintense masses is unknown, nor whether the pattern of T1 hyperintensity affects malignancy likelihood.

PURPOSE

To determine the malignancy proportion among six patterns of T1 hyperintensity within non-enhancing cystic renal masses.

METHODS

This retrospective, single-institution study included 72 Bosniak class II and IIF, non-enhancing, T1-hyperintense cystic renal masses. Diagnosis was confirmed by histopathology or by follow-up imaging demonstrating 5-year size and morphologic stability, decreased in size by ≥ 30%, resolution, or Bosniak down-classification. Six patterns of T1 hyperintensity were pre-defined: homogeneous (pattern A), fluid-fluid level (pattern B), peripherally markedly T1-hyperintense (pattern C), containing a T1-hyperintense non-enhancing nodule (pattern D), peripherally T1-hypointense (pattern E), and heterogeneously T1-hyperintense without a distinct pattern (pattern F). Three readers independently assigned each mass to a pattern. Individual and mean malignancy proportion were determined. Mann-Whitney test and Fischer's exact test compared the likelihood of malignancy between patterns. Inter-reader agreement was analyzed with Gwet's agreement coefficient (AC).

RESULTS

Among 72 masses, the mean number of masses assigned was 11 (15%) to pattern A, 21 (29%) to pattern B, 6 (8%) to pattern C, 7 (10%) to pattern D, 5 (7%) to pattern E, and 22 (31%) to pattern F. Five of 72 masses (7%) were malignant; none was assigned pattern A, B, or D. Mean malignancy proportion was 5% (0/9, 1/6, and 0/4) for pattern C, 13% (0/4, 1/3, and 1/7) for pattern E, and 18% (5/20, 3/21, and 4/25) for pattern F. Malignant masses were more likely assigned to pattern E or F (p = 0.003-0.039). Inter-reader agreement was substantial (Gwet's AC: 0.68).

CONCLUSION

Bosniak version 2019 class IIF masses that are non-enhancing and heterogeneously T1-hyperintense with a fluid-fluid level are likely benign. Those that are non-enhancing and heterogeneously T1-hyperintense without a distinct pattern have a malignancy proportion up to 25% (5/20).

Diagnostic performance of multiparametric magnetic resonance imaging in the differentiation of clear cell renal cell cancer

PURPOSE

This study aimed to evaluate the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) in the differentiation of renal cell carcinoma (RCC) subtypes.

METHODS

This is a retrospective diagnostic performance study, in which the diagnostic performances of mpMRI features were evaluated to differentiate clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC). Adult patients who were evaluated using a 3-Tesla dynamic contrast-enhanced mpMRI before undergoing partial or radical nephrectomy for possible malignant renal tumors were included in the study. Signal intensity change percentages (SICP) between contrast-enhanced phases and pre-administration period for both the tumor and normal renal cortex, and tumor-to-cortex enhancement index (TCEI); tumor apparent diffusion coefficient (ADC) values; tumor-to-cortex ADC ratio; and a scale which was developed according to the tumor signal intensities on the axial fat-suppressed T2-weighted Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images were used in ROC analysis to estimate the presence of ccRCC in the patients. The reference test positivity was the histopathologic examination of the surgical specimens.

RESULTS

Ninety-eight tumors from 91 patients were included in the study, and 59 of them were ccRCC, 29 were pRCC, and 10 were chRCC. The mpMRI features that had the three highest sensitivity rates were excretory phase SICP, T2-weighted HASTE scale score, and corticomedullary phase TCEI (93.2%, 91.5%, and 86.4%, respectively). However, those with the three highest specificity rates were nephrographic phase TCEI, excretory phase TCEI, and tumor ADC value (94.9%, 94.9%, and 89.7%, respectively).

CONCLUSION

Several parameters on mpMRI showed an acceptable performance to differentiate ccRCC from non-ccRCC.

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

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.

Multiparametric magnetic resonance imaging for characterizing renal tumors: A validation study of the algorithm presented by Cornelis et al

Objectives

In the last decade, the incidence of renal cell carcinoma (RCC) has been rising, with the greatest increase observed for solid tumors. Magnetic resonance imaging (MRI) protocols and algorithms have recently been available for classifying RCC subtypes and benign subtypes. The objective of this study was to prospectively validate the MRI algorithm presented by Cornelis et al. for RCC classification.

Material and Methods

Over a 7-month period, 38 patients with 44 renal tumors were prospectively included in the study and received an MRI examination in addition to the conventional investigation program. The MRI sequences were: T2-weighted, dual chemical shift MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced T1-weighted in wash-in and wash-out phases. The images were evaluated according to the algorithm by two experienced, blinded radiologists, and the histopathological diagnosis served as the gold standard.

Results

Of 44 tumors in 38 patients, only 8 tumors (18.2%) received the same MRI diagnosis according to the algorithm as the histopathological diagnosis. MRI diagnosed 16 angiomyolipoma, 14 clear cell RCC (ccRCC), 12 chromophobe RCC (chRCC), and two papillary RCC (pRCC), while histopathological examination diagnosed 24 ccRCC, four pRCC, one chRCC, and one mixed tumor of both pRCC and chRCC. Malignant tumors were statistically significantly larger than the benign (3.16 ± 1.34 cm vs. 2.00 ± 1.04 cm, P = 0.006).

Conclusion

This prospective study could not reproduce Cornelis et al.'s results and does not support differentiating renal masses using multiparametric MRI without percutaneous biopsy in the future. The MRI algorithm showed few promising results to categorize renal tumors, indicating histopathology for clinical decisions and follow-up regimes of renal masses are still required.

