Lipid-poor Angiomyolipoma Research Articles

Is fat quantification based on proton density fat fraction useful for differentiating renal tumor types?

This study retrospectively assessed the diagnostic accuracy of fat quantification based on proton density fat fraction (PDFF) for differentiating renal tumors. In this retrospective study, 98 histologically confirmed clear cell renal cell carcinomas (ccRCCs), 35 papillary renal cell carcinomas (pRCCs), 14 renal oncocytomas, 16 chromophobe renal cell carcinomas (chRCCs), 10 lymphomas, 19 uroepithelial tumors, 10 lipid-poor angiomyolipomas (AMLs), and 25 lipid-rich AMLs were identified in 226 patients (127 males and 99 females) over 5years. All patients underwent multiparametric kidney MRI. The MRI protocol included an axial plane and a volumetric 3D fat fraction sequence known as mDIXON-Quant for PDFF measurement. Demographic data were recorded, and PDFF values were independently reviewed by two radiologists blinded to pathologic results. MRI examinations were performed using a 1.5T system. MRI-PDFF measurements were obtained from the solid parts of all renal tumors. Fat quantification was performed using a standard region of interest for each tumor, compared to histopathological diagnoses. Sensitivity and specificity analyses were performed to calculate the diagnostic accuracy for each histopathological tumor type. Nonparametric variables were compared among the subgroups using the Kruskal-Wallis H test and Mann Whitney U test. P-values < 0.05 were considered statistically significant. In all, 102 patients underwent partial nephrectomy, 70 patients underwent radical nephrectomy, and the remaining 54 had biopsies. Patient age (mean: 58.11years; range: 18-87years) and tumor size (mean: 29.5mm; range: 14-147mm) did not significantly differ across groups. All measurements exhibited good interobserver agreement. The mean ccRCC MRI-PDFF was 12.6 ± 5.06% (range: 11.58-13.61%), the mean pRCC MRI-PDFF was 2.72 ± 2.42% (range: 2.12-3.32%), and the mean chRCC MRI-PDFF was 1.8 ± 1.4% (range: 1.09-2.5%). Clear cell RCCs presented a significantly higher fat ratio than other RCC types, uroepithelial tumors, lymphomas, and lipid-poor AMLs (p < 0.05). Lipid-rich AMLs demonstrated a very high fat ratio. MRI-PDFF facilitated accurate differentiation of ccRCCs from other renal tumors with high sensitivity and specificity.

Feasibility and Reproducibility of T2 Mapping Compared with Diffusion-Weighted Imaging in Solid Renal Masses.

Accurate prediction of renal mass subtypes, along with the WHO/ISUP grade and pathological T (pT) stage of clear cell renal cell carcinoma (ccRCC), is crucial for optimal decision making. Our study aimed to investigate the feasibility and reproducibility of motion-robust radial T2 mapping in differentiating lipid-poor angiomyolipoma (MFAML) from RCC and characterizing the WHO/ISUP grade and pT stage of ccRCC. Finally, 92 patients undergoing renal radial T2 mapping and ZOOMit DWI were recruited. The T2 values and apparent diffusion coefficient (ADC) were analyzed. Correlation coefficients were calculated between ADC and T2 values. Notably, ccRCC exhibited higher T2 and ADC values than MFAML (p < 0.05). T2 values were lower in the higher WHO/ISUP grade and pT stage of ccRCC (all p < 0.05). ADC showed no significant difference for pT stage (p = 0.056). T2 values revealed a higher area under the curve (AUC) in evaluating the WHO/ISUP grade compared to ADC (0.936 vs. 0.817, p = 0.027). T2 values moderately positively correlated with ADC (r = 0.675, p < 0.001). In conclusion, quantitative motion-robust radial T2 mapping is feasible for characterizing solid renal masses and could provide additional value for multiparametric imaging in predicting WHO/ISUP grade and pT stage of ccRCC.

