Characterization Of Renal Masses Research Articles

Molecular imaging of renal cell carcinomas: ready for prime time.

The clinical diagnosis of renal cell carcinoma (RCC) is constantly evolving. Diagnostic imaging of RCC relying on enhanced computed tomography (CT) and magnetic resonance imaging (MRI) is commonly used for renal mass characterization and assessment of tumour thrombosis, whereas pathology is the gold standard for establishing diagnosis. However, molecular imaging is rapidly improving the clinical management of RCC, particularly clear-cell RCC. Molecular imaging aids in the non-invasive visualization and characterization of specific biomarkers such as carbonic anhydrase IX and CD70 within the tumours, which help to assess tumour heterogeneity and status. Target-specific molecular imaging of RCCs will substantially improve the diagnostic landscape of RCC and will further facilitate clinical decision-making regarding initial staging and re-staging, monitoring of recurrence and metastasis, patient stratification and selection, and the prediction and evaluation of treatment responses.

Imaging in Renal Cell Carcinoma Detection.

Imaging in renal cell carcinoma (RCC) is a constantly evolving landscape. The incidence of RCC has been rising over the years with the improvement in image quality and sensitivity in imaging modalities resulting in "incidentalomas" being detected. We aim to explore the latest advances in imaging for RCC. A literature search was conducted using Medline and Google Scholar, up to May 2024. For each subsection of the manuscript, a separate search was performed using a combination of the following key terms "renal cell carcinoma", "renal mass", "ultrasound", "computed tomography", "magnetic resonance imaging", "18F-Fluorodeoxyglucose PET/CT", "prostate-specific membrane antigen PET/CT", "technetium-99m sestamibi SPECT/CT", "carbonic anhydrase IX", "girentuximab", and "radiomics". Studies that were not in English were excluded. The reference lists of selected manuscripts were checked manually for eligible articles. The main imaging modalities for RCC currently are ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI). Contrast-enhanced US (CEUS) has emerged as an alternative to CT or MRI for the characterisation of renal masses. Furthermore, there has been significant research in molecular imaging in recent years, including FDG PET, PSMA PET/CT, 99mTc-Sestamibi, and anti-carbonic anhydrase IX monoclonal antibodies/peptides. Radiomics and the use of AI in radiology is a growing area of interest. There will be significant change in the field of imaging in RCC as molecular imaging becomes increasingly popular, which reflects a shift in management to a more conservative approach, especially for small renal masses (SRMs). There is the hope that the improvement in imaging will result in less unnecessary invasive surgeries or biopsies being performed for benign or indolent renal lesions.

Value of elastography in characterization of solid renal masses

BackgroundSolid renal masses often come to light as incidental findings during abdominal ultrasound examinations. Once detected, determining whether a renal mass is benign or malignant becomes imperative for informed decision-making regarding management and treatment strategies. In this investigation, the aim was to explore the diagnostic efficacy of real-time strain sonoelastography in assessing solid renal masses.MethodsThis prospective research was steered on 26 individuals diagnosed with a solid renal mass, as endorsed by pathological analysis after surgical removal or biopsy. Elastography was performed on all patients. The measurement of strain index values for tissues was achieved by placing regions of interest of equal or near-equal size on both the tumor (A) and the adjacent normal renal cortex (B).ResultsStrain elastography showed no correlation with patient’s age, size of mass and probe to mass distance with p > 0.05 all. Sensitivity analysis showed that strain index can significantly predict malignant renal masses (P = 0.003) using a cut-off point 2, with 92.9% area under curve, 95.2% sensitivity, 80% specificity, 80% negative predictive value, 95.2% positive predictive value, and overall diagnostic accuracy 92.3%. Strain index > 2 was an independent predictor for malignant renal masses (P = 0.025), odds ratio 7.29 when adjusting for other risk factors. Malignant renal masses were significantly higher strain index compared to benign lesions with (P = 0.001).ConclusionsStrain elastography is a valuable technique for distinguishing between malignant and benign solid renal tumors. Benign lesions have lower strain index values compared to malignant ones, making the strain index a useful screening tool for distinguishing between benign and malignant renal masses using cut-off point 2.

