Multiphasic contrast-enhanced MRI: single-slice versus volumetric quantification of tumor enhancement for the assessment of renal clear-cell carcinoma fuhrman grade

Diagnostics and prognostic evaluation in renal cell tumors: the German S3 guidelines recommendations

The German guidelines on renal cell carcinoma (RCC) have been developed at highest level of evidence based on systematic literature review. In this paper, we are presenting the current recommendations on diagnostics including preoperative imaging and imaging for stage evaluation as well as histopathological classification. The role of tumor biopsy is further discussed. In addition, different prognostic scores and the status of biomarkers in RCC are critically evaluated.

Tumor grade estımatıon of clear cell and papıllary renal cell carcınomas usıng contrast-enhanced MDCT and FSE T2 weıghted MR ımagıng: radıology-pathology correlatıon

BACKGROUND

Discrimination of low grade (grade 1-2) renal tumors from high grade (grade 3-4) ones carries crucial importance in terms of the management of these patients and also in the decision-making of appropriate treatment strategies. Our aim was to investigate whether contrast-enhanced multidetector computed tomography (MDCT) and T2 weighted fast spin echo (FSE) magnetic resonance imaging (MRI) could play a specific role in the discrimination of low grade versus high grade tumors in clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) patients.

METHODS

In this study, we retrospectively evaluated 66 RCC patients based on histopathologic findings who had underwent either partial or total nephrectomies. Our cohort consisted of 52 ccRCC and 14 pRCC patients, of whom 50 were male (%76) and 16 were female (%24). Among the 52 ccRCC patients, 18 had both cortico-medullary phase contrast-enhanced CT and MRI, 15 had only cortico-medullary phase CT and 19 had only MRI examination. In the pRCC group, 8 patients had both cortico-medullary phase contrast-enhanced CT and MRI, 3 had only cortico-medullary phase CT and 3 had only MRI. We both calculated mean tumor attenuation values on cortico-medullary phase MDCT images as HU (hounsfield unit) and also tumor mean signal intensity values on FSE T2 weighted MR images, using both region of interest and whole lesion measurements including normal renal cortex. The obtained values were compared with the grading results of the ccRCC and pRCC tumors according to the WHO/International Society of Urological Pathology grading system.

RESULTS

A significant positive correlation was found between the mean attenuation values of both tumor subtypes on cortico-medullary phase contrast-enhanced CT and their grades (p < 0.001). High grade tumors exhibited higher mean attenuation values (74.3 ± 22.3 HU) than the low grade tumors (55.2 ± 23.7 HU) in both subtypes. However, a statistically significant correlation was not found between the mean signal intensity values of the two tumor subtypes on FSE T2 weighted MR images and their grades (p > 0.05). Low grade tumors had a mean signal intensity value of 408.9 ± 44.6, while high grade tumors showed a value of 382.1 ± 44.2. The analysis of the ccRCC group patients, yielded a statistically significant correlation between the mean signal intensity values on T2 weighted images and tumor grading (p < 0.001). Low grade (grade 1-2) ccRCC patients exhibited higher mean signal intensity values (475.7 ± 51.3), as compared to those of high grade (grade 3-4) (418.5 ± 45.7) tumors. On the other hand, analysis of the pRCC group patients revealed that there was a significant correlation between the mean attenuation values of tumors on cortico-medullary phase contrast-enhanced CT and their grades (p < 0.001). High grade papillary subtype tumors (54.2 ± 25.2) showed higher mean attenuation values than the low grade (35.5 ± 18.8) ones.

CONCLUSIONS

Contrast-enhanced MDCT and T2 weighted FSE MRI can play a considerable role in the discrimination of low grade versus high grade tumors of both subtype RCC patients. Thus, these non-invasive evaluation techniques may have positive impact on the determination of the management and treatment strategies of these patients.

Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades

OBJECTIVES

Nuclear grades are proved to be one of the most significant prognostic factors for clear cell renal cell carcinoma (ccRCC). Radiomics nomogram is a widely used noninvasive tool that could predict tumor phenotypes. In this study, we performed radiomics analysis to develop and validate a CT-based nomogram for the preoperative prediction of nuclear grades in ccRCC.