Small Renal Masses: Developing a Robust Radiomic Signature.

(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38-87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.

Distinguishing lipid-poor angiomyolipoma from renal carcinoma using tumor shape.

To validate the "overflowing beer sign" (OBS) for distinguishing between lipid-poor angiomyolipoma (AML) and renal cell carcinoma, and to determine whether it improves the detection of lipid-poor AML when added to the angular interface sign, a previously-validated morphologic feature associated with AML. Retrospective nested case-control study of all 134 AMLs in an institutional renal mass database matched 1:2 with 268 malignant renal masses from the same database. Cross-sectional imaging from each mass was reviewed and the presence of each sign was identified. A random selection of 60 masses (30 AML and 30 benign) was used to measure interobserver agreement. Both signs were strongly associated with AML in the total population (OBS: OR 17.4 95% CI 8.0-42.5, p < 0.001; angular interface: OR 12.6, 95% CI 5.9-29.7, p < 0.001) and the population of patients excluding those with visible macroscopic fat (OBS: OR 11.2, 95% CI 4.8-28.7, p < 0.001; angular interface: 8.5, 95% CI 3.7-21.1, p < 0.001). In the lipid-poor population, the specificity of both signs was excellent (OBS: 95.6%, 95% CI 91.9%-98%; angular interface: 95.1%, 95% CI 91.3%-97.6%). Sensitivity was low for both signs (OBS: 31.4%, 95% CI 24.0-45.4%; angular interface: 30.5%, 95% CI 20.8%-41.6%). Both signs showed high levels of inter-rater agreement (OBS 90.0% 95% CI 80.5 - 95.9; angular interface 88.6, 95% CI 78.7-94.9) Testing for AML using the presence of either sign in this population improved sensitivity (39.0%, 95% CI 28.4%-50.4%, p = 0.023) without significantly reducing specificity (94.2%, 95% CI 90%-97%, p = 0.2) relative to the angular interface sign alone. Recognition of the OBS increases the sensitivity of detection of lipid-poor AML without significantly reducing specificity.

Diagnostic performance of the “drooping” sign in CT diagnosis of exophytic renal angiomyolipoma

To evaluate the prevalence of angular interface and the "drooping" sign in exophytic renal angiomyolipomas (AMLs) and the diagnostic performance in differentiating exophytic lipid-poor AMLs from other solid renal masses. This IRB-approved, two-center study included 185 patients with 188 exophytic solid renal masses < 4cm with histopathology and pre-operative CT within 30days of surgical resection or biopsy. Images were reviewed for the presence of angular interface and the "drooping" sign qualitatively by three readers blinded to the final diagnosis, with majority rules applied. Both features were assessed quantitatively by cohort creators (who are not readers) independently. Free-marginal kappa was used to assess inter-reader agreement and agreement between two methods assessing each feature. Fisher's exact test, Mann-Whitney test, and multivariable logistic regression with two-tailed p < 0.05 were used to determine statistical significance. Diagnostic performance was assessed. Ninety-four patients had 96 AMLs, and 91 patients had 92 non-AMLs. Seventy-four (77%) of AMLs were lipid-poor based on quantitative assessment on CT. The presence of angular interface and the "drooping" sign by both qualitative and quantitative assessment were statistically significantly associated with AMLs (39% (qualitative) and 45% (quantitative) vs 15% (qualitative) and 13% (quantitative), and 48% (qualitative) and 43% (quantitative) vs 4% (qualitative) and 1% (quantitative), respectively, all p < 0.001) in univariable analysis. In multivariable analysis, only the "drooping" sign in either qualitative or quantitative assessment was a statistically significant predictor of AMLs (both p < 0.001). Inter-reader agreement for the "drooping" sign was moderate (k = 0.55) and for angular interface was fair (k = 0.33). Agreement between the two methods of assessing the "drooping" sign was substantial (k = 0.84) and of assessing the angular interface was moderate (k = 0.59). The "drooping" sign both qualitatively and quantitatively, alone or in combination of angular interface, had very high specificity (96-100%) and positive predictive value (PPV) (89-100%), moderate negative predictive value (62-68%), but limited sensitivity (23-49%) for lipid-poor AMLs. The "drooping" sign by both qualitative and quantitative assessment is highly specific for lipid-rich and lipid-poor AMLs. This feature alone or in combination with angular interface can aid in CT diagnosis of lipid-poor AMLs with very high specificity and PPV.

