Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis. Eur Radiol. 2018;28:1854-1861. [PMID: 29178029 DOI: 10.1007/s00330-017-5141-0]

Multi-sequence MRI-based radiomics model to preoperatively predict the WHO/ISUP grade of clear Cell Renal Cell Carcinoma: a two-center study

OBJECTIVES

To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC).

METHODS

This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant.

RESULTS

Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04-0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849-0.856) across different folds.

CONCLUSION

The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC.

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.

Role of Contrast-Enhanced Ultrasound With the Enhancement Pattern and Qualitative Analysis for Differentiating Hypovascular Solid Renal Lesions

OBJECTIVE

The aim of the work described here was to explore the clinical value of contrast-enhanced ultrasound (CEUS) with the enhancement pattern and qualitative analysis in distinguishing different types of hypovascular solid renal lesions.

METHODS

A total of 140 patients with 140 renal tumors (all diagnosed by pathology), which manifested hypo-enhancement on CEUS, were included in this study. We compared conventional ultrasound (US) and CEUS features in five common hypovascular renal tumors, including renal angiomyolipoma (RAML), clear cell renal cell carcinoma (ccRCC), renal pelvic urothelial carcinoma (RPUC), papillary renal cell carcinoma (pRCC) and chromophobe renal cell carcinoma (chRCC). The diagnostic value of conventional US and qualitative parameters of CEUS for differentiating hypovascular solid renal lesions were evaluated.

RESULTS

The mean age of patients with a benign renal lesion was younger than that of patients with a malignant renal lesion (p < 0.05). Echogenicity and qualitative parameters such as wash-out, perfusion defects and perilesional rim-like enhancement (PRE) in the two groups differed significantly (all p values <0.05). Benign renal lesions exhibited mainly slow wash-out, whereas malignant renal lesions exhibited predominantly fast wash-out on CEUS (p < 0.05). There were significant differences in echogenicity, such as between RAML and ccRCC, between RAML and RPUC and between RAML and pRCC (all p values <0.05). The rates of appearance of perfusion defect in ccRCC (48%, 13/27) and pRCC (53%, 10/19) were significantly higher than the rate in RAML (14%, 6/43) (p < 0.05). The rates of appearance of PRE in ccRCC (15%, 4/27), pRCC (26%, 5/19) and chRCC (24%,4/17) were significantly higher than the rate in RAML (9%, 4/43) (p < 0.05).

CONCLUSION

CEUS with the enhancement pattern and qualitative analysis may be helpful in distinguishing malignant from benign hypovascular renal lesions.

Using the "2 standard deviations" rule with Dixon MRI to differentiate renal cell carcinoma types

BACKGROUND

Clear cell and non-clear cell renal cell carcinoma (RCC) are distinguishable based on microscopic fat, detectable by chemical shift MRI. However, these assessments are often subjective. Conversely, Dixon MRIs and the "2 standard deviations" rule (2SDR) are quantitative methods that may decrease diagnostic subjectivity. Therefore, this study assessed the value of the 2SDR for detecting microscopic fat and thus differentiating clear cell and non-clear cell RCC using Dixon MRI.

METHODS

This retrospective study included patients with RCC who underwent preoperative Dixon MRI. The patients were grouped based on tumor type: clear cell RCC and non-clear cell RCC. The 2SDR value was calculated based on in-phase and opposed-phase images and then compared between the two groups. 2SDR values >0 indicated clear cell RCCs, whereas values <0 indicated non-clear cell RCC.

RESULTS

We included 151 patients; 114 patients had clear cell RCC, of which 106 had a 2SDR value >0. Furthermore, 37 patients had non-clear cell RCC, of which 3 had a 2SDR value >0. The 2SDR value was significantly higher in the clear cell RCC group than in the non-clear cell RCC group (p = 0.000). Overall, 93.0% (106/114) and 8.1% (3/37) of patients with clear cell and non-clear cell RCC, respectively, had microscopic fat. The evaluation indices for this 2SDR method were: accuracy: 92.72%, sensitivity: 92.98%, specificity: 91.89%, positive predictive value: 97.25%, and negative predictive value: 80.95%.

CONCLUSIONS

2SDR values calculated from Dixon magnetic resonance images can differentiate clear cell from non-clear cell RCCs by detecting microscopic fat.

PRECIS

The "2 standard deviations" rule value calculated from Dixon MR images differentiates clear cell from non-clear cell renal cell carcinoma with high efficiency by detecting microscopic fat.

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.

MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

Objectives

Standard magnetic resonance imaging (MRI) techniques are different to distinguish minimal fat angiomyolipoma (mf-AML) with minimal fat from renal cell carcinoma (RCC). Here we aimed to evaluate the diagnostic performance of MRI-based radiomics in the differentiation of fat-poor AMLs from other renal neoplasms.