Role of non-contrast magnetic resonance imaging in pre-surgical evaluation of renal masses in renal impairment patients

Background

The aim of this work is to study the role of non-contrast MRI in pre-surgical evaluation of renal masses in renal impairment patients as confirmed by both intraoperative and histopathological findings. Intraoperative and histopathological findings were correlated with radiological data.

Methods

This prospective study included 20 patients comprising 25 renal masses. The data were collected in a period from April 2018 to September 2019. All patients underwent partial or radical nephrectomy by the same surgeon.

Results

Based on MRI findings, 9 masses (36%) and 8 masses (32%) were found to be associated with collecting system invasion and perinephric fat invasion, respectively. Histopathological assessment confirmed only 6 cases (24%) with collecting system invasion and 7 cases (28%) demonstrated perinephric fat. Seven masses (28%) had intralesional hemorrhage detected by MRI and confirmed by pathological findings. The MRI detected 6 cases (24%) with lymph nodes invasion, while the histopathological assessment confirmed lymphatic invasion in 7 cases (28%). Only 2 cases (8%) had vascular invasion detected by preoperative MRI and confirmed by histopathology and surgery. The final histopathological examination confirmed 20 malignant neoplasms (80%: RCC = 18, leiomyosarcoma = 2), 3 benign neoplasms (12%: angiomyolipoma = 1, oncocytoma = 2) and 2 non-neoplastic benign masses (8%: renal abscess = 1, xanthogranulomatous pyelonephritis = 1).

Conclusion

Non-contrast MRI is a crucial imaging tool in renal impairment patients who cannot be examined with contrast-enhanced CT or MRI. It assesses the extent of the renal sinus fat and the perinephric fat invasion.

Cross-sectional imaging assessment of renal masses with emphasis on MRI

Magnetic resonance imaging (MRI) is a useful complementary imaging tool for the diagnosis and characterization of renal masses, as it provides both morphologic and functional information. A core MRI protocol for renal imaging should include a T1-weighted sequence with in- and opposed-phase images (or, alternatively with DIXON technique), T2-weighted and diffusion-weighted images as well as a dynamic contrast-enhanced sequence with subtraction images, followed by a delayed post-contrast T1-weighted sequence. The main advantages of MRI over computed tomography include increased sensitivity for contrast enhancement, less sensitivity for detection of calcifications, absence of pseudoenhancement, and lack of radiation exposure. MRI may be applied for renal cystic lesion characterization, differentiation of renal cell carcinoma (RCC) from benign solid renal tumors, RCC histologic grading, staging, post-treatment follow-up, and active surveillance of patients with treated or untreated RCC.

MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier

Objective

To develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures.

Materials and Methods

We retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set.

Results

The ICCs of 257 texture features were equal to or higher than 0.80 (0.828–0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively.

Conclusions

A machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.

Clinicopathological and radiological significance of the collateral vessels of renal cell carcinoma on preoperative computed tomography

This study aimed to investigate the clinicopathological and radiological significance of the collateral vessel of renal cell carcinoma (RCC) on preoperative computed tomography (CT). Preoperative contrast-enhanced CT of 236 consecutive patients with pathological documented RCC were retrospectively reviewed during the period of 2014. The associations of the presence of collateral vessels with perioperative clinicopathological and radiological features, as well as long term survival outcomes were analyzed. Totally, collateral vessels were detected by contrast-enhanced CT in 110 of 236 patients. The presence of collateral vessels was significantly associated with higher pathologic T stage, higher Fuhrman grade, higher overall RENAL scores, greater tumor size and enhancement, and more tumor necrosis (all P < 0.05). In patients with clear cell RCC, those harboring collateral vessels had significantly higher SSIGN scores (P < 0.001) and shorter overall survival (P = 0.01) than those without collateral vessel. The incidence of intraoperative blood loss, blood transfusion, radical nephrectomy (RN) and open surgery were also significantly higher in patients with collateral vessels (all P < 0.05). In multivariate analysis, the presence of collateral vessels was significantly associated with RN (P = 0.021) and open surgery (P = 0.012). The presence of collateral vessels was significantly associated with aggressive clinicopathological parameters and worse prognosis. It is worth paying attention to its association with the choice of RN and open surgery in clinical practice.

Characterizing T2 iso- and hypo-intense renal masses on MRI: Can templated algorithms improve accuracy?

PURPOSE

To assess if a templated algorithm can improve the diagnostic performance of MRI for characterization of T2 isointense and hypointense renal masses.

METHODS

In this retrospective study, 60 renal masses with histopathologic diagnoses that were also confirmed as T2 iso- or hypointense on MRI were identified (mean ± standard deviation, range: 3.9 ± 2.5, 1.0-13.7 cm). Two semi-quantitative diagnostic algorithms were created based on MRI features of renal masses reported in the literature. Three body-MRI trained radiologists provided clinical diagnoses based on their experience and separately provided semiquantitative data for each components of the two algorithms. The algorithms were applied separately by a radiology trainee without additional interpretive input. Logistic regression was used to compare the accuracy of the three methods in distinguishing malignant versus benign lesions and in diagnosing the exact histopathology. Inter-reader agreement for each method was calculated using Fleiss' kappa statistics.

RESULTS

The accuracy of the two algorithms and clinical experience were similar (70%, 69%, and 64%, respectively, p = 0.22-0.32), with fair to moderate inter-reader agreement (Fleiss's kappa: r = 0.375, r = 0.308, r = 0.375, respectively, all p < 0.0001). The accuracy of the two algorithms and clinical experience in diagnosing specific histopathology were also no different from each other (34%, 29%, and 32%, respectively, p = 0.49-0.74), with fair to moderate inter-reader agreement (Fleiss's kappa: r = 0.20, r = 0.28, r = 0.375, respectively, all p < 0.0001).

