Association of Clear Cell Likelihood Score on MRI and Growth Kinetics of Small Solid Renal Masses on Active Surveillance

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.

Characterization of Demographical Histologic Diversity in Small Renal Masses With the Clear Cell Likelihood Score

OBJECTIVE

This study aimed to develop a diagnostic model to estimate the distribution of small renal mass (SRM; ≤4 cm) histologic subtypes for patients with different demographic backgrounds and clear cell likelihood score (ccLS) designations.

MATERIALS AND METHODS

A bi-institution retrospective cohort study was conducted where 347 patients (366 SRMs) underwent magnetic resonance imaging and received a ccLS before pathologic confirmation between June 2016 and November 2021. Age, sex, race, ethnicity, socioeconomic status, body mass index (BMI), and the ccLS were tabulated. The socioeconomic status for each patient was determined using the Area Deprivation Index associated with their residential address. The magnetic resonance imaging-derived ccLS assists in the characterization of SRMs by providing a likelihood of clear cell renal cell carcinoma (ccRCC). Pathological subtypes were grouped into four categories (ccRCC, papillary renal cell carcinoma, other renal cell carcinomas, or benign). Generalized estimating equations were used to estimate probabilities of the pathological subtypes across different patient subgroups.

RESULTS

Race and ethnicity, BMI, and ccLS were significant predictors of histology (all P < 0.001). Obese (BMI, ≥30 kg/m 2 ) Hispanic patients with ccLS of ≥4 had the highest estimated rate of ccRCC (97.1%), and normal-weight (BMI, <25 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 had the lowest (0.2%). The highest estimated rates of papillary renal cell carcinoma were found in overweight (BMI, 25-30 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 (92.3%), and the lowest, in obese Hispanic patients with ccLS ≥4 (<0.1%).

CONCLUSIONS

Patient race, ethnicity, BMI, and ccLS offer synergistic information to estimate the probabilities of SRM histologic subtypes.

Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI

INTRODUCTION

Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel-Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics.

MATERIAL AND METHODS

We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups-'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models.

RESULTS

This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90.

CONCLUSION

This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population.

Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score

PURPOSE

Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC.

METHODS

We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)].

RESULTS

In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1-2) and 2 to 5 (76.7% in scores 4-5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780).

CONCLUSION

The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.

Insights into Renal Cell Carcinoma with Novel Imaging Approaches

This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.

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

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

Cystic Renal Masses: Old and New Paradigms

Cystic renal masses describe a spectrum of lesions with benign and/or malignant features. Cystic renal masses are most often identified incidentally with the Bosniak classification system stratifying their malignant potential. Solid enhancing components most often represent clear cell renal cell carcinoma yet display an indolent natural history relative to pure solid renal masses. This has led to an increased adoption of active surveillance as a management strategy in those who are poor surgical candidates. This article provides a contemporary overview of historical and emerging clinical paradigms in the diagnosis and management of this distinct clinical entity.

Radiologist's Disease: Imaging for Renal Cancer

There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.

Growth Kinetics of Pancreatic Neuroendocrine Neoplasms by Histopathologic Grade

OBJECTIVES

The aims of the study are to describe the growth kinetics of pathologically proven, treatment-naive pancreatic neuroendocrine neoplasms (panNENs) at imaging surveillance and to determine their association with histopathologic grade and Ki-67.

METHODS

This study included 100 panNENs from 95 patients who received pancreas protocol computed tomography or magnetic resonance imaging from January 2005 to July 2022. All masses were treatment-naive, had histopathologic correlation, and were imaged with at least 2 computed tomography or magnetic resonance imaging at least 90 days apart. Growth kinetics was assessed using linear and specific growth rate, stratified by grade and Ki-67. Masses were also assessed qualitatively to determine other possible imaging predictors of grade.

RESULTS

There were 76 grade 1 masses, 17 grade 2 masses, and 7 grade 3 masses. Median (interquartile range) linear growth rates were 0.06 cm/y (0-0.20), 0.40 cm/y (0.22-1.06), and 2.70 cm/y (0.41-3.89) for grade 1, 2, and 3 masses, respectively (P < 0.001). Linear growth rate correlated with Ki-67 with r2 of 0.623 (P < 0.001). At multivariate analyses, linear growth rate was the only imaging feature significantly associated with grade (P = 0.009).

CONCLUSIONS

Growth kinetics correlate with Ki-67 and grade. Grade 1 panNENs grow slowly versus grade 2-3 panNENs.

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.

Growth Kinetics and Progression Rate of Bosniak Classification, Version 2019 III and IV Cystic Renal Masses on Imaging Surveillance

Background: Active surveillance is increasingly used as first-line management for localized renal masses. Triggers for intervention primarily reflect growth kinetics, which are poorly investigated for cystic masses defined by Bosniak classification version 2019 (v2019). Objective: To determine growth kinetics and incidence rates of progression of class III and IV cystic renal masses, as defined by Bosniak classification v2019. Methods: This retrospective study included 105 patients (68 men, 37 women; median age, 67 years) with 112 Bosniak v2019 class III or IV cystic renal masses on baseline renal-mass protocol CT or MRI examinations from January 2005 to September 2021. Mass dimensions were measured. Progression was defined as any of: linear growth rate (LGR) ≥5 mm per year (representing clinical guideline threshold for intervention), volume doubling time <1 year, T category increase, or N1 or M1 disease. Class III and IV masses were compared. Time-to-progression was estimated using Kaplan-Meier curve analysis. Results: At baseline, 58 masses were class III and 54 were class IV. Median follow-up was 406 days. Median LGR was for class III masses 0.0 mm per year [interquartile range (IQR) -1.3 to 1.8] and for class IV masses 2.3 mm per year (IQR 0.0¬¬-5.7) (p<.001). LGR exceeded 5 mm per year in 4 (7%) class 3 masses and 15 (28%) class IV masses (p=.005). Two patients, both with class IV masses, developed distant metastases. Incidence rate of progression was for class III masses 11.0 (95% CI 4.5-22.8) and for class IV masses 73.6 (95% CI 47.8-108.7) per 100,000 person-days of follow-up. Median time-to-progression was undefined for class III mases given small number of progression events and 710 days for class IV masses. Hazard ratio of progression for class IV relative to class III masses was 5.1 (95% CI 2.5-10.8) (p<.001). Conclusion: During active surveillance of cystic masses evaluated using Bosniak classification v2019, class IV masses grew faster and were more likely to progress than class III masses. Clinical Impact: In comparison with current active surveillance guidelines that treat class III and IV masses similarly, future iterations may incorporate relatively more intensive surveillance for class IV masses.