METHOD

CT images and clinical data of 258 ccRCC patients were retrieved from the Cancer Imaging Archive (TCIA). Radiomics features were extracted from arterial-phase CT images using 3D Slicer software. LASSO regression model was performed to develop a radiomics signature in the training set (n = 143). A radiomics nomogram was constructed combining radiomics signature and selected clinical predictors. Receiver operating characteristic (ROC) curve and calibration curve were used to determine the performance of the radiomics nomogram in the training and validation set (n = 115). Decision curve analysis was used to assess the clinical usefulness of the CT-based nomogram.

RESULTS

One thousand three hundred sixteen radiomics features were extracted from arterial-phase CT images. A radiomics signature, consisting of 20 features, was developed and showed a favorable performance in discriminating nuclear grades with an area under the curve (AUC) of 0.914 and 0.846 in the training and validation set, respectively. The CT-based nomogram, including the radiomics signature and the CT-determined T stage, achieved good calibration and discrimination in the training set (AUC, 0.929; 95% CI, 0.886-0.972) and validation set (AUC, 0.876; 95% CI, 0.812-0.939). Decision curve analysis demonstrated the clinical usefulness of the CT-based nomogram.

CONCLUSION

The noninvasive CT-based nomogram, including radiomics signature and CT-determined T stage, could improve the accuracy of preoperative grading of ccRCC and provide individualized treatment for ccRCC patients.

KEY POINTS

• Contrast-enhanced CT may help in preoperative grading of ccRCC. • The CT-based nomogram incorporated a radiomics signature and CT-determined T stage could preoperatively predict ccRCC grades. • The CT-based nomogram has the potential to improve individualized treatment and assist clinical decision making of ccRCC patients.

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.

Methods

Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.

Results

A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.

Conclusion

We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Association of tumor grade, enhancement on multiphasic CT and microvessel density in patients with clear cell renal cell carcinoma

PURPOSE

Clear cell renal cell carcinoma (ccRCC) comprises nearly 90% of all diagnosed RCC subtypes and has the worst prognosis and highest metastatic potential. The strongest prognostic factors for patients with ccRCC include histological subtype and Fuhrman grade, which are incorporated into prognostic models. Since ccRCC is a highly vascularized tumor, there may be differences in enhancement patterns on multidetector CT (MDCT) due to the hemodynamics and microvessel density (MVD) of the lesions. This may provide a noninvasive method to characterize incidentally detected low- and high-grade ccRCCs on MDCT. The purpose of our study was to determine the correlation between MDCT enhancement parameters, ccRCC MVD, and Fuhrman grade to determine its utility and value in assessing tumor vascularity and grade in vivo.

METHODS

In this retrospective, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low-grade (LG), and 43 high-grade (HG) ccRCCs underwent preoperative four-phase MDCT. A 3D volume of interest (VOI) was obtained for every tumor and absolute enhancement and the wash-in/wash-out of enhancement for each phase was assessed. Immunohistochemistry on resected specimens was used to quantify MVD. Linear regression and Pearson correlation were used to investigate the strength of the association between 3D VOI enhancement and MVD. Stepwise logistic regression analysis determined independent predictors of HG ccRCC. Cut-off values and odds Ratio (OR) with 95% CIs were reported. The clinical, radiomic, and pathologic features with the highest performance in the stepwise logistic regression analysis were evaluated using receiver operator characteristics (ROC) and area under the curve (AUC).

RESULTS

Absolute enhancement in the nephrographic phase < 52.1 Hounsfield Units (HU) (HR 0.979, 95% CI 0.964-0.994, p value = 0.006), lesion size > 4.3 cm (HR 1.450, 95% CI 1.211-1.738, p value < 0.001), and an intratumoral MVD < 15% (HR 0.932, 95% CI 0.867-1.002, p value = 0.058) were independent predictors of HG ccRCC with an AUC of 0.818 (95% CI 0.725-0.911). HG ccRCCs had a significant association between 3D VOI enhancement and MVD in each post-contrast phase (r² = 0.238 to 0.455, p < 0.05).