Imaging features of renal lipid-poor angiomyolipomas: a pictorial review

Imaging features of renal lipid-poor angiomyolipomas: a pictorial review

Prevalence of thoracoabdominal imaging findings in tuberous sclerosis complex

BackgroundTuberous sclerosis complex (TSC) results in neurodevelopmental phenotypes, benign tumors, and cysts throughout the body. Recent studies show numerous rare findings in TSC. Guidelines suggest routine abdominal and chest imaging to monitor these thoracoabdominal findings, but imaging is not uniformly done across centers. Thus, the prevalence of many findings is unknown. To answer this, we categorized the clinical reads of 1398 thoracoabdominal scans from 649 patients of all ages in the Cincinnati Children’s Hospital TSC Repository Database.ResultsTypical TSC findings were present in many patients: kidney cysts (72%), kidney fat-containing angiomyolipomas (51%), kidney lipid-poor angiomyolipomas (27%), liver angiomyolipomas (19%), and lung nodules thought to represent multifocal micronodular pneumocyte hyperplasia (MMPH) (18%). While many features were more common in TSC2 patients, TSC1 patients had a higher prevalence of MMPH than TSC2 patients (24% versus 13%, p = 0.05). Many rare findings (e.g., lymphatic malformations and liver masses) are more common in TSC than in the general population. Additionally, most thoracoabdominal imaging findings increased with age except kidney cysts which decreased, with the 0–10 years age group having the highest percentage (69% 0–10 years, 49% 10–21 years, 48% 21 + years, p < 0.001). Finally, in our population, no patients had renal cell carcinoma found on abdominal imaging.ConclusionsThese results show that regular thoracoabdominal scans in TSC may show several findings that should not be ignored or, conversely, over-reacted to when found in patients with TSC. Female sex, TSC2 mutation, and age are risk factors for many thoracoabdominal findings. The data suggest novel interactions of genetic mutation with pulmonary nodules and age with renal cysts. Finally, in agreement with other works, these findings indicate that several rare thoracoabdominal imaging findings occur at higher rates in the TSC population than in the general population. This work supports obtaining detailed thoracoabdominal imaging in patients with TSC.

CT imaging findings of renal epithelioid lipid-poor angiomyolipoma.

To identify specific imaging and clinicopathological features of a rare potentially malignant epithelioid variant of renal lipid-poor angiomyolipoma (E-lpAML). A total of 20 patients with E-lpAML and 43 patients with other lpAML were retrospectively included. Multiphase computed tomography (CT) imaging features and clinicopathological findings were recorded. Independent predictors for E-lpAML were identified using multivariate logistic regression and were used to construct a diagnostic score for differentiation of E-lpAML from other lpAML. The E-lpAML group consisted of 6 men and 14 women (age median ± SD: 39.45 ± 15.70, range: 16.0-68.0years). E-lpAML tended to appear as hyperdense mass lesions located at the renal sinus (n = 8, 40%) or at the renal cortex (n = 12, 60%), with a "fast-in and slow-out" enhancement pattern (n = 20, 100%), cystic degeneration (n = 18, 90%), "eyeball" sign (n = 11, 55%), and tumor neo-vasculature (n = 15, 75%) on CT. Multivariate logistic regression analysis showed that the independent predictors for diagnosing E-lpAML were cystic degeneration on CT imaging and CT value of the tumor in corticomedullary phase of enhancement. A predictive model was built with the two predictors, achieving an area under the curve (AUC) of 93.5% (95% confidence interval (95%CI): 84.3-98.2%) with a sensitivity of 95.0% (95%CI: 75.1-99.9%) and a specificity of 83.72% (95%CI: 69.3-93.2%). We identified specific CT imaging features and predictors that could contribute to the correct diagnosis of E-lpAML. Our findings should be helpful for clinical management of E-lpAML which could potentially be malignant and may require nephron-sparing surgery while other lpAML tumors which are benign require no intervention. • It is important to differentiate renal epithelioid lipid-poor angiomyolipoma (E-lpAML) from other lpAML because of differences in clinical management. • E-lpAML tumors tend to be large hyperdense tumors in the renal sinus with cystic degeneration and "fast-in and slow-out" pattern of enhancement. • Our CT imaging-based predictive model was robust in its performance for predicting E-lpAML from other lpAML tumors.