Diagnostic accuracy of the Clear Cell Likelihood Score and selected MRI parameters in the characterization of indeterminate renal masses – a single-institution study

PurposeWe aimed to assess the diagnostic accuracy of the clear cell likelihood score (ccLS) and value of other selected magnetic resonance imaging (MRI) features in the characterization of indeterminate small renal masses (SRMs).MethodsFifty patients with indeterminate SRMs discovered on MRI between 2012 and 2023 were included. The ccLS for the characterization of clear cell renal cell carcinoma (ccRCC) was calculated and compared to the final diagnosis (ccRCC vs. ‘all other’ masses).ResultsThe ccLS = 5 had a satisfactory accuracy of 64.0% and a very high specificity of 96.3%; however, its sensitivity of 26.1% was relatively low. Receiver operating curve (ROC) analysis revealed that from the selected MRI features, only T1 ratio and arterial to delayed enhancement (ADER) were good discriminators between ccRCC and other types of renal masses (area under curve, AUC = 0.707, p = 0.01; AUC = 0.673, p = 0.03; respectively). The cut-off points determined in ROC analysis using the Youden index were 0.73 (p = 0.01) for T1 ratio and 0.99 for ADER (p = 0.03). The logistic regression model demonstrated that ccLS = 5 and T1 ratio (OR = 15.5 [1.1-218.72], p = 0.04; OR = 0.002 [0.00-0.81], p = 0.04) were significant predictors of ccRCC.ConclusionsThe ccLS algorithm offers an encouraging method for the standardization of imaging protocols to aid in the diagnosis and management of SRMs in daily clinical practice by enhancing detectability of ccRCC and reducing the number of unnecessary invasive procedures for benign or indolent lesions. However, its diagnostic performance needs multi-center large cohort studies to validate it before it can be incorporated as a diagnostic algorithm and will guide future iterations of clinical guidelines. The retrospective nature of our study and small patient population confined to a single clinical center may impact the generalizability of the results; thus, future studies are required to define whether employment of the T1 ratio or ADER parameter may strengthen the diagnostic accuracy of ccRCC diagnosis.Graphical

A Prospective Study of the Diagnostic Performance of Photon-Counting CT Compared With MRI in the Characterization of Renal Masses.

The aim of this study was to assess the interreader reliability and per-RCC sensitivity of high-resolution photon-counting computed tomography (PCCT) in the detection and characterization of renal masses in comparison to MRI. This prospective study included 24 adult patients (mean age, 52 ± 14 years; 14 females) who underwent PCCT (using an investigational whole-body CT scanner) and abdominal MRI within a 3-month time interval and underwent surgical resection (partial or radical nephrectomy) with histopathology (n = 70 lesions). Of the 24 patients, 17 had a germline mutation and the remainder were sporadic cases. Two radiologists (R1 and R2) assessed the PCCT and corresponding MRI studies with a 3-week washout period between reviews. Readers recorded the number of lesions in each patient and graded each targeted lesion's characteristic features, dimensions, and location. Data were analyzed using a 2-sample t test, Fisher exact test, and weighted kappa. In patients with von Hippel-Lindau mutation, R1 identified a similar number of lesions suspicious for neoplasm on both modalities (51 vs 50, P = 0.94), whereas R2 identified more suspicious lesions on PCCT scans as compared with MRI studies (80 vs 56, P = 0.12). R1 and R2 characterized more lesions as predominantly solid in MRIs (R1: 58/70 in MRI vs 52/70 in PCCT, P < 0.001; R2: 60/70 in MRI vs 55/70 in PCCT, P < 0.001). R1 and R2 performed similarly in detecting neoplastic lesions on PCCT and MRI studies (R1: 94% vs 90%, P = 0.5; R2: 73% vs 79%, P = 0.13). The interreader reliability and per-RCC sensitivity of PCCT scans acquired on an investigational whole-body PCCT were comparable to MRI scans in detecting and characterizing renal masses. PCCT scans have comparable performance to MRI studies while allowing for improved characterization of the internal composition of lesions due to material decomposition analysis. Future generations of this imaging modality may reveal additional advantages of PCCT over MRI.

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.

Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.

To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. 32 Patients (23M/9F; age 61.8 ± 10.6years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73cm) undergoing resection were prospectively recruited. All patients underwent 1.5T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.

Utility of Chemical Shift Imaging and Diffusion-weighted Images/Apparent Diffusion Coefficient Maps as a Tool for Evaluation of Solid Renal Tumors.

