Prevalence of 'Fat-Poor' Adrenal Adenomas at Chemical-Shift MRI.

OBJECTIVE: : To determine the prevalence of 'fat-poor' adrenal adenomas at chemical-shift-MRI. MATERIALS AND METHODS: : This prospective IRB approved study identified 104 consecutive patients with 127 indeterminate adrenal masses that underwent 1.5-T chemical-shift-MRI between 2021-2023. Two blinded radiologists independently measured: 1) 2-Dimensionsal (2D) chemical-shift signal intensity (SI)-index on 2D Chemical-shift-MRI (SI-index >16.5% diagnosed presence of microscopic fat), 2) unenhanced CT attenuation (in cases where unenhanced CT was available). RESULTS: : From 127 adrenal masses, there were 94% (119/127) adenomas and 6% (8/127) other masses (2 pheochromocytoma, 5 metastases, 1 lymphoma). 98% (117/119) adenomas had SI-Index >16.5%, only 2% (2/119) adenomas were 'fat-poor' by MRI. SI-Index >16.5% was 100% specific for adenoma, all other masses had SI-Index <16.5%. Unenhanced CT was available in 43% (55/127) lesions (50 adenomas, 5 other masses). 34% (17/50) adenomas were lipid-poor (>10 HU). Percentage of adenomas with SI-Index >16.5% were: 1) ≤10 HU, 100% (33/33), 2) 11-29 HU, 100% (12/12), 3) ≥30 HU, 60% (3/5). No other masses had attenuation ≤10 HU (0/5). CONCLUSION: : Fat-poor adrenal adenomas are uncommon using 2D chemical-shift signal intensity index >16.5% at 1.5-T, occurring in approximately 2% of adenomas in this large prospective series.

Evaluation of a multiparametric renal CT algorithm for diagnosis of clear-cell renal cell carcinoma among small (≤ 4 cm) solid renal masses.

OBJECTIVE: To evaluate a recently proposed CT-based algorithm for diagnosis of clear-cell renal cell carcinoma (ccRCC) among small (≤ 4 cm) solid renal masses diagnosed by renal mass biopsy. METHODS: This retrospective study included 51 small renal masses in 51 patients with renal-mass CT and biopsy between 2014 and 2021. Three radiologists independently evaluated corticomedullary phase CT for the following: heterogeneity and attenuation ratio (mass:renal cortex), which were used to inform the CT score (1-5). CT score ≥ 4 was considered positive for ccRCC. Diagnostic accuracy was calculated for each reader and overall using fixed effects logistic regression modelling. RESULTS: There were 51% (26/51) ccRCC and 49% (25/51) other masses. For diagnosis of ccRCC, area under curve (AUC), sensitivity, specificity, and positive predictive value (PPV) were 0.69 (95% confidence interval 0.61-0.76), 78% (68-86%), 59% (46-71%), and 67% (54-79%), respectively. CT score ≤ 2 had a negative predictive value 97% (92-99%) to exclude diagnosis of ccRCC. For diagnosis of papillary renal cell carcinoma (pRCC), CT score ≤ 2, AUC, sensitivity, specificity, and PPV were 0.89 (0.81-0.98), 81% (58-94%), 98% (93-99%), and 85% (62-97%), respectively. Pooled inter-observer agreement for CT scoring was moderate (Fleiss weighted kappa = 0.52). CONCLUSION: The CT scoring system for prediction of ccRCC was sensitive with a high negative predictive value and moderate agreement. The CT score is highly specific for diagnosis of pRCC. CLINICAL RELEVANCE STATEMENT: The CT score algorithm may help guide renal mass biopsy decisions in clinical practice, with high sensitivity to identify clear-cell tumors for biopsy to establish diagnosis and grade and high specificity to avoid biopsy in papillary tumors. KEY POINTS: • A CT score ≥ 4 had high sensitivity and negative predictive value for diagnosis of clear-cell renal cell carcinoma (RCC) among solid ≤ 4-cm renal masses. • A CT score ≤ 2 was highly specific for diagnosis of papillary RCC among solid ≤ 4-cm renal masses. • Inter-observer agreement for CT score was moderate.

Utility of multiparametric renal CT for differentiation of low-grade from high-grade cT1a clear cell renal cell carcinoma.

