Predominantly multiple focal non-cystic renal lesions: an imaging approach

Multiple non-cystic renal lesions are occasionally discovered during imaging for various reasons and poses a diagnostic challenge to the practicing radiologist. These lesions may appear as a primary or dominant imaging finding or may be an additional abnormality in the setting of multiorgan involvement. Awareness of the imaging appearance of the various entities presenting as renal lesions integrated with associated extrarenal imaging findings along with clinical information is crucial for a proper diagnostic approach and patient work-up. This review summarizes the most relevant causes of infectious, inflammatory, vascular, and neoplastic disorders presenting as predominantly multiple focal non-cystic lesions.

Clear cell likelihood score may improve diagnosis and management of renal masses

The detection of solid renal masses has increased over time due to incidental findings during imaging studies conducted for unrelated medical conditions. Approximately 20% of lesions measuring less than 4 cm are benign and 80% are malignant. Clear cell renal cell carcinoma (ccRCC) is the most frequent among renal carcinomas, responsible for 65-80% of cases. The increased detection of renal masses facilitates early diagnosis and treatment. However, it also leads to more invasive interventions, which result in higher morbidity and costs. Currently, only histological analysis can offer an accurate diagnosis. Surgical nephron loss significantly elevates morbidity and mortality rates. Active surveillance represents a conservative management approach for patients diagnosed with a solid renal mass that is endorsed by both American Urological Association and the European Society for Medical Oncology. However, active surveillance is used in a minority of patients and varies across institutions. The lack of clinical studies using a standardized approach to incidentally detected small renal masses precludes the widespread use of active surveillance. Hence, there is an urgent need for better patient selection, distinguishing those who require surgery from those suitable for active surveillance. The clear cell likelihood score (ccLS) represents a novel MRI tool for assessing the probability of a renal mass being a ccRCC. In this study, we present a comprehensive review of renal masses and their evaluation using the ccLS to facilitate shared decision between urologists and patients.

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

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.

Renal angiomyolipoma: from imaging to intervention

A high volume of cross-sectional imaging has created a window of opportunity for radiologists to identify renal angiomyolipomas (AMLs). The purpose of this review is to help the reader recognise the spectrum of renal AML appearances using different imaging methods and to gain an understanding of the classic and atypical features for appropriate lesion characterisation. Risk factors for AML growth and rupture will be highlighted. An overview of the imaging features of acute AML rupture will be provided, principally relating to computed tomography (CT) assessment. A series of cases will be presented, including a case of peripartum renal AML rupture during Caesarean section leading to diagnostic dilemma. The indications for intervention and available treatment options will be considered: medical therapy, surgery, and interventional radiology (IR) techniques including their pros and cons. Emergency interventional radiology management with selective transarterial embolisation will be presented and analysed in relation to technique, angiographic appearances (pre and post embolisation) and associated complications.

Machine Learning-Based Magnetic Resonance Radiomics Analysis for Predicting Low- and High-Grade Clear Cell Renal Cell Carcinoma

PURPOSE

To explore whether high- and low-grade clear cell renal cell carcinomas (ccRCC) can be distinguished using radiomics features extracted from magnetic resonance imaging.

METHODS

In this retrospective study, 154 patients with pathologically proven clear ccRCC underwent contrast-enhanced 3 T magnetic resonance imaging and were assigned to the development (n = 122) and test (n = 32) cohorts in a temporal-split setup. A total of 834 radiomics features were extracted from whole-tumor volumes using 3 sequences: T2-weighted imaging (T2WI), diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging. A random forest regressor was used to extract important radiomics features that were subsequently used for model development using the random forest algorithm. Tumor size, apparent diffusion coefficient value, and percentage of tumor-to-renal parenchymal signal intensity drop in the tumors were recorded by 2 radiologists for quantitative analysis. The area under the receiver operating characteristic curve (AUC) was generated to predict ccRCC grade.

RESULTS

In the development cohort, the T2WI-based radiomics model demonstrated the highest performance (AUC, 0.82). The T2WI-based radiomics and radiologic feature hybrid model showed AUCs of 0.79 and 0.83, respectively. In the test cohort, the T2WI-based radiomics model achieved an AUC of 0.82. The range of AUCs of the hybrid model of T2WI-based radiomics and radiologic features was 0.73 to 0.80.

CONCLUSION

Magnetic resonance imaging-based classifier models using radiomics features and machine learning showed satisfactory diagnostic performance in distinguishing between high- and low-grade ccRCC, thereby serving as a helpful noninvasive tool for predicting ccRCC grade.

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.

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.

Wunderlich Syndrome: Comprehensive Review of Diagnosis and Management

Wunderlich syndrome (WS), which was named after Carl Wunderlich, is a rare clinical syndrome characterized by an acute onset of spontaneous renal hemorrhage into the subcapsular, perirenal, and/or pararenal spaces, without a history of antecedent trauma. Patients may present with a multitude of symptoms ranging from nonspecific flank or abdominal pain to serious manifestations such as hypovolemic shock. The classic symptom complex of flank pain, a flank mass, and hypovolemic shock referred to as the Lenk triad is seen in a small subset of patients. Renal neoplasms such as angiomyolipomas and clear cell renal cell carcinomas that display an increased proclivity for hemorrhage and rupture contribute to approximately 60%-65% of all cases of WS. A plethora of renal vascular diseases (aneurysms or pseudoaneurysms, arteriovenous malformations or fistulae, renal vein thrombosis, and vasculitis syndromes) account for 20%-30% of cases of WS. Rare causes of WS include renal infections, cystic diseases, calculi, kidney failure, and coagulation disorders. Cross-sectional imaging modalities, particularly multiphasic CT or MRI, are integral to the detection, localization, and characterization of the underlying causes and facilitate optimal management. However, large-volume hemorrhage at patient presentation may obscure underlying causes, particularly neoplasms. If the initial CT or MRI examination shows no contributary causes, a dedicated CT or MRI follow-up study may be warranted to establish the cause of WS. Renal arterial embolization is a useful, minimally invasive, therapeutic option in patients who present with acute or life-threatening hemorrhage and can help avoid emergency radical surgery. Accurate diagnosis of the underlying cause of WS is critical for optimal patient treatment in emergency and nonemergency clinical settings. ^©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

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.