February 2022 Medical Image of the Month: Multifocal Micronodular Pneumocyte Hyperplasia in the Setting of Tuberous Sclerosis

No abstract available. Article truncated at 150 words. A 22-year-old man with a known diagnosis of Tuberous Sclerosis Complex (TSC) presents for a follow-up unenhanced chest CT to re-evaluate pulmonary nodules seen in the lung bases on a prior abdominal CT. The patient also has a history of multiple renal lesions consistent with lipid-poor angiomyolipomas based on previous abdominal MRI findings. The chest CT demonstrated multiple scattered, randomly distributed sub-centimeter pulmonary nodules of solid or subsolid morphology [Figure 1A]. Those nodules visible in the lung bases on the prior abdominal CT had remined stable over the 12-month interval consistent with multifocal microscopic pneumocyte hyperplasia (MMPH)[Figure 1B,C]. Myocardial fatty foci (MFF) were visualized in the heart on soft tissue window display settings [Figure 2A] as were multiple scattered sclerotic bone lesions on bone window display settings [Figure 2B,C]. No pulmonary cysts were seen to suggest lymphangioleiomyomatosis (LAM) and there were no thoracic lymphangiomas. MMPH represents a benign hamartomatous proliferation …

Role of MDCT in Assessment and Characterization of Renal Masses

Aim: The purpose of our study is to evaluate the ability of MDCT to differentiate renal masses based on CT characteristics and location and differentiate benign from malignant masses. Results: The most common benign neoplasm was renal cortical cyst, Bosniak 1 category. Normal renal cortex showed greatest enhancement in the parenchymal phase.The most common malignant neoplasm was Clear cell RCC and most common infectious lesion was renal abscess. Statistical significance (p&lt;0.005) amongst tumor groups were seen in age, tumor attenuation, tumor to kidney enhancement, mean absolute attenuation and de enhancement characteristics. Tumor to kidney enhancement ratios was indicative of how many times a tumor enhanced with respect to renal parenchyma and ranged from 0.0 for simple cystic lesions to 1.7 in malignant clear cell RCC. Statistical significance amongst tumor groups was seen between sex, lesion margins, calcification, lymphadenopathy, CT density and enhancement pattern. Presence of male gender, lymphadenopathy, a heterogeneous pattern of enhancement were factors in support of malignancy. A homogeneous pattern of enhancement, coarse calcification, regular tumor margins were consistent with benign etiology. The relative attenuation with adjacent renal cortex in vascular phase was maximum for clear cell RCC. Classical Angiomyolipoma were best diagnosed in the unenhanced phase with CT attenuation of &lt;-10 HU, diagnostic of macroscopic fat. Lipid poor angiomyolipomas had unenhanced attenuation of &gt;45 HU and a homogeneous parenchymal phase maximum enhancement.