Methods

A total of 69 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of RCC (n=50) or mf-AML (n=19) who underwent conventional MRI and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were included. Clinical data including age, sex, tumor location, urine creatinine, and urea nitrogen were collected from medical records. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured from renal tumors. We used the ITK-SNAP software to manually delineate the regions of interest on T2-weighted imaging (T2WI) and IVIM-DWI from the largest cross-sectional area of the tumor. We extracted 396 radiomics features by the Analysis Kit software for each MR sequence. The hand-crafted features were selected by using the Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO). Diagnostic models were built by logistic regression analysis. Receiver operating characteristic curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for the differentiation of mf-AML and RCC. Decision curve analysis (DCA) was used to evaluate the clinical utility of the models.

Results

Clinical model based on urine creatinine achieved an AUC of 0.802 (95%CI: 0.761-0.843). IVIM-based model based on f value achieved an AUC of 0.692 (95%CI: 0.627-0.757). T2WI-radiomics model achieved an AUC of 0.883 (95%CI: 0.852-0.914). IVIM-radiomics model achieved an AUC of 0.874 (95%CI: 0.841-0.907). Combined radiomics model achieved an AUC of 0.919 (95%CI: 0.894-0.944). Clinical-radiomics model yielded the best performance, with an AUC of 0.931 (95%CI: 0.907-0.955). The calibration curve and DCA confirmed that the clinical-radiomics model had a good consistency and clinical usefulness.

Conclusion

The clinical-radiomics model may be served as a noninvasive diagnostic tool to differentiate mf-AML with RCC, which might facilitate the clinical decision-making process.

Renal Epithelioid Angiomyolipoma in Children

Renal angiomyolipoma is a rare cause of renal tumor in children. Most are associated with tuberous sclerosis, and the classic type is observed more commonly. Epithelioid angiomyolipoma is even rarer with only limited case reports and series published in literature, most of which are of adult patients. We describe a 12-year-old boy, a diagnosed patient of tuberous sclerosis, who presented with pain in the left flank. On evaluation, it was found to have a left renal mass with the clinical picture suggestive of renal cell carcinoma. Partial nephrectomy was performed and histopathology revealed epithelioid angiomyolipoma. The child was asymptomatic at follow-up after 3 months. Only a few such cases in children are found in literature, which are discussed alongside. Differential diagnosis of this rare tumor must be kept in mind in a renal tumor as surgery is generally curative in this possibly malignant tumor. Metastasis confers a poor prognosis. Chemotherapy is generally not effective, although various regimens have been tried. Tumor recurrence must be kept in mind and a follow-up after apparent complete remission is of paramount importance.

Magnetic resonance imaging features of minimal-fat angiomyolipoma and causes of preoperative misdiagnosis

BACKGROUND

Minimal-fat angiomyolipoma (mf-AML) is often misdiagnosed as renal cell carcinoma before surgery.

AIM

To analyze the magnetic resonance imaging (MRI) features of mf-AML and the causes of misdiagnosis by MRI before operation.

METHODS

A retrospective analysis was performed on ten patients with mf-AML confirmed by surgical pathology, all of whom underwent preoperative MRI examination to analyze the morphological characteristics and MRI signals of the tumor.

RESULTS

MRI revealed a circular-like mass in 4/10 (40%) patients, an oval mass in 6/10 patients (60%), a mass with a capsule in 9/10 patients (90%), and a mass with a lipid component in 7/10 patients (70%). The diameter of the masses in all ten patients was from 11 to 47 mm; the diameter was between 11 mm and 40 mm in 8/10 (80%) patients and between 40 mm and 47 mm in 2/10 (20%) patients.

CONCLUSION

An oval morphological characteristic is strong evidence for the diagnosis of mf-AML, while a capsule and lipids are atypical manifestations of mf-AML.

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

OBJECTIVE. The objective of our study was to preoperatively predict fat-poor angiomyolipoma (fp-AML) and renal cell carcinoma (RCC) by conducting quantitative analysis of contrast-enhanced CT images. MATERIALS AND METHODS. One hundred fifteen patients with a pathologic diagnosis of fp-AML or RCC from a single institution were randomly allocated into a train set (tumor size: mean ± SD, 4.50 ± 2.62 cm) and test set (tumor size: 4.32 ± 2.73 cm) after data augmentation. High-dimensional histogram-based features, texture-based features, and Laws features were first extracted from CT images and were then combined as different combinations sets to construct a logistic prediction model based on the least absolute shrinkage and selection operator procedure for the prediction of fp-AML and RCC. Prediction performances were assessed by classification accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, true-positive rate, and false-positive rate (FPR). In addition, we also investigated the effects of different gray-scales of quantitative features on prediction performances. RESULTS. The following combination sets of features achieved satisfying performances in the test set: histogram-based features (mean AUC = 0.8492, mean classification accuracy = 91.01%); histogram-based features and texture-based features (mean AUC = 0.9244, mean classification accuracy = 91.81%); histogram-based features and Laws features (mean AUC = 0.8546, mean classification accuracy = 88.76%); and histogram-based features, texture-based features, and Laws features (mean AUC = 0.8925, mean classification accuracy = 90.36%). The different quantitative gray-scales did not have an obvious effect on prediction performances. CONCLUSION. The integration of histogram-based features with texture-based features and Laws features provided a potential biomarker for the preoperative diagnosis of fp-AML and RCC. The accurate diagnosis of benign or malignant renal masses would help to make the clinical decision for radical surgery or close follow-up.