CONCLUSION

Semi-quantitative templated algorithms based on MRI features of renal masses did not improve the ability to diagnose T2 iso- and hypointense renal masses when compared to unassisted interpretation by body MR trained subspecialists.

Advanced Fusion Imaging and Contrast-Enhanced Imaging (CT/MRI-CEUS) in Oncology

Fusion imaging depicts an innovative technique that facilitates combining assets and reducing restrictions of advanced ultrasound and cross-sectional imaging. The purpose of the present retrospective study was to evaluate the role of fusion imaging for assessing hepatic and renal lesions. Between 02/2011-08/2020, 92 patients in total were included in the study, of which 32 patients had hepatic lesions, 60 patients had renal lesions. Fusion imaging was technically successful in all patients. No adverse side effects upon intravenous (i.v.) application of SonoVue® (Bracco, Milan, Italy) were registered. Fusion imaging could clarify all 11 (100%) initially as indeterminate described hepatic lesions by computed tomography/magnetic resonance imaging (CT/MRI). Moreover, 5/14 (36%) initially suspicious hepatic lesions could be validated by fusion imaging, whereas in 8/14 (57%), malignant morphology was disproved. Moreover, fusion imaging allowed for the clarification of 29/30 (97%) renal lesions initially characterized as suspicious by CT/MRI, of which 19/30 (63%) underwent renal surgery, histopathology revealed malignancy in 16/19 (84%), and benignity in 3/19 (16%). Indeterminate findings could be elucidated by fusion imaging in 20/20 (100%) renal lesions. Its accessibility and repeatability, even during pregnancy and in childhood, its cost-effectiveness, and its excellent safety profile, make fusion imaging a promising instrument for the thorough evaluation of hepatic and renal lesions in the future.

Protocol Optimization for Renal Mass Detection and Characterization

Renal masses increasingly are found incidentally, largely due to the frequent use of medical imaging. Computed tomography (CT) and MR imaging are mainstays for renal mass characterization, presurgical planning of renal tumors, and surveillance after surgery or systemic therapy for advanced renal cell carcinomas. CT protocols should be tailored to different clinical indications, balancing diagnostic accuracy and radiation exposure. MR imaging protocols should take advantage of the improved soft tissue contrast for renal tumor diagnosis and staging. Optimized imaging protocols enable analysis of imaging features that help narrow the differential diagnoses and guide management in patients with renal masses.

Contrast-Enhanced Ultrasound (CEUS) for Follow-Up of Bosniak 2F Complex Renal Cystic Lesions-A 12-Year Retrospective Study in a Specialized European Center

Bosniak 2F renal cystic lesions feature morphologic characteristics between Bosniak I and III categories, the majority of which remain benign. However, a minor part of Bosniak 2F lesions may progress to malignancy. The purpose of this study was to assess Bosniak 2F cystic lesions during follow-up examinations by CEUS. One-hundred-and-twelve out of 364 patients with Bosniak 2F lesions underwent follow-up CEUS examinations between February 2008 and February 2020. Twelve out of 364 patients underwent renal surgery without follow-up CEUS. The progression rate of Bosniak 2F renal lesions detected by CEUS accounted for 7.1% (8/112 patients) after a mean of 12.9 months. The first follow-up CEUS revealed 75% of progressions (6/8), the remaining 25% (2/8) of progressions were detected during second follow-up CEUS. Underlying clear-cell renal cell carcinoma was histopathologically validated in 5/8 progressive complex cystic renal lesions. Stable sonomorphologic features were observed in 92.1% (104/112 patients). CEUS depicts a promising diagnostic imaging modality in the diagnostic work-up and follow-up of complex renal cystic lesions at higher spatial and temporal resolutions than CT or MRI. Its excellent safety profile, its easy and repeatable accessibility, and low financial costs render CEUS an attractive and powerful alternative imaging tool for monitoring complex renal cystic lesions.

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

BACKGROUND

Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC).

PURPOSE

To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC.

STUDY TYPE

Retrospective.

POPULATION

In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned.

FIELD STRENGTH/SEQUENCE

Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T₂ WI, contrast-enhanced T₁ WI, and diffusion weighted imaging.

ASSESSMENT

Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics.

RESULTS

The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05).

DATA CONCLUSION

Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

OBJECTIVE. The objective of our study was to preoperatively predict fat-poor angiomyolipoma (fp-AML) and renal cell carcinoma (RCC) by conducting quantitative analysis of contrast-enhanced CT images. MATERIALS AND METHODS. One hundred fifteen patients with a pathologic diagnosis of fp-AML or RCC from a single institution were randomly allocated into a train set (tumor size: mean ± SD, 4.50 ± 2.62 cm) and test set (tumor size: 4.32 ± 2.73 cm) after data augmentation. High-dimensional histogram-based features, texture-based features, and Laws features were first extracted from CT images and were then combined as different combinations sets to construct a logistic prediction model based on the least absolute shrinkage and selection operator procedure for the prediction of fp-AML and RCC. Prediction performances were assessed by classification accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, true-positive rate, and false-positive rate (FPR). In addition, we also investigated the effects of different gray-scales of quantitative features on prediction performances. RESULTS. The following combination sets of features achieved satisfying performances in the test set: histogram-based features (mean AUC = 0.8492, mean classification accuracy = 91.01%); histogram-based features and texture-based features (mean AUC = 0.9244, mean classification accuracy = 91.81%); histogram-based features and Laws features (mean AUC = 0.8546, mean classification accuracy = 88.76%); and histogram-based features, texture-based features, and Laws features (mean AUC = 0.8925, mean classification accuracy = 90.36%). The different quantitative gray-scales did not have an obvious effect on prediction performances. CONCLUSION. The integration of histogram-based features with texture-based features and Laws features provided a potential biomarker for the preoperative diagnosis of fp-AML and RCC. The accurate diagnosis of benign or malignant renal masses would help to make the clinical decision for radical surgery or close follow-up.

Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging

INTRODUCTION

Detection of small renal masses (SRM) is increasing with the use of cross-sectional imaging, although many incidental lesions have negligible metastatic potential. A method to identify this subtype would aid in risk stratification. A previously reported clear cell likelihood score (ccLS; 1-very unlikely, 2-unlikely, 3-equivocal, 4-likely, and 5-very likely), based on retrospective review of multiparametric magnetic resonance imaging (mpMRI), predicted the likelihood of encountering clear cell renal cell carcinoma (ccRCC) at surgery. Here, we assess the performance of ccLS prospectively assigned for prediction of ccRCC.

METHODS

Patients with a known renal mass who underwent mpMRI at a single institution between June 2016 and April 2018 were prospectively assigned a ccLS as part of the clinical MRI report. These patients were retrospectively reviewed, and those with a cT1a lesion and available pathological tissue diagnosis (diagnostic biopsy or extirpative surgery) were selected for analysis.

RESULTS

In total, 57 patients (mean age 61.7 ± 14.9 years) with 63 cT1a renal masses were identified. Mean tumor size was 2.7 ± 0.7 cm. Defining ccLS 4-5 lesions as positive demonstrated an overall accuracy of 84%, sensitivity of 89%, specificity of 79%, positive predictive value of 84%, and negative predictive value of 86%. A ccLS of 1-2 demonstrates an 86% accuracy and 100% sensitivity/positive predictive value of identifying non-ccRCC histology.

CONCLUSIONS

Utilizing prospectively assigned ccLS, we confirm that mpMRI can reasonably identify ccRCC histology in cT1a renal masses. Standardization of imaging protocols and reporting criteria such as the ccLS can be used to aid in the diagnosis and management of small renal masses.

Can MRI be used to diagnose histologic grade in T1a (< 4 cm) clear cell renal cell carcinomas?

OBJECTIVE

To assess whether MRI can differentiate low-grade from high-grade T1a cc-RCC.

MATERIALS AND METHODS

With IRB approval, 49 consecutive solid < 4 cm cc-RCC (low grade [Grade 1 or 2] N = 38, high grade [Grade 3] N = 11) with pre-operative MRI before nephrectomy were identified between 2013 and 2018. Tumor size, apparent diffusion coefficient (ADC) histogram analysis, enhancement wash-in and wash-out rates, and chemical shift signal intensity index (SI index) were assessed by a blinded radiologist. Subjectively, two blinded Radiologists also assessed for (1) microscopic fat, (2) homogeneity (5-point Likert scale), and (3) ADC signal (relative to renal cortex); discrepancies were resolved by consensus. Outcomes were studied using Chi square, multivariate analysis, logistic regression modeling, and ROC. Inter-observer agreement was assessed using Cohen's kappa.

RESULTS

Tumor size was 24 ± 7 (13-39) mm with no association to grade (p = 0.45). Among quantitative features studied, corticomedullary phase wash-in index (p = 0.015), SI index (p = 0.137), and tenth-centile ADC (p = 0.049) were higher in low-grade tumors. 36.8% (14/38) low-grade tumors versus zero high-grade tumors demonstrated microscopic fat (p = 0.015; Kappa = 0.67). Microscopic fat was specific for low-grade disease (100.0% [71.5-100.0]) with low sensitivity (36.8% [21.8-54.6]). Other subjective features did not differ between groups (p > 0.05). A logistic regression model combining microscopic fat + wash-in index + tenth-centile-ADC yielded area under ROC curve 0.98 (Confidence Intervals 0.94-1.0) with sensitivity/specificity 87.5%/100%.

CONCLUSION

The combination of microscopic fat, higher corticomedullary phase wash-in and higher tenth-centile ADC is highly accurate for diagnosis of low-grade disease among T1a clear cell RCC.

Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment

Cystic renal cell carcinoma (RCC) is almost certainly overdiagnosed and overtreated. Efforts to diagnose and treat RCC at a curable stage result in many benign neoplasms and indolent cancers being resected without clear benefit. This is especially true for cystic masses, which compared with solid masses are more likely to be benign and, when malignant, less aggressive. For more than 30 years, the Bosniak classification has been used to stratify the risk of malignancy in cystic renal masses. Although it is widely used and still effective, the classification does not formally incorporate masses identified at MRI or US or masses that are incompletely characterized but are highly likely to be benign, and it is affected by interreader variability and variable reported malignancy rates. The Bosniak classification system cannot fully differentiate aggressive from indolent cancers and results in many benign masses being resected. This proposed update to the Bosniak classification addresses some of these shortcomings. The primary modifications incorporate MRI, establish definitions for previously vague imaging terms, and enable a greater proportion of masses to enter lower-risk classes. Although the update will require validation, it aims to expand the number of cystic masses to which the Bosniak classification can be applied while improving its precision and accuracy for the likelihood of cancer in each class.

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.