CONCLUSIONS

Absolute enhancement of the entire lesion obtained from a 3D VOI in the nephrographic phase on preoperative MDCT can provide quantitative data that are a significant, independent predictor of a high-grade clear cell RCC and can be used to assess tumor vascularity and grade in vivo.

Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma

Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making.

PURPOSE

To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma.

STUDY TYPE

Retrospective.

POPULATION

In all, 376 patients with 430 renal cell carcinoma lesions from 2008-2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set.

FIELD STRENGTH/SEQUENCE

1.5T and 3.0T/T₂ -weighted and T₁ contrast-enhanced sequences.

ASSESSMENT

The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model.

STATISTICAL TESTS

Mann-Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity.

RESULTS

The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73-0.96), sensitivity of 0.89 (95% CI: 0.74-0.96), and specificity of 0.88 (95% CI: 0.73-0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73-0.90), sensitivity of 0.92 (95% CI: 0.84-0.97), and specificity of 0.78 (95% CI: 0.68-0.86) in the WHO/ISUP test set.

DATA CONCLUSION

Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Can MRI be used to diagnose histologic grade in T1a (< 4 cm) clear cell renal cell carcinomas?

OBJECTIVE

To assess whether MRI can differentiate low-grade from high-grade T1a cc-RCC.

MATERIALS AND METHODS

With IRB approval, 49 consecutive solid < 4 cm cc-RCC (low grade [Grade 1 or 2] N = 38, high grade [Grade 3] N = 11) with pre-operative MRI before nephrectomy were identified between 2013 and 2018. Tumor size, apparent diffusion coefficient (ADC) histogram analysis, enhancement wash-in and wash-out rates, and chemical shift signal intensity index (SI index) were assessed by a blinded radiologist. Subjectively, two blinded Radiologists also assessed for (1) microscopic fat, (2) homogeneity (5-point Likert scale), and (3) ADC signal (relative to renal cortex); discrepancies were resolved by consensus. Outcomes were studied using Chi square, multivariate analysis, logistic regression modeling, and ROC. Inter-observer agreement was assessed using Cohen's kappa.

RESULTS

Tumor size was 24 ± 7 (13-39) mm with no association to grade (p = 0.45). Among quantitative features studied, corticomedullary phase wash-in index (p = 0.015), SI index (p = 0.137), and tenth-centile ADC (p = 0.049) were higher in low-grade tumors. 36.8% (14/38) low-grade tumors versus zero high-grade tumors demonstrated microscopic fat (p = 0.015; Kappa = 0.67). Microscopic fat was specific for low-grade disease (100.0% [71.5-100.0]) with low sensitivity (36.8% [21.8-54.6]). Other subjective features did not differ between groups (p > 0.05). A logistic regression model combining microscopic fat + wash-in index + tenth-centile-ADC yielded area under ROC curve 0.98 (Confidence Intervals 0.94-1.0) with sensitivity/specificity 87.5%/100%.

CONCLUSION

The combination of microscopic fat, higher corticomedullary phase wash-in and higher tenth-centile ADC is highly accurate for diagnosis of low-grade disease among T1a clear cell RCC.

Nonenhancing Component of Clear Cell Renal Cell Carcinoma on Computed Tomography Correlates With Tumor Necrosis and Stage and Serves as a Size-Independent Prognostic Biomarker

OBJECTIVES

This study aimed to quantify nonenhancing tumor (NT) component in clear cell renal cell carcinoma (ccRCC) and assess its association with histologically defined tumor necrosis, stage, and survival outcomes.

METHODS

Among 183 patients with ccRCC, multi-institutional changes in computed tomography attenuation of tumor voxels were used to quantify percent of NT. Associations of NT with histologic tumor necrosis and tumor stage/grade were tested using Wilcoxon signed rank test and with survival outcomes using Kaplan-Meier curves/Cox regression analysis.

RESULTS

Nonenhancing tumor was higher in ccRCC with tumor necrosis (11% vs 7%; P = 0.040) and higher pathological stage (P = 0.042 and P < 0.001, respectively). Patients with greater NT had higher incidence of cancer recurrence after resection (P < 0.001) and cancer-specific mortality (P < 0.001).