To study the utility of chemical shift imaging (CSI) and diffusion-weighted images (DWI)/apparent diffusion coefficient (ADC) maps for the evaluation of solid renal tumors. Magnetic resonance imaging (MRI) has an equivalent application as computerized tomography (CT) in the characterization of renal masses. It offers a radiation-free imaging technique and has a better soft tissue contrast than CT. Also, MRI is favored in patients with chronic kidney disease. MRI is useful when findings on CT are equivocal. The role of DWI in characterizing solid renal lesions as malignant is encouraging, and DWI can be particularly useful when gadolinium is contraindicated. CSI is useful in differentiating angiomyolipoma (AML) from clear cell (cc) renal cell carcinoma (RCC). We did a cross-sectional study on 24 patients with solid renal masses. MRI of the upper abdomen (from the dome of the diaphragm to the iliac crest) will be done on an MRI machine in our department (1.5T, ACHIEVA, Phillips medical system) using the torso coil. There was no significant association seen in terms of ADC values and histological subtypes (χ2 = 11.222, p = 0.082). In our study, 50% (one out of two) of AML showed a signal drop, whereas 40% of cases (6 out of 15) of ccRCC and 66% (two out of three) of papillary RCC showed a signal drop. In this article, we concluded CSI, although a useful tool to look for microscopic fat, can't be used as a reliable marker to rule in cc-carcinoma as both AML and papillary cell carcinoma have microscopic fat. Further, no histological classification can be done on the basis of DWI/ADC images.

The Use of Radiomic Tools in Renal Mass Characterization.

The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.

Ultrasound Elastography for the Differentiation of Benign and Malignant Solid Renal Masses: A Systematic Review and Meta-Analysis

The incidental finding of small renal masses in CT and MRI examinations can present a diagnostic challenge. Renal cell carcinoma (RCC) and angiomyolipoma (AML) are the most common incidental malignant and benign renal lesions but may present with similar US features. US elastography is a non-invasive technique that can assess tissue elasticity, has shown promising results in many clinical settings, and could be able to differentiate between benign and malignant renal lesions based on tissue stiffness. The purpose of this article is to systematically review the applications of US elastography in the characterization of solid renal masses and to derive and compare the summary estimates of different stiffness values across different lesion subtypes. In December 2022, a systematic search was carried out on the MEDLINE (PubMed) and EMBASE databases to retrieve studies on the application of US elastography in the characterization of solid renal masses. After article selection by three researchers, 14 studies entered qualitative synthesis. A total of 1190 patients were included, and the elastography data of 959 lesions were examined: 317/959 (33%) benign and 642/959 (67%) malignant. Among the malignancies, 590 (91%) were RCC, whereas, among the 317 benign lesions, 244 (77%) were AML. All lesions were classified using a histopathological (biopsy or operative specimen) or imaging (US follow-up/CT/MRI) reference standard. After data extraction and methodological quality evaluation, quantitative synthesis was performed on 12 studies, 4 using strain elastography (SE) and 8 using shear wave elastography (SWE), with single- and double-arm random-effects meta-analyses. Lesion stiffness measured with SE was available in four studies, with an RCC strain ratio higher than the AML strain ratio both in an indirect comparison (Cochran’s Q test p = 0.014) and in a direct comparison (p = 0.021). Conversely, the SWE measurements of RCC and AML stiffness did not significantly differ either at an indirect comparison (p = 0.055) or direct comparison (p = 0.114).

Evaluation of a novel Re-VASC score to differentiate malignant and benign T1a renal masses.