Personalized Treatment for Small Renal Tumors: Decision Analysis of Competing Causes of Mortality

Purpose To compare the effectiveness of personalized treatment for small (≤4 cm) renal tumors versus routine partial nephrectomy (PN), accounting for various competing causes of mortality. Materials and Methods A state-transition microsimulation model was constructed to compare life expectancy of management strategies for small renal tumors by using 1 000 000 simulations in the following ways: routine PN or personalized treatment involving percutaneous ablation for risk factors for worsening chronic kidney disease (CKD), and otherwise PN; biopsy, with triage of renal cell carcinoma (RCC) to PN or ablation depending on risk factors for worsening CKD; active surveillance for growth; and active surveillance when MRI findings are indicative of papillary RCC. Transition probabilities were incorporated from the literature. Effects of parameter variability were assessed in sensitivity analysis. Results In patients of all ages with normal renal function, routine PN yielded the longest life expectancy (eg, 0.67 years in 65-year-old men with nephrometry score [NS] of 4). Otherwise, personalized strategies extended life expectancy versus routine PN: in CKD stages 2 or 3a, moderate or high NS, and no comorbidities, MRI guidance for active surveillance extended life expectancy (eg, 2.60 years for MRI vs PN in CKD 3a, NS 10); and with Charlson comorbidity index of 1 or more, biopsy or active surveillance for growth extended life expectancy (eg, 2.70 years for surveillance for growth in CKD 3a, NS 10). CKD 3b was most effectively managed by using MRI to help predict papillary RCC for surveillance. Conclusion For patients with chronic kidney disease and small renal tumors, personalized treatment selection likely extends life expectancy. © RSNA, 2019 Online supplemental material is available for this article.

Imaging Advances in the Management of Kidney Cancer

New developments in cross-sectional imaging, including contrast-enhanced ultrasound, dual-energy computed tomography, multiparametric magnetic resonance imaging, single-photon emission computed tomography, and positron emission tomography, together with novel application of existing and novel radiotracers, have changed the landscape of renal mass characterization (ie, virtual biopsy) as well as the detection of metastatic disease, prognostication, and response assessment in patients with advanced kidney cancer. A host of imaging response criteria have been developed to characterize the response to targeted and immune therapies and correlate with patient outcomes, each with strengths and limitations. Recent efforts to advance the field are aimed at increasing objectivity with quantitative techniques and the use of banks of imaging data to match the vast genomic data that are becoming available. The emerging field of radiogenomics has the potential to transform further the role of imaging in kidney cancer management through eventual noninvasive characterization of the tumor histology and genetic microenvironment in single renal masses and/or metastatic disease. We review of the effect of currently available imaging techniques in the management of patients with kidney cancer, including localized, locally advanced, and metastatic disease.

Diagnostic Accuracy of MRI for Detection of Papillary Renal Cell Carcinoma: A Systematic Review and Meta-Analysis

OBJECTIVE

The objective of our study was to perform a systematic review and meta-analysis of the diagnostic performance of MRI in differentiation of papillary renal cell carcinoma (RCC) from other renal masses.

MATERIALS AND METHODS

We performed searches of three electronic databases for studies that used MRI techniques to differentiate papillary RCC from other renal lesions. Methodologic quality was assessed, and diagnostic test accuracy was summarized using bivariate random-effects modeling or with construction of a summary ROC (SROC) curve.

RESULTS

Thirteen studies involving 275 papillary RCC lesions and 758 other renal masses met the inclusion criteria. Resulting summary estimates for the performance of MRI to differentiate papillary RCC from other renal lesions were a sensitivity of 79.6% (95% CI, 62.3-90.2%) and specificity of 88.1% (95% CI, 80.1-93.1%). In subgroup analysis, quantitative pooling of seven studies using enhancement in the corticomedullary phase resulted in a sensitivity of 85.6% (95% CI, 67.8-94.4%), specificity of 91.7% (95% CI, 76.0-97.5%), and area under the SROC curve of 0.894. Four studies used tumor appearance on T2-weighted imaging to detect papillary RCC, and results showed a pooled sensitivity of 89.9% (95% CI, 73.0-96.7%) and specificity of 84.9% (95% CI, 69.0-93.4%). Three studies used signal loss on T1-weighted in-phase imaging to detect papillary RCC but marked heterogeneity precluded pooling.

CONCLUSION

Meta-analysis supports moderate sensitivity and excellent specificity of quantitative enhancement in the corticomedullary phase for differentiating papillary RCC from other tumors. The accuracy of combining enhancement and T2 signal-intensity characteristics merits further evaluation as a potential aid for management decisions.

Imaging Protocols for Active Surveillance in Renal Cell Carcinoma

PURPOSE OF REVIEW

To review the growth kinetics of small renal masses and available imaging modalities for mass characterization and surveillance, highlight current organizational recommendations for the active surveillance of small renal masses, and discuss the most recently reported oncological outcomes of patients as they relate to various surveillance imaging protocols and progression to delayed intervention.

RECENT FINDINGS

Overall, organizational guideline recommendations are broad and lack specifics regarding timing and modality for follow-up imaging of small renal masses. Additionally, despite general consensus in the literature about certain criteria to trigger delayed intervention, there exist no formal guidelines. Active surveillance of small renal masses is an acceptable management strategy for patients with prohibitive surgical risk; however, standardized imaging protocols for surveillance are lacking, as are randomized, prospective trials to evaluate the ideal follow-up protocol.

Imaging of Solid Renal Masses

The increase in serendipitous detection of solid renal masses on imaging has not resulted in a reduction in mortality from renal cell carcinoma. Consequently, efforts for improved lesion characterization have been pursued and incorporated into management algorithms for distinguishing clinically significant tumors from those with favorable histology or benign conditions. Although diagnostic imaging strategies have evolved for optimized lesion detection, distinction between benign tumors and both indolent and aggressive malignant neoplasms remain an important diagnostic challenge. Recent advances in cross-sectional imaging have expanded the role of these tests in the noninvasive characterization of solid renal tumors.