CONCLUSION

Nonenhancing tumor on preoperative computed tomographic scans in patients with ccRCC correlates with tumor necrosis and stage and may serve as an independent imaging prognostic biomarker for cancer recurrence and cancer-specific survival.

Update on Indications for Percutaneous Renal Mass Biopsy in the Era of Advanced CT and MRI

OBJECTIVE. The objective of this article is to review the burgeoning role of percutaneous renal mass biopsy (RMB). CONCLUSION. Percutaneous RMB is safe, accurate, and indicated for an expanded list of clinical scenarios. The chief scenarios among them are to prevent treatment of benign masses and help select patients for active surveillance (AS). Imaging characterization of renal masses has improved; however, management decisions often depend on a histologic diagnosis and an assessment of biologic behavior of renal cancers, both of which are currently best achieved with RMB.

CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma

BACKGROUND

The purpose of this study was to analyze the image heterogeneity of clear-cell renal-cell carcinoma (ccRCC) by computer tomography texture analysis and to provide new objective quantitative imaging parameters for the pre-operative prediction of Fuhrman-grade ccRCC.

METHODS

A retrospective analysis of 131 cases of ccRCCs was performed by manually depicting tumor areas. Then, histogram-based texture parameters were calculated. The texture-feature values between Fuhrman low- (Grade I-II) and high-grade (Grade III-IV) ccRCCs were compared by two independent sample t-tests (False Discovery Rate correction), and receiver operating characteristic curve (ROC) was used to evaluate the efficacy of using texture features to predict Fuhrman high- and low-grade ccRCCs.

RESULTS

There were no statistical differences for any texture parameters without filtering (p > 0.05). There was a statistically significant difference between the entropy (fine) of the corticomedullary phase and the entropy (fine and coarse) of the nephrographic phase after Laplace of Gaussian filtering. The area under the ROC of the entropy was between 0.74 and 0.83.

CONCLUSIONS

Computer tomography texture features can predict the Fuhrman grading of ccRCC pre-operatively, with entropy being the most important imaging marker for clinical application.

Association of qualitative and quantitative imaging features on multiphasic multidetector CT with tumor grade in clear cell renal cell carcinoma

PURPOSE

The purpose of the study was to determine if enhancement features and qualitative imaging features on multiphasic multidetector computed tomography (MDCT) were associated with tumor grade in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective, IRB approved, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low grade (LG) and 43 high grade (HG) ccRCCs underwent preoperative four-phase MDCT in unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases. Previously published quantitative (absolute peak lesion enhancement, absolute peak lesion enhancement relative to normal enhancing renal cortex, 3D whole lesion enhancement and the wash-in/wash-out of enhancement within the 3D whole lesion ROI) and qualitative (enhancement pattern; presence of necrosis; pattern of; tumor margin; tumor-parenchymal interface, tumor-parenchymal interaction; intratumoral vascularity; collecting system infiltration; renal vein invasion; and calcification) assessments were obtained for each lesion independently by two fellowship-trained genitourinary radiologists. Comparisons between variables included χ², ANOVA, and student t test. p values less than 0.05 were considered to be significant. Inter-reader agreement was obtained with the Gwet agreement coefficient (AC1) and standard error (SE) was reported.

RESULTS

No significant differences were observed between the LG and HG ccRCC cohorts with respect to absolute peak lesion enhancement and relative lesion enhancement ratio. There was a significant inverse correlation between low and high grade ccRCC and tumor enhancement the NP (71 HU vs. 54 HU, p < 0.001) and EX (52 HU vs. 39 HU, p < 0.001) phases using the 3D whole lesion ROI method. The percent wash-in of 3D enhancement from the UN to the CM phase was also significantly different between LG and HG ccRCCs (352% vs. 255%, p = 0.003). HG lesions showed significantly more calcification, necrosis, collecting system infiltration and ill-defined tumor margins (p < 0.05). Overall agreement between the two readers had a mean AC1 of 0.8172 (SE 0.0235).

CONCLUSIONS

Quantitatively, high grade ccRCC had significantly lower whole lesion enhancement in the NP and EX phases on MDCT. Qualitatively, high grade ccRCC were significantly more likely to be associated with calcifications, necrosis, collecting system infiltration, and an ill-defined tumor margin.