Performance of clear cell likelihood scores in characterizing solid renal masses at multiparametric MRI: an external validation study

PURPOSE

The purpose of this study is to evaluate the accuracy and interobserver agreement of ccLS in diagnosing clear cell renal cell carcinoma (ccRCC).

METHODS

This retrospective single-center study evaluated consecutive patients with solid renal masses who underwent mpMRI followed by percutaneous biopsy and/or surgical excision between January 2010 and December 2020. Predominantly (> 75%) cystic masses, masses with macroscopic fat and infiltrative masses were excluded. Two abdominal radiologists independently scored each renal mass according to the proposed ccLS algorithm. The diagnostic performance of ccLS categories for ccRCC was calculated using logistic regression modeling. Diagnostic accuracy for predicting ccRCC was calculated using 2 × 2 contingency tables. Interobserver agreement for ccLS was evaluated with Cohen's k statistic.

RESULTS

A total of 79 patients (mean age, 63 years ± 12 [SD], 50 men) with 81 renal masses were evaluated. The mean size was 36 mm ± 28 (range 10-160). Of the renal masses included, 44% (36/81) were ccRCC. The area under the receiver operating characteristic curve was 0.87 (95% CI 0.79-0.95). Using ccLS ≥ 4 to diagnose ccRCC, the sensitivity, specificity, and positive predictive value were 93% (95% CI 79, 99), 63% (95% CI 48, 77), and 67% (95% CI 58, 75), respectively. The negative predictive value of ccLS ≤ 2 was 93% (95% CI 64, 99). The proportion of ccRCC by ccLS category 1 to 5 were 10%, 0%, 10%, 57%, and 84%, respectively. Interobserver agreement was moderate (k = 0.47).

CONCLUSION

In this study, clear cell likelihood score had moderate interobserver agreement and resulted in 96% negative predictive value in excluding ccRCC.

Magnetic Resonance Imaging Virtual Biopsy of Common Solid Renal Masses-A Pictorial Review

ABSTRACT

The expanded application of radiologic imaging resulted in an increased incidence of renal masses in the recent decade. Clinically, it is difficult to determine the malignant potential of the renal masses, thus resulting in complex management. Image-guided biopsies are the ongoing standard of care to identify molecular variance but are limited by tumor accessibility and heterogeneity. With the evolving importance of individualized cancer therapies, radiomics has displayed promising results in the identification of tumoral mutation status on routine imaging. This article discusses how magnetic resonance imaging features can guide a radiologist toward identifying renal mass characteristics.

MRI Characteristics of Pediatric and Young-Adult Renal Cell Carcinoma: A Single-Center Retrospective Study and Literature Review

Pediatric renal cell carcinoma (RCC) is a rare malignancy. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessment of these tumors. The previous literature has suggested that cross-sectional-imaging findings differ between RCC and other pediatric renal tumors and between RCC subtypes. However, studies focusing on MRI characteristics are limited. Therefore, this study aims to identify MRI characteristics of pediatric and young-adult RCC, through a single-center case series and literature review. Six identified diagnostic MRI scans were retrospectively assessed, and an extensive literature review was conducted. The included patients had a median age of 12 years (63-193 months). Among other subtypes, 2/6 (33%) were translocation-type RCC (MiT-RCC) and 2/6 (33%) were clear-cell RCC. Median tumor volume was 393 cm³ (29-2191 cm³). Five tumors had a hypo-intense appearance on T2-weighted imaging, whereas 4/6 were iso-intense on T1-weighted imaging. Four/six tumors showed well-defined margins. The median apparent diffusion coefficient (ADC) values ranged from 0.70 to 1.20 × 10^-3 mm²/s. In thirteen identified articles focusing on MRI characteristics of MiT-RCC, the majority of the patients also showed T2-weighted hypo-intensity. T1-weighted hyper-intensity, irregular growth pattern and limited diffusion-restriction were also often described. Discrimination of RCC subtypes and differentiation from other pediatric renal tumors based on MRI remains difficult. Nevertheless, T2-weighted hypo-intensity of the tumor seems a potential distinctive characteristic.

Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies

OBJECTIVES

To determine whether a CT-based machine learning (ML) can differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance, and evaluate the impact of variable CT imaging phases, slices, tumor sizes, and region of interest (ROI) segmentation strategies.

METHODS

Patients with pathologically proven RCCs and benign renal tumors from our institution between 2008 and 2020 were included as the training dataset for ML model development and internal validation (including 418 RCCs and 78 benign tumors), and patients from two independent institutions and a public database (TCIA) were included as the external dataset for individual testing (including 262 RCCs and 47 benign tumors). Features were extracted from three-phase CT images. CatBoost was used for feature selection and ML model establishment. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML model.

RESULTS

The ML model based on 3D images performed better than that based on 2D images, with the highest AUC of 0.81 and accuracy (ACC) of 0.86. All three radiologists achieved better performance by referring to the classifier's decision, with accuracies increasing from 0.82 to 0.87, 0.82 to 0.88, and 0.76 to 0.87. The ML model achieved higher negative predictive values (NPV, 0.82-0.99), and the radiologists achieved higher positive predictive values (PPV, 0.91-0.95).