OBJECTIVE: To determine if CT can differentiate low-grade from high-grade clear cell renal cell carcinoma (ccRCC) in cT1a solid ccRCC. METHODS AND MATERIALS: This retrospective cross-sectional study evaluated 78 < 4 cm solid (>25% enhancing) ccRCC in 78 patients with renal CT within 12 months of surgery between January 2016 and December 2019. Two radiologists (R1/R2), blinded to pathology, independently recorded mass:size, calcification, attenuation and heterogeneity (5-point Likert scale) and recorded a 5-point ccRCC CT Score. Multivariate logistic regression (LR) was performed. RESULTS: There were 64.1% (50/78) low-grade (5/50 Grade 1 and 45/50 Grade 2) and 35.9% (28/78) high-grade (27/28 Grade 3 and 1/28 Grade 4) tumors.Unenhanced CT attenuation was higher (35.9±10.3 R1 and 34.9±10.7 R2 high-grade vs 29.7±10.2 R1 and 29.5±9.8 R2 low-grade, p=0.01-0.02), absolute corticomedullary phase attenuation ratio (CMphase-ratio; 0.67±0.16 R1 and 0.66±0.16 R2 vs 0.93±0.83 R1 and 0.80±0.33 R2, p=0.04-0.05) and 3-tiered stratification of CMphase-ratio (p=0.02) lower in high-grade tumors.A two-variable LR-model including unenhanced CT attenuation and CM.phase-ratio achieved area under the receiver operator characteristic curve of: 73% (95% confidence intervals 59-86%) and 72% (59-84%) for R1 and R2.ccRCC CT score differed by ccRCC grade (p<0.01 R1, R2) with high-grade tumors occurring most commonly in moderately enhancing ccRCC score 4 (46.4% [13/28] R1 and 54% [15/28]). CONCLUSION: Among cT1a ccRCC, high-grade tumors have higher unenhanced CT attenuation and are less avidly enhancing. ADVANCES IN KNOWLEDGE: High-grade ccRCC have higher attenuation (possibly due to less microscopic fat) and lower corticomedullary phase enhancement compared to low-grade tumors. This may result in categorization of high-grade tumors in lower ccRCC diagnostic algorithm categories.

Proposed Modifications to the Multiparametric CT Score for Diagnosis of ccRCC Using Hyperattenuation, Segmental Enhancement Inversion, and ADER: A Secondary Analysis.

In a secondary analysis of 148 small (≤ 4 cm) solid renal masses, none of three CT features (hyperattenuation on unenhanced images, segmental enhancement inversion, or arterial-to-delayed enhancement ratio) significantly improved the performance of a multiparametric CT score for the diagnosis of clear cell renal cell carcinoma.

Development of a Multiparametric Renal CT Algorithm for Diagnosis of Clear Cell Renal Cell Carcinoma Among Small (≤ 4 cm) Solid Renal Masses.

BACKGROUND. The MRI clear cell likelihood score predicts the likelihood that a renal mass is clear cell renal cell carcinoma (ccRCC). A CT-based algorithm has not yet been established. OBJECTIVE. The purpose of our study was to develop and evaluate a CT-based algorithm for diagnosing ccRCC among small (≤ 4 cm) solid renal masses. METHODS. This retrospective study included 148 patients (73 men, 75 women; mean age, 58 ± 12 [SD] years) with 148 small (≤ 4 cm) solid (> 25% enhancing tissue) renal masses that underwent renal mass CT (unenhanced, corticomedullary, and nephrographic phases) before resection between January 2016 and December 2019. Two radiologists independently evaluated CT examinations and recorded calcification, mass attenuation in all phases, mass-to-cortex corticomedullary attenuation ratio, and heterogeneity score (score on a 5-point Likert scale, assessed in corticomedullary phase). Features associated with ccRCC were identified by multivariable logistic regression analysis and then used to create a five-tiered CT score for diagnosing ccRCC. RESULTS. The masses comprised 53% (78/148) ccRCC and 47% (70/148) other histologic diagnoses. The mass-to-cortex corticomedullary attenuation ratio was higher for ccRCC than for other diagnoses (reader 1: 0.84 ± 0.68 vs 0.68 ± 0.65, p = .02; reader 2: 0.75 ± 0.29 vs 0.59 ± 0.25, p = .02). The heterogeneity score was higher for ccRCC than other diagnoses (reader 1: 4.0 ± 1.1 vs 1.5 ± 1.6, p < .001; reader 2: 4.4 ± 0.9 vs 3.3 ± 1.5, p < .001). Other features showed no difference. A five-tiered diagnostic algorithm including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score had interobserver agreement of 0.71 (weighted κ) and achieved an AUC for diagnosing ccRCC of 0.75 (95% CI, 0.68-0.82) for reader 1 and 0.72 (95% CI, 0.66-0.82) for reader 2. A CT score of 4 or greater achieved sensitivity, specificity, and PPV of 71% (95% CI, 59-80%), 79% (95% CI, 67-87%), and 79% (95% CI, 67-87%) for reader 1 and 42% (95% CI, 31-54%), 81% (95% CI, 70-90%), and 72% (95% CI, 56-84%) for reader 2. A CT score of 2 or less had NPV of 85% (95% CI, 69-95%) for reader 1 and 88% (95% CI, 69-97%) for reader 2. CONCLUSION. A five-tiered renal CT algorithm, including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score, had substantial interobserver agreement, moderate AUC and PPV, and high NPV for diagnosing ccRCC. CLINICAL IMPACT. The CT algorithm, if validated, may represent a useful clinical tool for diagnosing ccRCC.