[Incidental findings of the kidneys, adrenal glands, adnexa uteri, gastrointestinal tract, mesentery and lymph nodes : Assessment and management recommendations]

CLINICAL ISSUE

Besides the upper abdominal parenchymal organs, the increasing application of cross-sectional imaging has also led to a rising number of incidental findings in the kidneys, adrenal glands, adnexa uteri, the gastrointestinal tract, mesentery and abdominal lymph nodes.

STANDARD RADIOLOGICAL METHODS

Abdominal computed tomography investigations often show unexpected findings without any correlating symptoms. The growing clinical relevance is due to the large number of incidental findings as well as an increasing awareness of ethical and socioeconomic factors.

ACHIEVEMENTS

When interpreting radiological findings not only morphological criteria but also individual risk factors of the patient and the clinical context are of great importance.

PRACTICAL RECOMMENDATIONS

The aims of this article are the description and evaluation of frequent incidental findings detected by computed tomography and to provide information about management recommendations.

Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development

Diagnosis of renal cell carcinomas (RCC) subtypes on computed tomography (CT) and magnetic resonance imaging (MRI) is clinically important. There is increased evidence that confident imaging diagnosis is now possible while standardization of the protocols is still required. Fat-poor angiomyolipoma show homogeneously increased unenhanced attenuation, homogeneously low signal on T2-weighted MRI and apparent diffusion coefficient (ADC) map, may contain microscopic fat and are classically avidly enhancing. Papillary RCC are also typically hyperattenuating and of low signal on T2-weighted MRI and ADC map; however, their gradual progressive enhancement after intravenous administration of contrast material is a differentiating feature. Clear cell RCC are avidly enhancing and may show intracellular lipid; however, these tumors are heterogeneous and are of characteristically increased signal on T2-weighted MRI. Oncocytomas and chromophobe tumors (collectively oncocytic neoplasms) show intermediate imaging findings on CT and MRI and are the most difficult subtype to characterize accurately; however, both show intermediately increased signal on T2-weighted with more gradual enhancement compared to clear cell RCC. Chromophobe tumors tend to be more homogeneous compared to oncocytomas, which can be heterogeneous, but other described features (e.g. scar, segmental enhancement inversion) overlap considerably between tumors. Tumor grade is another important consideration in small solid renal masses with emerging studies on both CT and MRI suggesting that high grade tumors may be separated from lower grade disease based upon imaging features.

Novel imaging in renal cell carcinoma

PURPOSE OF REVIEW

Renal cell carcinoma is a heterogeneous disease with a spectrum of subtypes and clinical behavior. Quantitative and qualitative imaging biomarkers are sought to correlate with genetic and histologic features and complement pathologic analysis.

RECENT FINDINGS

Texture analysis, radiogenomics, and modality-specific advancements have yielded an array of renal cell carcinoma imaging biomarkers in the research domain. Although many techniques are promising, standardization and validation of these procedures are needed prior to implementation into clinical practice.

SUMMARY

We introduce novel imaging techniques and analytic methods which have been shown to contribute to characterization of renal cell carcinoma and its subtypes, aggressiveness, and responsiveness to therapy, including associated advantages and limitations.

Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology

Purpose To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods This single-center retrospective HIPAA-compliant institutional review board-approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P = .003; CE OR, 4.45; 95% CI: 1.8, 10.8; P < .001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P < .001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%-67%; specificity range, 97%-99%), with fair to moderate interreader agreement (κ range = 0.23-0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P = .049), with moderate interreader agreement (κ = 0.49). Conclusion The proposed standardized MR imaging-based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists. ^© RSNA, 2018 Online supplemental material is available for this article.

Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis

OBJECTIVES

To determine the performance of chemical shift signal intensity index (CS-SII) values for distinguishing minimal-fat renal angiomyolipoma (mfAML) from renal cell carcinoma (RCC) and to assess RCC subtype characterisation.

METHODS

We identified eligible studies on CS magnetic resonance imaging (CS-MRI) of focal renal lesions via PubMed, Embase, and the Cochrane Library. CS-SII values were extracted by lesion type and evaluated using linear mixed model-based meta-regression. RCC subtypes were analysed. Two-sided p value <0.05 indicated statistical significance. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.

RESULTS

Eleven articles involving 850 patients were included. Minimal-fat AML had significantly higher CS-SII value than RCC (p < 0.05); there were no significant differences between mfAML and clear cell RCC (cc-RCC) (p = 0.112). Clear cell RCC had a significantly higher CS-SII value than papillary RCC (p-RCC) (p < 0.001) and chromophobe RCC (ch-RCC) (p = 0.045). The methodological quality was relatively high, and Begg's test data points indicated no obvious publication bias.

CONCLUSIONS

The CS-SII value for differentiating mfAML from cc-RCC remains unproven, but is a promising method for differentiating cc-RCC from p-RCC and ch-RCC.

KEY POINTS

• RCC CS-SII values are significantly lower than those of mfAML overall. • CS-SII values cannot aid differentiation between mfAML and cc-RCC. • CS-SII values might help characterise RCC subtypes.

Imaging and Screening of Kidney Cancer

Renal cell carcinoma (RCC) exhibits a diverse and heterogeneous disease spectrum, but insight into its molecular biology has provided an improved understanding of potential risk factors, oncologic behavior, and imaging features. Computed tomography (CT) and MR imaging may allow the identification and preoperative subtyping of RCC and assessment of a response to various therapies. Active surveillance is a viable management option in some patients and has provided further insight into the natural history of RCC, including the favorable prognosis of cystic neoplasms. This article reviews CT and MR imaging in RCC and the role of screening in selected high-risk populations.

Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI

BACKGROUND

Dysregulated lipid and glucose metabolism in clear cell renal cell carcinoma (ccRCC) has been implicated in disease progression, and whole tumor tissue-based assessment of these changes is challenged by the tumor heterogeneity. We studied a noninvasive quantitative MRI method that predicts metabolic alterations in the whole tumor.