e16548 Background: Early characterization of renal cell carcinoma (RCC) is pertinent for cancer prognosis and patient treatment. However, characterization of renal masses as benign or malignant on computed tomography (CT) remains a difficult clinical challenge. Angiogenesis plays a major role in RCC progression and assessing neovascularity in tumor-burdened kidneys with Re-VASC scoring has been shown to aptly predict tumor pathology and staging. In the current study, we evaluated if Re-VASC can reliably differentiate between malignant T1a ( &lt; 4cm) RCC and benign T1a renal masses. Methods: All eligible patients had a diagnosis of T1a from pathologic or clinical evaluation. The inclusion criteria for malignant masses included a diagnosis of RCC by pathology after partial or radical nephrectomy. Our T1a benign group was comprised of patients with a diagnosis of a non-malignant renal mass (angiomyolipoma or oncocytoma) established via histopathology or CT imaging (fat containing lesions). Re-VASC score was calculated on axial preoperative contrast enhanced CT utilizing the system described below: 0 = No visible neovascularization 1 = visualized single new vessel &lt; 3mm in diameter 2 = visualized single new vessel ≥3mm in diameter 3 = visualized multiple new vessels &lt; 3mm in diameter 4 = visualized multiple new vessels and at least one vessel ≥3mm in diameter. Results: A total of 64 benign and 69 malignant T1a tumors were included. Comparison of the two groups demonstrated no significant differences with regards to age, sex, ASA, and BMI. The average Re- VASC scores was 0.159 and 0.753 for benign and malignant masses respectively (p &lt; 0.001). Additionally, the tumor size for benign masses was 2.373 and for malignant masses was 2.546 (p = 0.210). There was no significant difference in Re-VASC score between the contralateral kidneys of malignant and benign kidney masses (p &lt; 0.508). Additionally, the sensitivity, specificity and positive predictive values were 26%, 86% and 74% respectively, when utilizing a Re-VASC score of 1 as a cutoff for malignancy. Conclusions: Our results show a significantly higher Re-VASC score in T1a malignant tumors and T1a benign tumors. While biopsy remains the gold standard for confirmation of malignant tumors such as RCC, radiological evaluation with tumor scoring systems like Re-VASC may offer a useful clinical decision-making tool in a less-invasive manner. Because renal biopsy carries non-negligible risk of complications, reducing volume of biopsies among low-risk patients may lead to better overall care. The Re-VASC score shows potential as a confirmatory test with a specificity of 86%. We would like to apply the Re-VASC score to an expanded multi-center trial, in order to assess its translatability to the general population.

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.

Role of renal mass biopsy for diagnosis and management: Review of current trends and future directions.

The frequency of detection of renal masses has increased over recent decades, causing a concurrent increase in early intervention by surgery. Growing recognition that this approach was contributing to overtreatment led to the broader use of preoperative renal mass biopsy (RMB) by core biopsy and/or fine-needle aspiration. Because more options for management, such as active surveillance and personalized therapy, are becoming increasingly available, a diagnosis by RMB is becoming a valuable tool for risk stratification and clinical decision making. Guidelines from various professional organizations have outlined situations in which RMB should be used, and it has been shown to be safe and effective. Rapid on-site evaluation (ROSE) using touch preparations of core biopsy or fine-needle aspiration smears provides an immediate assessment of adequacy and appropriate triage. ROSE also ensures sufficient material to perform immunohistochemistry and molecular studies for more accurate characterization of renal masses and personalized treatment. The integral role of cytopathology laboratories in precision medicine can also be successfully used in optimizing the workup of RMB from ROSE to final diagnosis, prognostication, and personalized management of kidney tumors. Herein, the authors review their extensive experience working together with interventional radiology and urology colleagues to use core biopsy and ROSE at the time of RMB for diagnosis and management of these lesions.

Role of multiparametric MRI in characterization of complicated cystic renal masses

BackgroundBosniak classification improves sensitivity and specificity for malignancy among cystic renal masses characterized with MRI. The quantitative parameters derived from diffusion-weighted imaging, and contrast enhancement, can be used in distinguishing between benign and malignant cystic renal masses.MethodsThis prospective observational study included 58 patients (39 male and 19 female) with complicated cystic renal mass initially diagnosed by US or CT. All patients underwent multiparametric MRI study (Pre- and Post-Gd-enhanced T1WI, T2WI and DWI) by using 3 Tesla MRI scanner. Each cystic renal lesion was assigned a category based on Bosniak classification. Demographic data were recorded. ADC ratio, dynamic enhancement parameters in both corticomedullary and nephrographic phases as well as absolute washout were calculated and compared using ROC curve analysis.ResultsThe sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of the multiparametric MRI in categorization of cystic renal masses according to Bosniak classification version 2019 were 90.32%, 100%, 100%, 90% and 94.83%, respectively, which was higher compared to biparametric MRI and conventional MRI.ConclusionsMultiparametric MRI can be utilized to confidently evaluate cystic renal masses, overcoming the traditional limitations of overlapping morphological imaging features. Quantitative parameters derived from multiparametric MRI allow better evaluation of complex cystic renal tumors to distinguish between benign and malignant complex cystic renal lesions.