Compressed Sensing Radial Sampling MRI of Prostate Perfusion: Utility for Detection of Prostate Cancer

Purpose To investigate the diagnostic performance of a dual-parameter approach by combining either volumetric interpolated breath-hold examination (VIBE)- or golden-angle radial sparse parallel (GRASP)-derived dynamic contrast agent-enhanced (DCE) MRI with established diffusion-weighted imaging (DWI) compared with traditional single-parameter evaluations on the basis of DWI alone. Materials and Methods Ninety-four male participants (66 years ± 7 [standard deviation]) were prospectively evaluated at 3.0-T MRI for clinical suspicion of prostate cancer. Included were 101 peripheral zone prostate cancer lesions. Histopathologic confirmation at MRI transrectal US fusion biopsy was matched with normal contralateral prostate parenchyma. MRI was performed with diffusion weighting and DCE by using GRASP (temporal resolution, 2.5 seconds) or VIBE (temporal resolution, 10 seconds). Perfusion (influx forward volume transfer constant [K^trans] and rate constant [K_ep]) and apparent diffusion coefficient (ADC) parameters were determined by tumor volume analysis. Areas under the receiver operating characteristic curve were compared for both sequences. Results Evaluated were 101 prostate cancer lesions (GRASP, 61 lesions; VIBE, 40 lesions). In a combined analysis, diffusion and perfusion parameters ADC with K^trans or K_ep acquired with GRASP had higher diagnostic performance compared with diffusion characteristics alone (area under the curve, 0.97 ± 0.02 [standard error] vs 0.93 ± 0.03; P < .006 and .021, respectively), whereas ADC with perfusion parameters acquired with VIBE had no additional benefit (area under the curve, 0.94 ± 0.03 vs 0.93 ± 0.04; P = .18and .50, respectively, for combination of ADC with K^trans and K_ep). Conclusion If used in a dual-parameter model, incorporating diffusion and perfusion characteristics, the golden-angle radial sparse parallel acquisition technique improves the diagnostic performance of multiparametric MRI examinations of the prostate. This effect could not be observed combining diffusing with perfusion parameters acquired with volumetric interpolated breath-hold examination. © RSNA, 2018.

Combined Volumetric and Density Analyses of Contrast-Enhanced CT Imaging to Assess Drug Therapy Response in Gastroenteropancreatic Neuroendocrine Diffuse Liver Metastasis

Objective

We propose a computer-aided method to assess response to drug treatment, using CT imaging-based volumetric and density measures in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and diffuse liver metastases.

Methods

Twenty-five patients with GEP-NETs with diffuse liver metastases were enrolled. Pre- and posttreatment CT examinations were retrospectively analyzed. Total tumor volume (volume) and mean volumetric tumor density (density) were calculated based on tumor segmentation on CT images. The maximum axial diameter (tumor size) for each target tumor was measured on pre- and posttreatment CT images according to Response Evaluation Criteria In Solid Tumors (RECIST). Progression-free survival (PFS) for each patient was measured and recorded.

Results

Correlation analysis showed inverse correlation between change of volume and density (Δ(V + D)), change of volume (ΔV), and change of tumor size (ΔS) with PFS (r = −0.653, P=0.001; r = −0.617, P=0.003; r = −0.548, P=0.01, respectively). There was no linear correlation between ΔD and PFS (r = −0.226, P=0.325).

Conclusion

The changes of volume and density derived from CT images of all lesions showed a good correlation with PFS and may help assess treatment response.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade

OBJECTIVES

To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features.

METHODS

161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients. We used interclass correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO) regression method to select features, then the selected features were constructed three classification models (CMP, NP and with their combination) to discriminate high and low grades CCRCC. These three models were built by logistic regression method using 5-fold cross validation strategy, evaluated with receiver operating characteristics curve (ROC) and compared using DeLong test.