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

OBJECTIVES

To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC).

METHODS

Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness.

RESULTS

Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness.

CONCLUSIONS

The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy.

KEY POINTS

• Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

Renal and adrenal masses containing fat at MRI: Proposed nomenclature by the society of abdominal radiology disease-focused panel on renal cell carcinoma

This article proposes a consensus nomenclature for fat-containing renal and adrenal masses at MRI to reduce variability, improve understanding, and enhance communication when describing imaging findings. The MRI appearance of "macroscopic fat" occurs due to a sufficient number of aggregated adipocytes and results in one or more of: 1) intratumoral signal intensity (SI) loss using fat-suppression techniques, or 2) chemical shift artifact of the second kind causing linear or curvilinear India-ink (etching) artifact within or at the periphery of a mass at macroscopic fat-water interfaces. "Macroscopic fat" is most commonly observed in adrenal myelolipoma and renal angiomyolipoma (AML) and only rarely encountered in other adrenal cortical tumors and renal cell carcinomas (RCC). Nonlinear noncurvilinear signal intensity loss on opposed-phase (OP) compared with in-phase (IP) chemical shift MRI (CSI) may be referred to as "microscopic fat" and is due to: a) an insufficient amount of adipocytes, or b) the presence of fat within tumor cells. Determining whether the signal intensity loss observed on CSI is due to insufficient adipocytes or fat within tumor cells cannot be accomplished using CSI alone; however, it can be inferred when other imaging features strongly suggest a particular diagnosis. Fat-poor AML are homogeneously hypointense on T₂ -weighted (T₂ W) imaging and avidly enhancing; signal intensity loss at OP CSI is uncommon, but when present is usually focal and is caused by an insufficient number of adipocytes within adjacent voxels. Conversely, clear-cell RCC are heterogeneously hyperintense on T₂ W imaging and avidly enhancing, with the signal intensity loss observed on OP CSI being typically diffuse and due to fat within tumor cells. Adrenal adenomas, adrenal cortical carcinoma, and adrenal metastases from fat-containing primary malignancies also show signal intensity loss on OP CSI due to fat within tumor cells and not from intratumoral adipocytes. Level of Evidence: 5 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;49:917-926.

CT features of hepatic epithelioid angiomyolipoma: differentiation from hepatocellular carcinoma in patients with noncirrhotic livers

Background

Hepatic epithelioid angiomyolipoma (HEA) shares some similarities with other hepatic tumors, such as hepatocellular carcinoma (HCC). Thus, establishing a definite diagnosis of HEA based on medical imaging is often difficult. In this study, we evaluated multiphasic computed tomography (CT) imaging to differentiate HEA from HCC in patients with noncirrhotic livers.

Methods

We retrospectively analyzed the clinical and imaging data of both contrast and non-contrast enhanced CT scans from 25 patients with HEA and 50 patients with HCC in noncirrhotic livers. CT features, including lesion position, size, shape, interior content, border, enhancement degree, and enhancement pattern, were independently evaluated by two radiologists. Intratumoral blood vessels, peripheral supply vessels, the early display of the hepatic vein, peripheral abnormal perfusion, peripheral washout sign, pseudocapsule, and portal tumor thrombus were also evaluated. Next, we quantitatively analyzed difference within results of clinical and CT characteristics between the HEA and HCC groups.

Results

The number of female HEA patients is more than male (76% vs. 24%), with a mean age of 49.44±10.33 years (from 30 to 68 years). The majority (64%) of HEA patients were asymptomatic, without hepatitis (88%). On non-contrast enhanced CT, HEA mainly manifested as a round (92%), hypodense mass (100%) with little fat (12%) and rare complications, such as hemorrhage (4%) and calcification (4%). HEA all manifested as an intensely enhanced mass on contrast-enhanced CT. The differences between HEA and HCC were significant in the imaging characteristics of the early display of the hepatic vein (32% vs. 0%, P=0.000), intratumoral blood vessels during the nonarterial phase (36% vs. 8%, P=0.003), washout enhancement (52% vs. 86%, P=0.001), and prolonged enhancement (40% vs. 4%, P=0.000).

Conclusions

Although HEA is an uncommon hepatic tumor, clinical and CT manifestation may be indicative. Clinical and CT characteristics including asymptomatic, non-hepatic, fat-deficient, early display of the hepatic vein, intratumoral blood vessels during the nonarterial phase and prolonged enhancement are selected to improve the recognition of HEA, supporting for a differential diagnosis from HCC.