The Role of CT Imaging in Characterization of Small Renal Masses.

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

Renal lesion characterization: clinical utility of single-phase dual-energy CT compared to MRI and dual-phase single-energy CT.

To assess the impact of dual-energy CT (DECT) utilization in practice by measuring the readers' confidence, the need for additional image requests, and diagnostic performance in renal lesion assessment, compared to single-energy CT (SECT) using contrast-enhanced MRI to establish the reference standard. Sixty-nine patients (M/F = 47/22) who underwent a dual-phase renal SECT (n = 34) or DECT (n = 35) and had a contrast-enhanced MRI within 180 days were retrospectively collected. Three radiologists assessed images on different sessions (SECT, DECT, and MRI) for (1) likely diagnosis (enhancing/non-enhancing); (2) diagnostic confidence (5-point Likert scale); (3) need for additional imaging test (yes/no); and (4) need for follow-up imaging (yes/no). Diagnostic accuracy was compared using AUC; p value < 0.05 was considered significant. One hundred fifty-six lesions consisting of 18% enhancing (n = 28/156, mean size: 30.37 mm, range: 9.9-94 mm) and 82% non-enhancing (n = 128/156, mean size: 23.91 mm, range: 5.0-94.2 mm) were included. The confidence level was significantly lower for SECT than their MRI (4.50 vs. 4.80, p value < 0.05) but not significantly different for DECT and the corresponding MRI (4.78 vs. 4.78, p > 0.05). There were significantly more requests for additional imaging in the SECT session than the corresponding MRI (20% vs. 4%), which was not significantly different between DECT and their MRI counterpart session (5.7% vs. 4.9%). Inter-reader agreement was almost perfect for DECT and MRI (kappa: 0.8-1) and substantial in SECT sessions (kappa: 0.6-0.8) with comparable diagnostic accuracy between SECT, DECT, and MRI (p value > 0.05). Single-phase DECT allows confident and reproducible characterization of renal masses with fewer recommendation for additional and follow-up imaging tests than dual-phase SECT and a performance similar to MRI. • DECT utilization leads to similar additional image requests to MRI (5.7% vs. 4.9%, p value > 0.05), whereas single-energy CT utilization leads to significantly higher image requests (20% vs. 4%, p value < 0.05). • DECT and MRI utilization bring highly reproducible results with almost perfect inter-reader agreement (kappa: 0.8-1), better than the inter-reader agreement in SECT utilization (kappa: 0.6-0.8). • Readers' confidence was not significantly altered between DECT and their MRI readout session (p value > 0.05). In contrast, confidence in the diagnosis was significantly lower in the SECT session than their MRI readout (p value < 0.05).

Diagnostic Performance and Interreader Agreement of the MRI Clear Cell Likelihood Score for Characterization of cT1a and cT1b Solid Renal Masses: An External Validation Study.

BACKGROUND. The clear cell likelihood score (ccLS) has been proposed for the noninvasive differentiation of clear cell renal cell carcinoma (ccRCC) from other renal neoplasms on multiparametric MRI (mpMRI), though further external validation remains needed. OBJECTIVE. The purpose of our study was to evaluate the diagnostic performance and interreader agreement of the ccLS version 2.0 (v2.0) for characterizing solid renal masses as ccRCC. METHODS. This retrospective study included 102 patients (67 men, 35 women; mean age, 56.9 ± 12.8 [SD] years) who underwent mpMRI between January 2013 and February 2018, showing a total of 108 (≥ 25% enhancing tissue) solid renal masses measuring 7 cm or smaller (83 cT1a [≤ 4 cm] and 25 cT1b [> 4 cm and ≤ 7 cm]), all with a histologic diagnosis. Three abdominal radiologists independently reviewed the MRI examinations using ccLS v2.0. Median reader sensitivity, specificity, and accuracy were computed for predicting ccRCC by ccLS of 4 or greater, and individual reader AUCs were derived. The percentage of masses that were ccRCC was calculated, stratified by ccLS. Interobserver agreement was assessed by the Fleiss kappa statistic. RESULTS. The sample included 45 ccRCCs (34 cT1a, 11 cT1b), 30 papillary renal cell carcinomas (RCCs), 13 chromophobe RCCs, 14 oncocytomas, and six fat-poor angiomyolipomas. Median reader sensitivity, specificity, and accuracy for predicting ccRCC by ccLS of 4 or greater were 85%, 82%, and 83% among cT1a masses and 82%, 100%, and 92% among cT1b masses. The three readers' AUCs for predicting ccRCC by ccLS for cT1a masses were 0.90, 0.84, and 0.89 and for cT1b masses were 0.99, 0.97, and 0.92. Across readers, the percentage of masses that were ccRCC among cT1a masses was 0%, 0%, 20%, 68%, and 93% for ccLS of 1, 2, 3, 4, and 5, respectively; among cT1b masses, the percentage of masses that were ccRCC was 0%, 0%, 32%, 90%, and 100% for ccLS of 1, 2, 3, 4, and 5, respectively. Interobserver agreement among cT1a and cT1b masses for ccLS of 4 or greater was 0.82 and 0.83 and for ccLS of 1-5 overall was 0.65 and 0.62, respectively. CONCLUSION. This study provides external validation of the ccLS, finding overall high measures of diagnostic performance and interreader agreement. CLINICAL IMPACT. The ccLS provides a standardized approach to the noninvasive diagnosis of ccRCC by MRI.