METHODS

We applied Dixon-based MRI for in vivo quantification of lipid accumulation (fat fraction [FF]) in targeted regions of interest of 45 primary ccRCCs and correlated these MRI measures to mass spectrometry-based lipidomics and metabolomics of anatomically colocalized tissue samples isolated from the same tumor after surgery.

RESULTS

In vivo tumor FF showed statistically significant (P < 0.0001) positive correlation with histologic fat content (Spearman correlation coefficient, ρ = 0.79), spectrometric triglycerides (ρ = 0.56) and cholesterol (ρ = 0.47); it showed negative correlation with free fatty acids (ρ = -0.44) and phospholipids (ρ = -0.65). We observed both inter- and intratumoral heterogeneity in lipid accumulation within the same tumor grade, whereas most aggressive tumors (International Society of Urological Pathology [ISUP] grade 4) exhibited reduced lipid accumulation. Cellular metabolites in tumors were altered compared with adjacent renal parenchyma.

CONCLUSION

Our results support the use of noninvasive quantitative Dixon-based MRI as a biomarker of reprogrammed lipid metabolism in ccRCC, which may serve as a predictor of tumor aggressiveness before surgical intervention.

FUNDING

NIH R01CA154475 (YZ, MF, PK, IP), NIH P50CA196516 (IP, JB, RJD, JAC, PK), Welch Foundation I-1832 (JY), and NIH P01HL020948 (JGM).

Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma

OBJECTIVES

To apply a statistical clustering algorithm to combine information from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) into a single tumour map to distinguish high-grade from low-grade T1b clear cell renal cell carcinoma (ccRCC).

METHODS

This prospective, Institutional Review Board -approved, Health Insurance Portability and Accountability Act -compliant study included 18 patients with solid T1b ccRCC who underwent pre-surgical DCE MRI. After statistical clustering of the parametric maps of the transfer constant between the intravascular and extravascular space (K ^trans ), rate constant (K _ep ) and initial area under the concentration curve (iAUC) with a fuzzy c-means (FCM) algorithm, each tumour was segmented into three regions (low/medium/high active areas). Percentages of each region and tumour size were compared to tumour grade at histopathology. A decision-tree model was constructed to select the best parameter(s) to predict high-grade ccRCC.

RESULTS

Seven high-grade and 11 low-grade T1b ccRCCs were included. High-grade histology was associated with higher percent high active areas (p = 0.0154) and this was the only feature selected by the decision tree model, which had a diagnostic performance of 78% accuracy, 86% sensitivity, 73% specificity, 67% positive predictive value and 89% negative predictive value.

CONCLUSIONS

The FCM integrates multiple DCE-derived parameter maps and identifies tumour regions with unique pharmacokinetic characteristics. Using this approach, a decision tree model using criteria beyond size to predict tumour grade in T1b ccRCCs is proposed.

KEY POINTS

• Tumour size did not correlate with tumour grade in T1b ccRCC. • Tumour heterogeneity can be analysed using statistical clustering via DCE-MRI parameters. • High-grade ccRCC has a larger percentage of high active area than low-grade ccRCCs. • A decision-tree model offers a simple way to differentiate high/low-grade ccRCCs.

Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging to Identify Clear Cell Renal Cell Carcinoma in cT1a Renal Masses

PURPOSE

The detection of small renal masses is increasing with the use of cross-sectional imaging, although many incidental lesions have negligible metastatic potential. Among malignant masses clear cell renal cell carcinoma is the most prevalent and aggressive subtype. A method to identify such histology would aid in risk stratification. Our goal was to evaluate a likelihood scale for multiparametric magnetic resonance imaging in the diagnosis of clear cell histology.

MATERIALS AND METHODS

We retrospectively reviewed the records of patients with cT1a masses who underwent magnetic resonance imaging and partial or radical nephrectomy from December 2011 to July 2015. Seven radiologists with different levels of experience who were blinded to final pathology findings independently reviewed studies based on a predefined algorithm. They applied a clear cell likelihood score, including 1-definitely not, 2-probably not, 3-equivocal, 4-probably and 5-definitely. Binary classification was used to determine the accuracy of clear cell vs all other histologies. Interobserver agreement was calculated with the weighted κ statistic.

RESULTS

A total of 110 patients with 121 masses were identified. Mean tumor size was 2.4 cm and 50% of the lesions were clear cell. Defining clear cell as scores of 4 or greater demonstrated 78% sensitivity and 80% specificity while scores of 3 or greater showed 95% sensitivity and 58% specificity. Interobserver agreement was moderate to good with a mean κ of 0.53.

CONCLUSIONS

A clear cell likelihood score used with magnetic resonance imaging can reasonably identify clear cell histology in small renal masses and may decrease the number of diagnostic renal mass biopsies. Standardization of imaging protocols and reporting criteria is needed to improve interobserver reliability.

Can diffusion-weighted magnetic resonance imaging of clear cell renal carcinoma predict low from high nuclear grade tumors

OBJECTIVE

To assess the diagnostic performance of the apparent diffusion coefficient (ADC) in predicting the Fuhrman nuclear grading of clear cell renal cell carcinomas (ccRCC).

MATERIALS AND METHODS

A total of 129 patients who underwent partial and radical nephrectomies with pathology-proven ccRCC were retrospectively evaluated. Histopathological characteristics and nuclear grades were analyzed. In addition, conventional magnetic resonance imaging (MRI) features were assessed in consensus by two radiologists to discriminate nuclear grading. ADC values were obtained from a region of interest (ROI) measurement in the ADC maps calculated from diffusion-weighted imaging (DWI) using b values of 50, 500, and 800 s/mm². The threshold values for predicting and differentiating low-grade cancers (Fuhrman I-II) from high grade (Fuhrman III-IV) was obtained using binary logistic regression. The ADC cut-off value for differentiating low- and high-grade tumors was determined using classification analysis.