CONCLUSIONS

A ML classifier based on whole-tumor three-phase CT images can be a useful and promising tool for differentiating RCCs from benign renal tumors. The ML model also perfectly complements radiologist interpretations.

KEY POINTS

• A machine learning classifier based on CT images could be a reliable way to differentiate RCCs from benign renal tumors. • The machine learning model perfectly complemented the radiologists' interpretations. • Subtle variances in ROI delineation had little effect on the performance of the ML classifier.

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.

Imaging Findings of a Renomedullary Interstitial Cell Tumor: A Case Report

Introduction: Renomedullary interstitial cell tumors are benign tumors of renal medulla. They are usually asymptomatic, and preoperative diagnosis based on radiological findings is challenging. Therefore, in most clinical situations, nephrectomy is ultimately performed for differential diagnosis. Case Presentation: A 54-year-old woman presented to our hospital with hematuria. An incidental mass in the left kidney was detected on abdominal computed tomography (CT) scan. The mass showed iso-attenuation to renal parenchyma in the pre-contrast image and hypo-attenuation in the portal venous phase; however, some enhancement was observed in the central portion of the mass. Based on contrast-enhanced ultrasonography (CEUS) after one year, a slight septum-like enhancement was observed in the central portion of the mass in the venous phase. In dynamic contrast-enhanced T1- and T2-weighted magnetic resonance images (MRI), the mass showed a low signal intensity, and delayed persistent enhancement was observed in 10- and 15-minute delayed phases. The mass was finally diagnosed as a renomedullary interstitial cell tumor. Conclusion: The imaging findings of renomedullary interstitial tumors included a low-signal-intensity mass of renal medulla on T1- and T2-weighted MRI and delayed enhancement on CEUS and dynamic MRI.

Systematic review and meta-analysis of multiparametric MRI clear cell likelihood scores for classification of small renal masses

Purpose

To systematically assess the multiparametric MRI clear cell likelihood score (ccLS) algorithm for the classification of small renal masses (SRM).

Methods

We conducted an electronic literature search on Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify relevant articles from 2017 up to June 30, 2022. We included studies reporting the diagnostic performance of the ccLS for characterization of solid SRM. The bivariate model and hierarchical summary receiver operating characteristic (HSROC) model were used to pool sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR). The quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

A total of 6 studies with 825 renal masses (785 patients) were included in the current meta-analysis. The pooled sensitivity and specificity for cT1a renal masses were 0.80 (95% CI 0.75–0.85) and 0.74 (95% CI 0.65–0.81) at the threshold of ccLS ≥4, the pooled LR+, LR−, and DOR were 3.04 (95% CI 2.34-3.95), 0.27 (95% CI 0.22–0.33), and 11.4 (95% CI 8.2-15.9), respectively. The area under the HSROC curve was 0.84 (95% CI 0.81–0.87). For all cT1 renal masses, the pooled sensitivity and specificity were 0.80 (95% CI 0.74–0.85) and 0.76 (95% CI 0.67–0.83).

Conclusions

The ccLS had moderate to high accuracy for identifying ccRCC from other RCC subtypes and with a moderate inter-reader agreement. However, its diagnostic performance remain needs multi-center, large cohort studies to validate in the future.

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.

Multimodality imaging of renal lymphoma and its mimics

Lymphomatous involvement of the genitourinary system, particularly the kidneys, is commonly detected on autopsies; yet on conventional diagnostic imaging renal lymphoma is significantly underestimated and underreported, in part due to its variable imaging appearance and overlapping features with other conditions. We present a spectrum of typical and atypical appearances of renal lymphoma using multimodality imaging, while reviewing the roles of imaging in the detection, diagnosis, staging, and surveillance of patients with lymphoma. We also illustrate a breadth of benign and malignant entities with similar imaging features confounding the diagnosis of renal lymphoma, emphasizing the role of percutaneous image-guided biopsy. Understanding the spectrum of appearances of renal lymphoma and recognizing the overlapping entities will help radiologists improve diagnostic confidence and accuracy.

Acquired cystic disease subtype renal cell carcinoma (ACD-RCC): prevalence and imaging features at a single institution

PURPOSE

Acquired cystic kidney disease (ACKD) is commonly seen in patients with end-stage renal disease (ESRD), and patients with ACKD have an increased risk of renal cell carcinoma (RCC). Acquired cystic disease-associated RCC (ACD-RCC) was incorporated into the 2016 World Health Organization Classification. This study aims to describe the imaging features of ACD-RCC, which are not well reported previously.

METHODS

Retrospective review of patients with ACKD who underwent total nephrectomy for concern of a renal mass between 2016 and 2021 yielded 122 nephrectomies in 107 patients. Pathology reports were searched for type and subtype of mass. In ACD-RCC subtypes, imaging studies were evaluated for modality and contrast enhancement (CE). Imaging findings assessed included cystic/solid nature, unenhanced CT (NECT) attenuation, enhancement characteristics [non-enhancing (< 10 HU difference), equivocal (10-20 HU), enhancing (> 20 HU)], subjective MRI enhancement, T1 and T2 signal intensity, restricted diffusion, ultrasound (US) echogenicity, and subjective CEUS enhancement.