RESULTS

We found 11 and 24 CMP and NP features were independently significantly associated with the Fuhrman grades. The model of CMP, NP and Combined model using radiomic feature set showed diagnostic accuracy of 0.719 (AUC [area under the curve], 0.766; 95% CI [confidence interval]: 0.709-0.816; sensitivity, 0.602; specificity, 0.838), 0.738 (AUC, 0.818; 95% CI:0.765-0.838; sensitivity, 0.693; specificity, 0.838), 0.777(AUC, 0.822; 95% CI: 0.769-0.866; sensitivity, 0.677; specificity, 0.839). There were significant differences in AUC between CMP model and Combined model (P = 0.0208), meanwhile, the differences between CMP model and NP model, NP model and Combined model reached no significant (P = 0.0844, 0.7915).

CONCLUSIONS

Radiomic features could be used as biomarker for the preoperative evaluation of the CCRCC Fuhrman grades.

Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development

Diagnosis of renal cell carcinomas (RCC) subtypes on computed tomography (CT) and magnetic resonance imaging (MRI) is clinically important. There is increased evidence that confident imaging diagnosis is now possible while standardization of the protocols is still required. Fat-poor angiomyolipoma show homogeneously increased unenhanced attenuation, homogeneously low signal on T2-weighted MRI and apparent diffusion coefficient (ADC) map, may contain microscopic fat and are classically avidly enhancing. Papillary RCC are also typically hyperattenuating and of low signal on T2-weighted MRI and ADC map; however, their gradual progressive enhancement after intravenous administration of contrast material is a differentiating feature. Clear cell RCC are avidly enhancing and may show intracellular lipid; however, these tumors are heterogeneous and are of characteristically increased signal on T2-weighted MRI. Oncocytomas and chromophobe tumors (collectively oncocytic neoplasms) show intermediate imaging findings on CT and MRI and are the most difficult subtype to characterize accurately; however, both show intermediately increased signal on T2-weighted with more gradual enhancement compared to clear cell RCC. Chromophobe tumors tend to be more homogeneous compared to oncocytomas, which can be heterogeneous, but other described features (e.g. scar, segmental enhancement inversion) overlap considerably between tumors. Tumor grade is another important consideration in small solid renal masses with emerging studies on both CT and MRI suggesting that high grade tumors may be separated from lower grade disease based upon imaging features.

Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma

OBJECTIVE

The objective of our study was to evaluate tumor attenuation and texture on unenhanced CT for potential differentiation of low-grade from high-grade chromophobe renal cell carcinoma (RCC).

MATERIALS AND METHODS

A retrospective study of 37 consecutive patients with chromophobe RCC (high-grade, n = 13; low-grade, n = 24) who underwent preoperative unenhanced CT between 2011 and 2016 was performed. Two radiologists (readers 1 and 2) blinded to the histologic grade of the tumor and outcome of the patients subjectively evaluated tumor homogeneity (3-point scale: completely homogeneous, mildly heterogeneous, or mostly heterogeneous). A third radiologist, also blinded to tumor grade and patient outcome, measured attenuation and contoured tumors for quantitative texture analysis. Comparisons were performed between high-grade and low-grade tumors using the chi-square test for subjective variables and sex, independent t tests for patient age and tumor attenuation, and Mann-Whitney U tests for texture analysis. Logistic regression models and ROC curves were computed.

RESULTS

There were no differences in age or sex between the groups (p = 0.652 and 0.076). High-grade tumors were larger (mean ± SD, 62.6 ± 34.9 mm [range, 17.0-141.0 mm] vs 39.0 ± 17.9 mm [16.0-72.3 mm]; p = 0.009) and had higher attenuation (mean ± SD, 45.5 ± 8.2 HU [range, 29.0-55.0 HU] vs 35.3 ± 8.5 HU [14.0-51.0 HU]; p = 0.001) than low-grade tumors. CT size and attenuation achieved good accuracy to diagnose high-grade chromophobe RCC: The AUC ± standard error was 0.85 ± 0.08 (p < 0.0001) with a sensitivity of 69.0% and a specificity of 100%. Subjectively, high-grade tumors were more heterogeneous (mildly or markedly heterogeneous: 69.2% [9/13] for reader 1 and 76.9% [10/13] for reader 2; reader 1, p = 0.024; reader 2, p = 0.001) with moderate agreement (κ = 0.57). Combined texture features diagnosed high-grade tumors with a maximal AUC of 0.84 ± 0.06 (p < 0.0001).