Shear wave elastography in the characterization of renal cell carcinoma and angiomyolipoma.

Detection and characterization of renal lesions are common in daily clinical practice. To investigate the effectiveness of shear wave elastography (SWE), a novel radiological examination technique, in the characterization of renal masses. The study included a total of 68 patients (33 men, 35 women; mean age = 57.71 ± 12.08 years; age range = 19-83 years) who underwent SWE. SWE measurements were obtained at depths of 2-8 cm from the probe surface in two different positions from an analysis window of approximately 0.5 × 1.0 cm on ultrasound. The cutoff SWE was calculated for the differentiation of renal cell carcinoma (RCC) and angiomyolipoma (AML) by receiver operating characteristic (ROC) analysis. When the result was statistically significant, the sensitivity, specificity, accuracy, and positive and negative predictive values of the test were calculated. Mass-to-parenchyma SWE ratios of RCCs were significantly higher than those of AMLs (P = 0.003). In ROC curve analysis, the SWE cutoff was 1.215 m/s to differentiate RCCs from AMLs. The area under the ROC curve was calculated as 0.74 (95% CI = 0.610-0.871, sensitivity = 70.7%, specificity = 70.6%, positive predictive value = 87.8%, negative predictive value = 44.4%). The SWE technique is increasingly used and may be useful in distinguishing RCC and AML lesions, and especially clear cell and non-clear cell RCCs.

Diagnostic Accuracy of Magnetic Resonance Imaging in Detecting Clear Renal Cell Carcinoma, Taking Histopathology as Gold Standard

Background: Presurgical staging and characterization of renal masses can be accomplished with magnetic resonance imaging (MRI). Objective: Present study aims to determine the diagnostic accuracy of magnetic resonance imaging in detecting clear renal cell carcinoma, taking histopathology as gold standard. Study design: Descriptive, cross-sectional study. Study duration and settings: Study was conducted at department of Diagnostic Radiology, Jinnah Hospital, Lahore from 4th June 2018 to 3rd December 2018. Material and methods: A total of 133 patients with clear renal cell carcinoma on ultrasonography having age 20-60 years of either genders were enrolled. Patients with pregnancy, previous nephrectomy and contraindicated for MR studies were excluded. MRI abdomen was performed in every patient using the 1.5 Tesla MR system with gradient strength of 33mT/m. MRI findings were correlated with histopathology findings. Results: MRI of abdomen pelvis was performed in all patients and there were 68 True Positive and six False Positive results observed . Among 59 patients whose MRI was negative, 06 patients (False Negative) found clear renal cell carcinoma on histopathology, but True Negative patients had no clear RCC (p=0.0001).Overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of diagnostic accuracy of magnetic resonance imaging in detecting clear renal cell carcinoma was 91.89%, 89.83%, 91.89%, 89.83% and 90.98% respectively Conclusion: This study concluded that magnetic resonance imaging is a highly sensitive and accurate non-invasive modality for diagnosing clear cell renal cell carcinoma.. Keywords: Renal cell carcinoma, magnetic resonance imaging, sensitivity