RESULTS

148 masses were identified, 122 (82%) of which were malignant and 26 (18%) benign. The three most common tumors were clear cell RCC (n = 47), papillary RCC (n = 35), and ACD-RCC (n = 21). Of the 21 cases of ACD-RCC, 16 had preoperative imaging: CT (15: 6 NECT only, 2 CECT only, 7 combined NECT and CECT), MRI (4), CEUS (5). Ten of these tumors were solid/mostly solid and 6 mixed cystic/solid. On NECT, the average attenuation was 35 HU (range 13-52). Of those with multiphasic CTs, 1 was non-enhancing, 3 were equivocal, and 3 enhanced. All 3 masses imaged with CE-MRI showed enhancement. All 4 tumors evaluated by MRI demonstrated T2 hypointensity and restricted diffusion. All five masses enhanced on CEUS.

CONCLUSION

ACD-RCC subtype was the third most common renal neoplasm in ACKD patients. Our findings found that no single imaging feature is pathognomonic for ACD-RCC. However, ACD-RCCs are typically solid masses with most demonstrating equivocal or mild enhancement on CT. T2 hypointensity and restricted diffusion were the most common MRI features.

Defining the Magnetic Resonance Features of Renal Lesions and Their Response to Everolimus in a Transgenic Mouse Model of Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is an inherited genetic disorder characterized by mutations in TSC1 or TSC2 class of tumor suppressers which impact several organs including the kidney. The renal manifestations are usually in the form of angiomyolipoma (AML, in 80% of the cases) and cystadenomas. mTOR inhibitors such as rapamycin and everolimus have shown efficacy in reducing the renal tumor burden. Early treatment prevents the progression of AML; however, the tumors regrow upon cessation of therapy implying a lifelong need for monitoring and management of this morbid disease. There is a critical need for development of imaging strategies to monitor response to therapy and progression of disease which will also facilitate development of newer targeted therapy. In this study we evaluated the potential of multiparametric ¹H magnetic resonance imaging (mpMRI) to monitor tumor response to therapy in a preclinical model of TSC, the transgenic mouse A/J Tsc2^+/-. We found 2-dimensional T₂-weighted sequence with 0.5 mm slice thickness to be optimal for detecting renal lesions as small as 0.016 mm³. Baseline characterization of lesions with MRI to assess physiological parameters such as cellularity and perfusion is critical for distinguishing between cystic and solid lesions. Everolimus treatment for three weeks maintained tumor growth at 36% from baseline, while control tumors displayed steady growth and were 70% larger than baseline at the end of therapy. Apparent diffusion coefficient, T₁ values and normalized T₂ intensity changes were also indictive of response to treatment. Our results indicate that standardization and implementation of improved MR imaging protocols will significantly enhance the utility of mpMRI in determining the severity and composition of renal lesions for better treatment planning.

Diagnostic Workup for Patients with Solid Renal Masses: A Cost-Effectiveness Analysis

Background: For patients with solid renal masses, a precise differentiation between malignant and benign tumors is crucial for forward treatment management. Even though MRI and CT are often deemed as the gold standard in the diagnosis of solid renal masses, CEUS may also offer very high sensitivity in detection. The aim of this study therefore was to evaluate the effectiveness of CEUS from an economical point of view. Methods: A decision-making model based on a Markov model assessed expenses and utilities (in QALYs) associated with CEUS, MRI and CT. The utilized parameters were acquired from published research. Further, a Monte Carlo simulation-based deterministic sensitivity analysis of utilized variables with 30,000 repetitions was executed. The willingness-to-pay (WTP) is at USD 100,000/QALY. Results: In the baseline, CT caused overall expenses of USD 10,285.58 and an efficacy of 11.95 QALYs, whereas MRI caused overall expenses of USD 7407.70 and an efficacy of 12.25. Further, CEUS caused overall expenses of USD 5539.78, with an efficacy of 12.44. Consequently, CT and MRI were dominated by CEUS, and CEUS remained cost-effective in the sensitivity analyses. Conclusions: CEUS should be considered as a cost-effective imaging strategy for the initial diagnostic workup and assessment of solid renal masses compared to CT and MRI.

Imaging findings of spontaneous intraabdominal hemorrhage: neoplastic and non-neoplastic causes

Contrary to traumatic and iatrogenic intraabdominal hemorrhages, spontaneous intraabdominal hemorrhage is a challenging clinical situation. A variety of neoplastic and non-neoplastic conditions may cause spontaneous intraabdominal bleeding. Imaging findings vary depending on the source of bleeding and the underlying cause. In this article, we aim to increase the awareness of imagers to the most common causes of spontaneous intraabdominal hemorrhage by using representative cases.

Renal Oncocytoma: The Diagnostic Challenge to Unmask the Double of Renal Cancer

Renal oncocytoma represents the most common type of benign neoplasm that is an increasing concern for urologists, oncologists, and nephrologists due to its difficult differential diagnosis and frequent overtreatment. It displays a variable neoplastic parenchymal and stromal architecture, and the defining cellular element is a large polygonal, granular, eosinophilic, mitochondria-rich cell known as an oncocyte. The real challenge in the oncocytoma treatment algorithm is related to the misdiagnosis due to its resemblance, at an initial radiological assessment, to malignant renal cancers with a completely different prognosis and medical treatment. Unfortunately, percutaneous renal biopsy is not frequently performed due to the possible side effects related to the procedure. Therefore, the majority of oncocytoma are diagnosed after the surgical operation via partial or radical nephrectomy. For this reason, new reliable strategies to solve this issue are needed. In our review, we will discuss the clinical implications of renal oncocytoma in daily clinical practice with a particular focus on the medical diagnosis and treatment and on the potential of novel promising molecular biomarkers such as circulating microRNAs to distinguish between a benign and a malignant lesion.