CONCLUSION

Tumor attenuation and heterogeneity assessed on unenhanced CT are associated with high-grade chromophobe RCC and correlate well with the histopathologic chromophobe tumor grading system.

Precision of manual two-dimensional segmentations of lung and liver metastases and its impact on tumour response assessment using RECIST 1.1

Background

Response evaluation criteria in solid tumours (RECIST) has significant limitations in terms of variability and reproducibility, which may not be independent. The aim of the study was to evaluate the precision of manual bi-dimensional segmentation of lung, liver metastases, and to quantify the uncertainty in tumour response assessment.

Methods

A total of 520 segmentations of metastases from six livers and seven lungs were independently performed by ten physicians and ten scientists on CT images, reflecting the variability encountered in clinical practice. Operators manually contoured the tumours, firstly independently according to the RECIST and secondly on a preselected slice. Diameters and areas were extracted from the segmentations. Mean standard deviations were used to build regression models and 95% confidence intervals (95% CI) were calculated for each tumour size and for limits of progressive disease (PD) and partial response (PR) derived from RECIST 1.1.

Results

Thirteen aberrant segmentations (2.5%) were observed without significant differences between the physicians and scientists; only the mean area of liver tumours (p = 0.034) and mean diameter of lung tumours (p = 0.021) differed significantly. No difference was observed between the methods. Inter-observer agreement was excellent (intra-class correlation >0.90) for all variables. In liver, overlaps of the 95% CI with the 95% CI of limits of PD or PR were observed for diameters above 22.7 and 37.9 mm, respectively. An overlap of 95% CIs was systematically observed for area. No overlaps were observed in lung.

Conclusions

Although the experience of readers might not affect the precision of segmentation in lung and liver, the results of manual segmentation performed for tumour response assessment remain uncertain for large liver metastases.

Electronic supplementary material

The online version of this article (doi:10.1186/s41747-017-0015-4) contains supplementary material, which is available to authorized users.

[Focal therapy for small renal masses : Observation, ablation or surgery]

BACKGROUND

The rising incidence of renal cell carcinoma, its more frequent early detection (stage T1a) and the increasing prevalence of chronic renal failure with higher morbidity and shorter life expectancy underscore the need for multimodal focal nephron-sparing therapy.

DISCUSSION

During the past decade, the gold standard shifted from radical to partial nephrectomy. Depending on the surgeon's experience, the patient's constitution and the tumor's location, the intervention can be performed laparoscopically with the corresponding advantages of lower invasiveness. A treatment alternative can be advantageous for selected patients with high morbidity and/or an increased risk of complications associated with anesthesia or surgery. Corresponding risk stratification necessitates previous confirmation of the small renal mass (cT1a) by histological examination of biopsy samples. Active surveillance represents a controlled delay in the initiation of treatment.

RESULTS

Percutaneous radiofrequency ablation (RFA) and laparoscopic cryoablation are currently the most common treatment alternatives, although there are limitations particularly for renal tumors located centrally near the hilum. More recent ablation procedures such as high intensity focused ultrasound (HIFU), irreversible electroporation, microwave ablation, percutaneous stereotactic ablative radiotherapy and high-dose brachytherapy have high potential in some cases but are currently regarded as experimental for the treatment of renal cell carcinoma.

Correlation between CT perfusion parameters and Fuhrman grade in pTlb renal cell carcinoma

PURPOSE

To evaluate the correlation of CT perfusion parameters with the Fuhrman grade in pT1b (4-7 cm) renal cell carcinoma (RCC).

METHODS

CT perfusion imaging and Fuhrman pathological grading of pT1b RCC were performed in 48 patients (10 grade 1, 27 grade 2, 9 grade 3, and 2 grade 4). Equivalent blood volume (BV _Equiv), permeability surface area product (PS), and blood flow (BF) of tumors were measured. Grade 1 and 2 were defined as low-grade group (n = 37), meanwhile high-grade group (n = 11) included grade 3 and 4. Comparisons of CT perfusion parameters and tumor size of the two different groups were performed. Correlations between CT perfusion parameters, Fuhrman grade (grade 1, 2, 3, and 4), and tumor size were assessed.

