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.

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

BACKGROUND. The lack of validated imaging markers to characterize biologic aggressiveness of small renal masses (SRMs)-defined as those categorized as cT1a and 4 cm and smaller-hinders medical decision-making among available initial management strategies. OBJECTIVE. The purpose of this article was to explore the association of the clear cell likelihood score (ccLS) on MRI with growth rates and progression of SRMs. METHODS. This retrospective study included consecutive SRMs assigned a ccLS on clinical MRI examinations performed between June 2016 and November 2019 at an academic tertiary-care medical center or its affiliated safety net hospital system. The ccLS reports the likelihood that the SRM represents clear cell renal cell carcinoma (ccRCC) from 1 (very unlikely) to 5 (very likely). The ccLS was extracted from clinical reports. Tumor size measurements were extracted from available prior and follow-up cross-sectional imaging examinations, through June 2020. Serial tumor size measurements were fit to linear and exponential growth curves. Estimated growth rates were grouped by the assigned ccLS. Tumor progression was defined by development of large size (> 4 cm in at least two consecutive measurements) and/or rapid growth (doubling of volume within 1 year). Differences among ccLS groups were evaluated using Kruskal-Wallis tests. Correlations between ccLS and growth rate were evaluated by Spearman correlation (ρ). RESULTS. Growth rates of 386 SRMs (100 ccLS 1-2, 75 ccLS 3, and 211 ccLS 4-5) from 339 patients (median age, 65 years; 198 men, 141 women) were analyzed. Median follow-up was 1.2 years. The ccLS was correlated with growth rates by size (ρ = 0.19; p < .001; ccLS 4-5, 9%/year; ccLS 1-2, 5%/year; p < .001) and by volume (ρ = 0.14; p = .006; ccLS 4-5, 29%/year; ccLS 1-2, 16%/year; p < .001). Disease progression (observed in 49 SRMs) was not significantly associated with ccLS group (p = .61). Two patients (0.6%) developed metastases during active surveillance: one ccLS 1 was a type 2 papillary renal cell carcinoma and one ccLS 4 was ccRCC. CONCLUSION. Growth is associated with ccLS in SRMs, with higher ccLS correlating with faster growth. CLINICAL IMPACT. SRMs with lower ccLS may be considered for active surveillance, whereas SRMs with higher ccLS may warrant earlier intervention. The noninvasive ccLS derived from MRI correlates with growth rate of SRMs and may help guide personalized management.

Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging.

OBJECTIVES: Solid renal masses have unknown malignant potential with commonly utilized imaging. Biopsy can offer a diagnosis of cancer but has a high non-diagnostic rate and complications. Reported use of multiparametric magnetic resonance imaging (mpMRI) to diagnose aggressive histology (i.e., clear cell renal cell carcinoma (ccRCC)) via a clear cell likelihood score (ccLS) was based on retrospective review of cT1a tumors. We aim to retrospectively assess the diagnostic performance of ccLS prospectively assigned to renal masses of all stages evaluated with mpMRI prior to histopathologic evaluation. METHODS: In this retrospective cohort study from June 2016 to November 2019, 434 patients with 454 renal masses from 2 institutions with heterogenous patient populations underwent mpMRI with prospective ccLS assignment and had pathologic diagnosis. ccLS performance was assessed by contingency table analysis. The association between ccLS and ccRCC was assessed with logistic regression. RESULTS: Mean age and tumor size were 60 ± 13 years and 5.4 ± 3.8 cm. Characteristics were similar between institutions except for patient age and race (both p < 0.001) and lesion laterality and histology (both p = 0.04). The PPV of ccLS increased with each increment in ccLS (ccLS1 5% [3/55], ccLS2 6% [3/47], ccLS3 35% [20/57], ccLS4 78% [85/109], ccLS5 93% [173/186]). Pooled analysis for ccRCC diagnosis revealed sensitivity 91% (258/284), PPV 87% (258/295) for ccLS ≥ 4, and specificity 56% (96/170), NPV 94% (96/102) for ccLS ≤ 2. Diagnostic performance was similar between institutions. CONCLUSIONS: We confirm the optimal diagnostic performance of mpMRI to identify ccRCC in all clinical stages. High PPV and NPV of ccLS can help inform clinical management decision-making. KEY POINTS: • The positive predictive value of the clear cell likelihood score (ccLS) for detecting clear cell renal cell carcinoma was 5% (ccLS1), 6% (ccLS2), 35% (ccLS3), 78% (ccLS4), and 93% (ccLS5). Sensitivity of ccLS ≥ 4 and specificity of ccLS ≤ 2 were 91% and 56%, respectively. • When controlling for confounding variables, ccLS is an independent risk factor for identifying clear cell renal cell carcinoma. • Utilization of the ccLS can help guide clinical care, including the decision for renal mass biopsy, reducing the morbidity and risk to patients.

Dynamic range adaptation in primary motor cortical populations.

Neural populations from various sensory regions demonstrate dynamic range adaptation in response to changes in the statistical distribution of their input stimuli. These adaptations help optimize the transmission of information about sensory inputs. Here, we show a similar effect in the firing rates of primary motor cortical cells. We trained monkeys to operate a brain-computer interface in both two- and three-dimensional virtual environments. We found that neurons in primary motor cortex exhibited a change in the amplitude of their directional tuning curves between the two tasks. We then leveraged the simultaneous nature of the recordings to test several hypotheses about the population-based mechanisms driving these changes and found that the results are most consistent with dynamic range adaptation. Our results demonstrate that dynamic range adaptation is neither limited to sensory regions nor to rescaling of monotonic stimulus intensity tuning curves, but may rather represent a canonical feature of neural encoding.