Common Subtype of Small Renal Mass MR Imaging Characterisation: A Medical Center Experience in Taiwan

Purpose

Many studies have shown that multiparametric magnetic resonance imaging (MRI) may be helpful for differentiating malignant renal cell carcinomas (RCCs) from benign lesions. However, the key imaging characteristics that differ between malignant and benign tumors still require further discussion.

Methods

We analyzed 60 adult patients diagnosed with 72 small renal masses (SRMs) who received preoperative MRI from 2014 to 2019 at a hospital in Taiwan. The MRI features included conventional MRI parameters, diffusion-weighted imaging (DWI) data, and dynamic contrast-enhanced (DCE) patterns, which were documented and compared among the four common subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), angiomyolipoma (AML) and other types of RCC. The apparent diffusion coefficient (ADC) values of high- and low-grade RCCs were also analyzed.

Results

The results show that ccRCC had higher T2-weighted signal intensity than the other three subgroups, a higher arterial wash-in index (AWI) and ADC value than AML and pRCC, and manifested a plateau (n = 9, 25%) or washout (n = 27, 75%) enhancement pattern. AMLs exhibited more intravoxel fat than the other three subtype groups, and half of the AMLs (6 in 12) contained bulk fat. pRCC demonstrated a more progressive (n = 3, 60%) enhancement pattern than the other three subgroups. The ADC value of high-grade RCCs was significantly lower than that of low-grade RCCs.

Conclusion

These findings may indicate that multiparametric MRI is useful in differentiating among four common pathological types of SRMs, and the ADC value may be helpful in evaluating the histological grade of malignancy.

Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning

Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.

MRI Findings of Renal Myxoma: A Case Report and Literature Review

Renal myxomas are very rare benign tumors. To date, a few cases have been reported in English literature, mostly in pathology and urology journals. Thus, there are few reports on the radiological findings associated with renal myxomas. We report on the imaging findings in a case of renal myxoma in a 62-year-old male. MRI demonstrated a well-defined mass in the left renal sinus, with intermediate high signal intensity on T2-weighted images and low signal intensity on T1-weighted images. The tumor showed gradual enhancement on contrast-enhanced T1-weighted images.

Quantitative 3-tesla multiparametric MRI in differentiation between renal cell carcinoma subtypes

Background

MRI provides several distinct quantitative parameters that may better differentiate renal cell carcinoma (RCC) subtypes. The purpose of the study is to evaluate the diagnostic accuracy of apparent diffusion coefficient (ADC), chemical shift signal intensity index (SII), and contrast enhancement in differentiation between different subtypes of renal cell carcinoma.

Results

There were 63 RCC as regard surgical histopathological analysis: 43 clear cell (ccRCC), 12 papillary (pRCC), and 8 chromophobe (cbRCC). The mean ADC ratio for ccRCC (0.75 ± 0.13) was significantly higher than that of pRCC (0.46 ± 0.12, P < 0.001) and cbRCC (0.41 ± 0.15, P < 0.001). The mean ADC value for ccRCC (1.56 ± 0.27 × 10−3 mm2/s) was significantly higher than that of pRCC (0.96 ± 0.25 × 10−3 mm2/s, P < 0.001) and cbRCC (0.89 ± 0.29 × 10−3 mm2/s, P < 0.001). The mean SII of pRCC (1.49 ± 0.04) was significantly higher than that of ccRCC (0.93 ± 0.01, P < 0.001) and cbRCC (1.01 ± 0.16, P < 0.001). The ccRCC absolute corticomedullary enhancement (196.7 ± 81.6) was significantly greater than that of cbRCC (177.8 ± 77.7, P < 0.001) and pRCC (164.3 ± 84.6, P < 0.001).

Conclusion

Our study demonstrated that multiparametric MRI is able to afford some quantitative features such as ADC ratio, SII, and absolute corticomedullary enhancement which can be used to accurately distinguish different subtypes of renal cell carcinoma.

DWI and ADC value versus ADC ratio in the characterization of solid renal masses: radiologic-pathologic correlation

Background

Renal masses are becoming an increasingly common finding on cross-sectional images. Characterization of the nature of the lesion either neoplastic or not, benign or malignant as well as further subtype characterization is becoming an important factor in determining management plan. The purpose of our study with to assess the sensitivity and specificity of both ADC mean value and ADC ratio in such characterization along with the calculation of different cutoff values to differentiate between different varieties, using pathological data as the main gold standard for diagnosis.

Results

Our study included 50 patients with a total of 72 masses. A final diagnosis was reached in 69 masses by pathological examination and three masses had clinical and laboratory signs of infection. We had a total of 49 malignant lesions (68%) and 23 benign lesions (32%). The ADC value of ccRCC (1.4 × 10−3 mm2/s) was significantly higher than all other renal masses. A cutoff ADC value of > 1.1 and a cutoff ADC ratio of > 0.56 can be used to differentiate between clear cell renal cell carcinoma and other lesions and an ADC value of < 0.8 and an ADC ratio of ≤ 0.56 to differentiate papillary renal cell carcinoma from other masses. There was no statistically significant ADC value to differentiate between benign and malignant lesions but a statistically significant ADC ratio (> 0.52) was reached.

Conclusion

ADC value and ADC ratio can be used as an adjunct tool in the characterization of different renal masses, with ADC ratio having a higher sensitivity, which can affect the prognosis and management of the patient.

Multimodality imaging review of focal renal lesions

Background

Focal lesions of the kidney comprise a spectrum of entities that can be broadly classified as malignant tumors, benign tumors, and non-neoplastic lesions. Malignant tumors include renal cell carcinoma subtypes, urothelial carcinoma, lymphoma, post-transplant lymphoproliferative disease, metastases to the kidney, and rare malignant lesions. Benign tumors include angiomyolipoma (fat-rich and fat-poor) and oncocytoma. Non-neoplastic lesions include infective, inflammatory, and vascular entities. Anatomical variants can also mimic focal masses.