RESULTS

Significant associations (P < 0.001) were found between nuclear grading, conventional MR features, and DWI. Hemorrhage, necrosis, perirenal fat invasion, enhancement homogeneity, and cystic component were identified as independent predictors of tumor grade. High-grade ccRCC had significantly lower mean ADC values compared to low-grade tumors. An ADC cut-off value of 1.6 × 10^-3 mm²/s had an optimal predictive percentage of 65.5% for low-grade tumors above this threshold and 81% for high-grade ccRCC below this threshold. Overall predictive accuracy was 70.5%.

CONCLUSION

The addition of ADC values to a model based on MRI conventional features demonstrates increased sensitivity and high specificity improving the distinguishing accuracy between both high-grade and low-grade ccRCC.

Qualitative and Quantitative Imaging Evaluation of Renal Cell Carcinoma Subtypes with Grating-based X-ray Phase-contrast CT

Current clinical imaging methods face limitations in the detection and correct characterization of different subtypes of renal cell carcinoma (RCC), while these are important for therapy and prognosis. The present study evaluates the potential of grating-based X-ray phase-contrast computed tomography (gbPC-CT) for visualization and characterization of human RCC subtypes. The imaging results for 23 ex vivo formalin-fixed human kidney specimens obtained with phase-contrast CT were compared to the results of the absorption-based CT (gbCT), clinical CT and a 3T MRI and validated using histology. Regions of interest were placed on each specimen for quantitative evaluation. Qualitative and quantitative gbPC-CT imaging could significantly discriminate between normal kidney cortex (54 ± 4 HUp) and clear cell (42 ± 10), papillary (43 ± 6) and chromophobe RCCs (39 ± 7), p < 0.05 respectively. The sensitivity for detection of tumor areas was 100%, 50% and 40% for gbPC-CT, gbCT and clinical CT, respectively. RCC architecture like fibrous strands, pseudocapsules, necrosis or hyalinization was depicted clearly in gbPC-CT and was not equally well visualized in gbCT, clinical CT and MRI. The results show that gbPC-CT enables improved discrimination of normal kidney parenchyma and tumorous tissues as well as different soft-tissue components of RCCs without the use of contrast media.

Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma

Background Identification of clinical features to determine the aggressive potential of tumors is highly warranted to stratify patients for adequate treatment. Computed tomography (CT) imaging features of clear cell renal cell carcinoma (ccRCC) may contribute to personalized risk assessment. Purpose To assess the correlation between CT imaging features and Fuhrman grade of ccRCC, and to identify the predictors of high Fuhrman grade in conjunction with tumor size. Material and Methods CT scans of 169 patients with 173 pathologically proven ccRCCs were retrospectively reviewed in consensus by two radiologists for the presence of intratumoral necrosis and intratumoral cyst and tumor size. Histologic grade was classified as either low (Fuhrman grade I or II) or high (Fuhrman grade III or IV). Statistical significance was evaluated by using univariate, multivariate regression, receiver operating characteristic (ROC) curve, and Spearman correlation analyses. Results On CT, 20 of the 173 tumors had intratumoral cysts, 60 had intratumoral necrosis, and 93 showed entirely solid tumors. The odds of high grade were higher with intratumoral necrosis and entirely solid tumor than with intratumoral cyst ( P < 0.03). Intratumoral necrosis showed a significantly high odds ratio of 25.73 for high Fuhrman grade. The ROC curve showed a threshold tumor size of 36 mm to predict high Fuhrman grade for overall tumors (area under the ROC curve, 0.70). In ccRCCs with intratumoral necrosis or cyst, tumor size did not significantly correlate with Fuhrman grade. Conclusion Intratumoral necrosis on CT was a strong and independent predictor of biologically aggressive ccRCCs, irrespective of tumor size.

Imaging of Solid Renal Masses

Detection of solid renal masses has increased, although it has not resulted in significant mortality reduction from renal cell carcinoma. Efforts for improved lesion characterization have been pursued and incorporated in management algorithms, in order to distinguish clinically significant tumors from favorable or benign conditions. Concurrently, imaging methods have produced evidence supporting their role as useful tools not only in lesion detection but also characterization. In addition, newer modalities, such as contrast-enhanced ultrasonography, and advanced applications of MR imaging, are being investigated. This article reviews the current role of different imaging methods in the characterization of solid renal masses.

The radiologist's role in the management of papillary renal cell carcinoma

Papillary carcinoma is the second most common renal cell carcinoma. It has a better prognosis than the more frequent clear cell carcinoma, although this does not hold true for advanced cases, because no specific treatment exists. It presents as a circumscribed peripheral tumor (small and homogeneously solid or larger and cystic/hemorrhagic) or as an infiltrating lesion that invades the veins, which has a worse prognosis. Due to their low vascular density, papillary renal cell carcinomas enhance less than other renal tumors, and this facilitates their characterization. On computed tomography, they might not enhance conclusively, and in these cases they are impossible to distinguish from hyperattenuating cysts. Contrast-enhanced ultrasonography and magnetic resonance imaging are more sensitive for detecting vascularization. Other characteristics include a specific vascular pattern, hypointensity on T2-weighted images, restricted water diffusion, and increased signal intensity in opposed phase images. We discuss the genetic, histologic, clinical, and radiological aspects of these tumors in which radiologists play a fundamental role in management.