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

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. 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

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). 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, 800s/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. 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. Combined usage of ADC, SI, CER, and CEI values may be useful for discrimination between RCC subtypes, oncocytomas, and lipid-poor AMLs.

MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma.

To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.

Performance of simultaneous multi-slice accelerated diffusion-weighted imaging for assessing focal renal lesions in pediatric patients with tuberous sclerosis complex.

Diffusion-weighted imaging (DWI) is a useful MRI technique to characterize abdominal lesions in children, but long acquisition times can lead to image degradation. Simultaneous multi-slice accelerated DWI is a promising technique to shorten DWI scan times. To test the feasibility of simultaneous multi-slice DWI of the kidneys in pediatric patients with tuberous sclerosis complex (TSC) and to evaluate the accelerated protocol regarding image quality and quantitative apparent diffusion coefficient (ADC) values compared to standard echoplanar DWI sequence. We included 33 children and adolescents (12 female, 21 male; mean age 10±5years) with TSC and renal cyst or angiomyolipoma on 3-tesla (T) MRI from 2017 to 2019. All studies included both free-breathing standard echoplanar DWI and simultaneous multi-slice DWI sequences. Subjective and quantitative image quality was evaluated using a predefined 5-point scale. ADC values were obtained for all renal cysts and angiomyolipomas ≥5mm. All statistical analysis was performed using Stata/SE v15.1. Simultaneous multi-slice DWI ADC values were slightly lower compared to standard echoplanar DWI for both renal cysts and angiomyolipomas (mean difference 0.05×10-3mm2/s, 95% confidence interval [CI] 0.40-0.50 and 0.024×10-3mm2/s, 95% CI 0.17-0.21, respectively, with P>0.1). Our results showed that renal lesions with ADC values >1.69×10-3mm2/s were all cysts, whereas lesions with values <1.16×10-3mm2/s were all angiomyolipomas. However, ADC values could not discriminate between lipid-rich and lipid-poor angiomyolipomas (P>0.1, for both sequences). A 55% reduction in scan time was achieved using simultaneous multi-slice DWI for abdominal imaging in children with TSC, with near identical image quality as standard DWI. These results suggest that multi-slice techniques should be considered more broadly as an MRI acceleration technique in children.

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

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. 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. 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). 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.

Diagnostic Performance of MRI in the Detection of Renal Lipid-Poor Angiomyolipomas: A Systematic Review and Meta-Analysis.

Background Lipid-poor angiomyolipomas (AMLs) are challenging to differentiate from other renal lesions at imaging and often necessitate biopsy or surgery. If sufficiently accurate, MRI may play a role as a replacement test for biopsy. Purpose To perform a systematic review to evaluate the diagnostic performance of MRI for lipid-poor AMLs in patients with renal masses. Materials and Methods A systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the "gray literature" (conference proceedings) was performed without language restriction through July 18, 2019, with the assistance of a health sciences librarian. Original articles with more than 10 patients evaluating the diagnostic performance of MRI, with histopathologic findings used as the reference standard, for the diagnosis of lipid-poor AMLs in patients with renal masses were included. Studies including AMLs with macroscopic fat and studies with insufficient data were excluded. Patient, clinical, MRI, and diagnostic performance parameters were independently acquired by two authors. Meta-analysis was performed by using a random-effects or bivariate mixed-effects regression model depending on the number of studies. Risk of bias of individual studies was evaluated by using Quality Assessment of Diagnostic Accuracy Studies-2. Results Twenty-three studies with 2196 patients and 25 contingency tables were included. The pooled sensitivity, specificity, and area under the receiver operating characteristic curve were 83% (95% confidence interval [CI]: 72%, 90%), 90% (95% CI: 84%, 94%), and 0.93 (95% CI: 0.91, 0.95), respectively. Considerable variability was present for several variables, including MRI parameters; however, subgroup analysis did not identify MRI sequence or field strength as sources for variability. All studies were at high risk of bias for index test domain because no reported thresholds were prespecified. Conclusion MRI shows promising accuracy for detecting lipid-poor angiomyolipomas (area under the receiver operating characteristic curve, >0.9), indicating a potential role as a replacement test for biopsy in selected patients. Studies evaluating MRI accuracy with a pragmatic algorithm and prespecified threshold may be helpful to confirm this potential role in the management pathway. © RSNA, 2020 Online supplemental material is available for this article.

Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network. In this institutional review board approved study of 143 patients with predominantly solid, lipid-poor, contrast enhancing renal lesions (46 benign and 97 malignant), patients with a pre-operative multiphase CECT of the abdomen and pelvis obtained between June 2009 and June 2015 were retrospectively queried. Benign renal masses included oncocytoma and lipid-poor angiomyolipoma and the malignant group included clear cell, papillary, and chromophobe carcinomas.Region of interests of whole tumor volumes were manually segmented, and CT phase images with the largest cross-section of the segmented tumor in the axial plane were used for assessment. Post-surgical pathological evaluation was used to establish diagnosis.The segmented images of renal masses were used as input to a CNN. The data were augmented and split into training (83.9%) and validation sets (16.1%) to determine the hyperparameters of the CNN. Thereafter. the performance of the resulting CNN was quantified using eightfold cross-validation. The CNN-based classifier demonstrated an overall accuracy of 78% (95% confidence interval: 76-80%), sensitivity of 70% (95% confidence interval: 66-74%), specificity of 81% (79-83%) and an area under the curve of 0.82. A CNN-based classifier to diagnose solid enhancing malignant renal masses based on multiphase CECT images was developed. It was established that a CNN-based classifier could be trained to accurately distinguish malignant renal lesions.

Diagnostic accuracy of signal loss in in-phase gradient-echo images for differentiation between small renal cell carcinoma and lipid-poor angiomyolipomas

To assess the diagnostic accuracy of signal loss on in-phase (IP) gradient-echo (GRE) images for differentiation between renal cell carcinomas (RCCs) and lipid-poor angiomyolipomas (lpAMLs). We retrospectively searched our institutional database for histologically proven small RCCs (<5.0 cm) and AMLs without visible macroscopic fat (lpAMLs). Two experienced radiologists assessed MRIs qualitatively, to depict signal loss foci on IP GRE images. A third radiologist drew regions of interest (ROIs) on the same lesions, on IP and out-of-phase (OP) images to calculate the ratio of signal loss. Diagnostic accuracy parameters were calculated for both techniques and the inter-reader agreement for the qualitative analysis was evaluated using the κ test. 15 (38.4%) RCCs lost their signal on IP images, with a sensitivity of 38.5% (95% CI = 23.4-55.4), a specificity of 100% (71.1-100), a positive predictive value (PPV) of 100% (73.4-100), a negative predictive value (NPV) of 31.4% (26.3-37.0), and an overall accuracy of 52% (37.4-66.3%). In terms of the quantitative analysis, the signal intensity index (SII= [(SIIP - SIOP) / SIOP] x 100) for RCCs was -0.132 ± 0.05, while for AMLs it was -0.031 ± 0.02, p = 0.26. The AUC was 0.414 ± -0.09 (0.237-0.592). Using 19% of signal loss as the threshold, sensitivity was 16% and specificity was 100%. The κappa value for subjective analysis was 0.63. Signal loss in "IP" images, assessed subjectively, was highly specific for distinction between RCCs and lpAMLs, although with low sensitivity. The findings can be used to improve the preoperative diagnostic accuracy of MRI for renal masses. Signal loss on "IP" GRE images is a reliable sign for differentiation between RCC and lpAMLs.

Usefulness of CT texture analysis in differentiating benign and malignant renal tumours

To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters. A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy. Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC. Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT.

To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics. In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation. A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve>0.7, p < 0.05) within these three groups of comparisons. In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhancedCT data.