Main body of the abstract

A range of imaging modalities are available to facilitate characterization; ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), magnetic resonance (MR) imaging, and positron emission tomography (PET), each with their own strengths and limitations. Renal lesions are being detected with increasing frequency due to escalating imaging volumes. Accurate diagnosis is central to guiding clinical management and determining prognosis. Certain lesions require intervention, whereas others may be managed conservatively or deemed clinically insignificant. Challenging cases often benefit from a multimodality imaging approach combining the morphology, enhancement and metabolic features.

Short conclusion

Knowledge of the relevant clinical details and key imaging features is crucial for accurate characterization and differentiation of renal lesions.

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.

Chromophobe Renal Cell Carcinoma with Radiologic-Pathologic Correlation

Renal cell carcinoma (RCC) is a heterogeneous group of neoplasms derived from the renal tubular epithelial cells. Chromophobe RCC (chRCC) is the third most common subtype of RCC, accounting for 5% of cases. chRCC may be detected as an incidental finding or less commonly may manifest with clinical symptoms. The mainstay of therapy for chRCC is surgical resection. chRCC has a better prognosis compared with the more common clear cell RCC. At gross pathologic analysis, chRCC is a solid well-defined mass with lobulated borders. Histologic findings vary by subtype but include large pale polygonal cells with abundant transparent cytoplasm, crinkled "raisinoid" nuclei with perinuclear halos, and prominent cell membranes. Pathologic analysis reveals only moderate vascularity. The most common imaging pattern is a predominantly solid renal mass with circumscribed margins and enhancement less than that of the renal cortex. The authors discuss chRCC with emphasis on correlative pathologic findings and illustrate the multimodality imaging appearances of chRCC by using cases from the Radiologic Pathology Archives of the American Institute for Radiologic Pathology. ^©RSNA, 2021.

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.

The Incidental Renal Mass- Update on Characterization and Management

Renal masses are commonly encountered on cross-sectional imaging examinations performed for nonrenal indications. Although most can be dismissed as benign cysts, a subset will be either indeterminate or suspicious; in many cases, imaging cannot be used to reliably differentiate between benign and malignant masses. On-going research in defining characteristics of common renal masses on advanced imaging shows promise in offering solutions to this issue. A recent update of the Bosniak classification (used to categorize cystic renal masses) was proposed with the goals of decreasing imaging follow-up in likely benign cystic masses, and therefore avoiding unnecessary surgical resection of such masses.

The role of imaging in the management of renal masses

The wide availability of cross-sectional imaging is responsible for the increased detection of small, usually asymptomatic renal masses. More than 50 % of renal cell carcinomas (RCCs) represent incidental findings on noninvasive imaging. Multimodality imaging, including conventional US, contrast-enhanced US (CEUS), CT and multiparametric MRI (mpMRI) is pivotal in diagnosing and characterizing a renal mass, but also provides information regarding its prognosis, therapeutic management, and follow-up. In this review, imaging data for renal masses that urologists need for accurate treatment planning will be discussed. The role of US, CEUS, CT and mpMRI in the detection and characterization of renal masses, RCC staging and follow-up of surgically treated or untreated localized RCC will be presented. The role of percutaneous image-guided ablation in the management of RCC will be also reviewed.

A review of clinical and MR imaging features of renal lipid-poor angiomyolipomas

BACKGROUND

Lipid-poor angiomyolipomas (lpAMLs) constitute up to 5% of renal angiomyolipomas and are challenging to differentiate from malignant renal lesions on imaging alone. This review aims to identify clinical and MRI features which can be utilized to improve specificity and diagnostic accuracy for detecting lpAMLs in patients being considered for active surveillance rather than intervention.

FINDINGS

Young age, female sex, and small lesion size are associated with lpAMLs in studies evaluating indeterminate renal lesions. The accuracy of criteria using T2-weighted imaging, diffusion-weighted imaging, chemical shift imaging, dynamic contrast enhancement, multiparametric imaging, and radiomics are reviewed. Low T2 signal intensity is a particularly important MRI feature for lpAML. In studies with low T2 signal intensity, homogeneous early enhancement is a typical feature with an arterial-to-delay enhancement ratio > 1.5. Intratumoral hemorrhage with decrease in signal intensity on in-phase chemical shift imaging may be particularly useful for differentiating papillary renal cell carcinomas from lpAMLs in low T2 signal intensity lesions. Combining clinical and multiparametric MRI features can result in near-perfect specificity for lpAML. In select patients, clinical and MRI features can result in a high specificity and diagnostic accuracy for lpAMLs. These lesions can be considered for active surveillance rather than invasive diagnostic and therapeutic procedures such as biopsy or surgery.

The role of diffusion-weighted MRI and contrast-enhanced MRI for differentiation between solid renal masses and renal cell carcinoma subtypes

PURPOSE

To assess the value of diffusion-weighted magnetic resonance imaging (DW-MRI) and contrast-enhanced MRI (CE-MRI) for differentiation between benign and malignant solid renal masses, renal cell carcinoma (RCC) subtypes, oncocytomas, and lipid-poor angiomyolipomas (LP-AML).