The association between adrenal adenoma size, autonomous cortisol secretion and metabolic derangements.

OBJECTIVE: Autonomous cortisol secretion (ACS) is common in patients with adrenal incidentalomas (AI). ACS is associated with increased cardiovascular morbidity and mortality. Data regarding the association between radiological characteristics of adrenal adenomas, their hormonal functionality and metabolic outcomes, are scarce and inconclusive. In this study, we aim to delineate the association between radiological characteristics of AI, ACS and metabolic status. METHODS: A cross-sectional study of 77 patients with AI who underwent a comprehensive hormonal evaluation. Radiological assessments were performed by an independent radiologist blinded to the clinical and hormonal phenotype of each case. Linear regression models were used to evaluate the association between post dexamethasone suppression test (DST) cortisol levels, metabolic indices and radiological measurements. RESULTS: Mean maximal adenoma diameter was greater in patients with versus without ACS (20.35 ± 6 vs. 27.09 ± 9.3 mm, respectively, p < .01). Maximal adenoma diameter was found to be positively and linearly correlated with post-DST morning cortisol levels across their entire range (R = .474, p < .01). Linear correlations between maximal adenoma diameter and indices of glycemic control showed a correlation coefficient (R) of .481 and .463 for fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c), respectively, p < .01. When analysis included only patients with ACS, an R = .584 and R = .565 was observed for FPG and HbA1c, respectively (p < .01 for both). The association between maximal adenoma diameter and both FPG and post-DST morning cortisol intensified in patients with metabolic syndrome. CONCLUSION: There is a quantitative positive mild correlation between AI size and both cortisol autonomy and metabolic parameters.

Association between Enlarged Axillary Lymph Nodes and Silicone Breast Implant Ruptures seen on Magnetic Resonance Imaging.

BACKGROUND: Silicone breast augmentation is a common cosmetic surgery. Previous case reports demonstrated lymphadenopathy in the presence of implant ruptures. OBJECTIVES: To investigate the association between enlarged axillary lymph nodes and silicone implant ruptures as seen on breast magnetic resonance imaging (MRI). METHODS: Two groups were derived retrospectively from breast MRI reports in our institution for the period December 2011-May 2014. A search of our hospital records for "silicone" and "lymph node" was performed (group A), and the relationship between the presence of enlarged nodes and ruptures was evaluated. The prevalence of ruptures in the presence of nodes was calculated and the association between MRI imaging features and ruptures evaluated. A search for "silicone" and "implant rupture" was performed (group B) and, as for group A, the relationship between the presence of ruptures and nodes was evaluated and the prevalence of enlarged nodes in the presence of ruptures calculated. RESULTS: Group A comprised 45 women with enlarged nodes. Intracapsular ruptures were associated with nodes (P = 0.005), while extracapsular ruptures showed a trend of association with nodes (P = 0.08). The prevalence of ruptures in the presence of nodes was 31.4%. Nodes associated with ruptures showed a strong silicone signal (P = 0.008) and absent enhancement (P = 0.005). Group B comprised 73 women with ruptures. Enlarged nodes were associated with both intra- and extracapsular ruptures (P < 0.001 and P = 0.002 respectively). The prevalence of nodes in the presence of ruptures was 22.2%. CONCLUSIONS: Enlarged axillary nodes were associated with ruptures in two groups of patients. This finding can guide clinical decisions when either enlarged nodes or ruptures are encountered in patients with silicone implants. The association between silicone lymphadenopathy and implant rupture raises concerns regarding the role of rupture in silicone-induced systemic disease.