METHODS

Minimum or lowest 'apparent diffusion coefficient' (ADC₁) and representative ADC values (ADC₂) of 112 renal masses (n: 46 benign renal mass, n: 66 malignant renal mass) were measured on DW-MRI images (b 50, 400, 800 s/mm²). Signal intensity (SI) measurements were performed in normal renal parenchyma and most avid enhanced area of the renal masses at precontrast, corticomedullary, and nephrographic phases on CE-MRI. Contrast enhancement rate (CER) and contrast enhancement index (CEI) values of renal masses were compared between benign-malignant renal masses and RCC subtypes, oncocytomas, and LP-AMLs.

RESULTS

There was no significant difference between ADC₁, ADC₂ values, and SI of benign and malignant renal masses (p = 0.721, p = 0.255, p = 0.872). Mean ADC₁ and ADC₂ values of clear cell RCCs were significantly higher than nonclear cell RCCs (p = 0.005 p = 0.002). Mean CER value of clear cell RCCs was significantly higher than nonclear cell RCCs in nephrographic phase (p = 0.003). Mean CEI values of clear cell RCCs were significantly higher than nonclear cell RCCs in the corticomedullary and nephrographic phase (p = 0.027 vs. 0.008). LP-AMLs were differentiated from other renal masses with wash-out phenomenon.

CONCLUSION

Combined usage of ADC, SI, CER, and CEI values may be useful for discrimination between RCC subtypes, oncocytomas, and lipid-poor AMLs.

State of the art in abdominal MRI structured reporting: a review

In the management of several abdominal disorders, magnetic resonance imaging (MRI) has the potential to significantly improve patient's outcome due to its diagnostic accuracy leading to more appropriate treatment choice. However, its clinical value heavily relies on the quality and quantity of diagnostic information that radiologists manage to convey through their reports. To solve issues such as ambiguity and lack of comprehensiveness that can occur with conventional narrative reports, the adoption of structured reporting has been proposed. Using a checklist and standardized lexicon, structured reports are designed to increase clarity while assuring that all key imaging findings related to a specific disorder are included. Unfortunately, structured reports have their limitations too, such as risk of undue report simplification and poor template plasticity. Their adoption is also far from widespread, and probably the ideal balance between radiologist autonomy and report consistency of has yet to be found. In this article, we aimed to provide an overview of structured reporting proposals for abdominal MRI and of works assessing its value in comparison to conventional free-text reporting. While for several abdominal disorders there are structured templates that have been endorsed by scientific societies and their adoption might be beneficial, stronger evidence confirming their imperativeness and added value in terms of clinical practice is needed, especially regarding the improvement of patient outcome.

Wunderlich Syndrome: Wonder What It Is

Wunderlich syndrome (WS) refers to spontaneous renal or perinephric hemorrhage occurring in the absence of known trauma. WS is much less common than hemorrhage occurring after iatrogenic or traumatic conditions. Lenk's triad of acute onset flank pain, flank mass, and hypovolemic shock is a classic presentation of WS but seen in less than a quarter of patients. The majority of patients present only with isolated flank pain and often imaged with an unenhanced CT in the emergency department. The underlying etiology is varied with most cases attributed to neoplasms, vascular disease, cystic renal disease and anticoagulation induced; the etiology is often occult on the initial exam and further evaluation is necessary. Urologists are familiar with this unique entity but radiologists, who are more likely to be the first to diagnose WS, may not be familiar with the imaging work up and management options. In the last decade or so, there has been a conspicuous shift in the approach to WS and thus it will be worthwhile to revisit WS in detail. In our review, we will review the multimodality imaging approach to WS, describe optimal follow up and elaborate on management.

Association of Tumor Size with Risk of Lymph Node Metastasis in Clear Cell Renal Cell Carcinoma: A Population-Based Study

The purpose of this article was to explore the association of tumor size with lymph node metastases (LNM) risk in patients with clear cell renal cell carcinoma (ccRCC). Based on the Surveillance, Epidemiology, and End Result (SEER) database, patients diagnosed with ccRCC from 1988 to 2015 were included in this study. For each patient, personal characteristics, clinicopathological data, and survival outcomes were, respectively, collected. Subsequently, the odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to investigate the potential risk factors for LNM in ccRCC. Finally, Kaplan-Meier (KM) survival plots of overall survival (OS) and ccRCC-specific survival (CSS) were evaluated on the basis of different tumor sizes. A total of 8,292 patients were finally enrolled in the study, 1,170 of whom (14.11%) had LNM. According to the heatmap, we could intuitively interpret that larger tumor size was related to an increased risk of LNM obviously. The risk of LNM was evidently greater for larger tumor size (4-7 cm: OR = 2.415, 95% CI = 1.708–3.415; 7–10 cm: OR = 3.746, 95% CI = 2.677–5.242; and >10 cm: OR = 4.617, 95% CI = 3.302–6.457) compared with smaller tumor size (≤4 cm). According to the KM survival plots of OS and CSS, we observed a gradual decline in survival with increasing tumor size, while the smallest tumor size had the best survival outcomes. These results indicated the positive relationship of tumor size with risk of LNM in ccRCC. And we also noticed continual decrease survival rates of OS and CSS with increasing tumor size.

Dual-Phase 99mTc-MIBI SPECT/CT in the Characterization of Enhancing Solid Renal Tumors: A Single-Institution Study of 147 Cases

PURPOSE

The aim of this study was to investigate the value of dual-phase Tc-MIBI SPECT/CT in the differential diagnosis between benign and malignant enhancing solid renal tumors.

PATIENTS AND METHODS

Totally, 180 patients were imaged with dual-phase Tc-MIBI SPECT/CT, which was performed 30 minutes and 90 minutes after Tc-MIBI administration. Among them, 147 patients with 148 histologically proved solid renal tumors met the selection criteria and were included for the final analysis. Relative quantification was performed by measuring the radioactive uptake ratio of tumor to the normal renal parenchymal background for both early and delayed images.

RESULTS

Benign renal tumors (4 renal oncocytomas and 8 lipid-poor angiomyolipomas) demonstrated a significantly higher early relative uptake value (ERUV) and delayed relative uptake value (DRUV) than malignant renal tumors (n = 136; both P < 0.0001). The ERUV cutoff value of 0.53 helped to differentiate benign from malignant renal tumors, with sensitivity of 100%, specificity of 94.8%, and accuracy of 95.3% for the diagnosis of benign renal tumors. The DRUV cutoff value of 0.50 helped to differentiate benign from malignant renal tumors, with sensitivity of 100%, specificity of 96.3%, and accuracy of 96.6% for the diagnosis of benign renal tumors. There was no statistically significant difference between the efficacy of ERUV and DRUV in the differential diagnosis between benign and malignant renal tumors (P = 0.5). The efficacies of ERUV and DRUV were all significantly higher than the retention index (both P < 0.0001).

CONCLUSIONS

Both early and delayed phase Tc-MIBI SPECT/CT are helpful for distinguishing benign renal oncocytoma and lipid-poor angiomyolipoma from malignant renal tumors, and the delayed phase imaging tends to show higher diagnostic accuracy.

Fumarate hydratase as a therapeutic target in renal cancer

INTRODUCTION

Renal cell carcinoma (RCC) is a heterogeneous group of cancers that can occur sporadically or as a manifestation of various inherited syndromes. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is one such inherited syndrome that predisposes patients to HLRCC-associated RCC. These tumors are notoriously aggressive and often exhibit early metastases. HLRCC results from germline mutations in the FH gene, which encodes the citric acid cycle enzyme fumarate hydratase (FH). FH loss leads to alterations in oxidative carbon metabolism, necessitating a switch to aerobic glycolysis, as well as a pseudohypoxic response and consequent upregulation of various pro-survival pathways. Mutations in FH also alter tumor cell migratory potential, response to oxidative stress, and response to DNA damage.

AREAS COVERED

We review the mechanisms by which FH loss leads to HLRCC-associated RCC and how these mechanisms are being rationally targeted.

EXPERT OPINION

FH loss results in the activation of numerous salvage pathways for tumor cell survival in HLRCC-associated RCC. Tumor heterogeneity requires individualized characterization via next-generation sequencing, ultimately resulting in HLRCC-specific treatment regimens. As HLRCC-associated RCC represents a classic Warburg tumor, targeting aerobic glycolysis is particularly promising as a future therapeutic avenue.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis

RATIONALE AND OBJECTIVES

This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis.

MATERIALS AND METHODS

Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest.

RESULTS

The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively.

CONCLUSION

ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.

"Renal emergencies: a comprehensive pictorial review with MR imaging"

Superior soft-tissue contrast and high sensitivity of magnetic resonance imaging (MRI) for detecting and characterizing disease may provide an expanded role in acute abdominal and pelvic imaging. Although MRI has traditionally not been exploited in acute care settings, commonly used in biliary obstruction and during pregnancy, there are several conditions in which MRI can go above and beyond other modalities in diagnosis, characterization, and providing functional and prognostic information. In this manuscript, we highlight how MRI can help in further assessment and characterization of acute renal emergencies. Currently, renal emergencies are predominantly evaluated with ultrasound (US) or computed tomography (CT) scanning. US may be limited by various patient factors and technologist experience while CT imaging with intravenous contrast administration can further compromise renal function. With the advent of rapid, robust non-contrast MRI, and magnetic resonance angiography (MRA) imaging studies with short scan times, free-breathing techniques, and lack of ionization radiation, the utility of MRI for renal evaluation might be superior to CT not only in diagnosing an emergent renal process but also by providing functional and prognostic information. This review outlines the clinical manifestations and the key imaging findings for acute renal processes including acute renal infarction, hemorrhage, and renal obstruction, among other entities, to highlight the added value of MRI in evaluating the finer nuances in acute renal emergencies.

Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics

PURPOSE

Solid renal masses (SRM) are difficult to differentiate based on standard MR features. The purpose of this study was to assess MR imaging features of SRM to evaluate performance of ensemble methods of classifying SRM subtypes.

MATERIALS AND METHODS

MR images of SRM (n = 330) were retrospectively evaluated for standard and multiparametric (mp) features. Models of MR features for predicting malignant and benign lesions as well as subtyping SRM were developed using a training dataset and performance was evaluated in a test data-set using recursive partitioning (RP), gradient booting machine (GBM), and random forest (RF) methods.

RESULTS

In the test dataset, GBM and RF models demonstrated an accuracy of 86% (95% CI 75% to 93%) for predicting benign versus malignant SRM compared to 83% (95% CI 71% to 91%) for the RP model. RF had the greatest accuracy in predicting SRM subtypes, 81.2% (95% CI 69.5% to 89.9%) compared with GBM 73.4% (95% CI 60.9% to 83.7%) or RP 70.3% (95% CI 57.6% to 81.1%). Marginal homogeneity was reduced by the RF model compared with the RP model (P < 0.001), but not the GBM model (P = 0.135). All models had high sensitivity and specificity for clear cell and papillary renal cell carcinomas (RCC), but performed less well in differentiating chromophobe RCC, oncocytomas, and fat-poor angiomyolipomas.

CONCLUSION

Ensemble methods for prediction of SRM from radiologist-assessed image characteristics have high accuracy for distinguishing benign and malignant lesions. SRM subtype classification is limited by the ability to categorize chromophobe RCCs, oncocytomas, and fat-poor angiomyolipomas.