RESULTS

PS was significantly lower in high grade than in low-grade pT1b RCC (P = 0.004). However, no significant differences were found in BV _Equiv and BF between the two groups (P > 0.05 for both). The optimal threshold value, sensitivity, specificity, and the area under the ROC curve for distinguishing the two groups using PS were 68.8 mL/100 g/min, 0.7, 0.8, and 0.8, respectively. Negative significant correlation was observed between PS and Fuhrman grade (r = -0.338, P = 0.019).

CONCLUSIONS

The PS of pT1b RCC had negative significant correlation with Fuhrman grade. CT perfusion appeared to be a non-invasive means to predict high Fuhrman grade of pT1b RCC preoperatively and guide the optimal treatment for the patient.

Can diffusion-weighted magnetic resonance imaging of clear cell renal carcinoma predict low from high nuclear grade tumors

OBJECTIVE

To assess the diagnostic performance of the apparent diffusion coefficient (ADC) in predicting the Fuhrman nuclear grading of clear cell renal cell carcinomas (ccRCC).

MATERIALS AND METHODS

A total of 129 patients who underwent partial and radical nephrectomies with pathology-proven ccRCC were retrospectively evaluated. Histopathological characteristics and nuclear grades were analyzed. In addition, conventional magnetic resonance imaging (MRI) features were assessed in consensus by two radiologists to discriminate nuclear grading. ADC values were obtained from a region of interest (ROI) measurement in the ADC maps calculated from diffusion-weighted imaging (DWI) using b values of 50, 500, and 800 s/mm². The threshold values for predicting and differentiating low-grade cancers (Fuhrman I-II) from high grade (Fuhrman III-IV) was obtained using binary logistic regression. The ADC cut-off value for differentiating low- and high-grade tumors was determined using classification analysis.

RESULTS

Significant associations (P < 0.001) were found between nuclear grading, conventional MR features, and DWI. Hemorrhage, necrosis, perirenal fat invasion, enhancement homogeneity, and cystic component were identified as independent predictors of tumor grade. High-grade ccRCC had significantly lower mean ADC values compared to low-grade tumors. An ADC cut-off value of 1.6 × 10^-3 mm²/s had an optimal predictive percentage of 65.5% for low-grade tumors above this threshold and 81% for high-grade ccRCC below this threshold. Overall predictive accuracy was 70.5%.

CONCLUSION

The addition of ADC values to a model based on MRI conventional features demonstrates increased sensitivity and high specificity improving the distinguishing accuracy between both high-grade and low-grade ccRCC.

Differentiation of Clear Cell Renal Cell Carcinoma From Other Renal Cortical Tumors by Use of a Quantitative Multiparametric MRI Approach

OBJECTIVE

The purpose of this study was to develop a quantitative multiparametric MRI approach to differentiating clear cell renal cell carcinoma (RCC) from other renal cortical tumors.

MATERIALS AND METHODS

This retrospective study included 119 patients with 124 histopathologically confirmed renal cortical tumors who underwent preoperative MRI including DWI, contrast-enhanced, and chemical-shift sequences before nephrectomy. Two radiologists independently assessed each tumor volumetrically, and apparent diffusion coefficient values, parameters from multiphasic contrast-enhanced MRI (peak enhancement, upslope, downslope, AUC), and chemical-shift indexes were calculated. Univariate and multivariable logistic regression analyses were performed to identify parameters associated with clear cell RCC.

RESULTS

Interreader agreement was excellent (intraclass correlation coefficient, 0.815-0.994). The parameters apparent diffusion coefficient (reader 1 AUC, 0.804; reader 2, 0.807), peak enhancement (reader 1 AUC, 0.629; reader 2, 0.606), and downslope (reader 1 AUC, 0.575; reader 2, 0.561) were significantly associated with discriminating clear cell RCC from other renal cortical tumors. The combination of all three parameters further increased diagnostic accuracy (reader 1 AUC, 0.889; reader 2, 0.907; both p ≤ 0.001), yielding sensitivities of 0.897 for reader 1 and 0.897 for reader 2, and specificities of 0.762 for reader 1 and 0.738 for reader 2 in the identification of clear cell RCC. With maximized sensitivity, specificities of 0.429 and 0.262 were reached for readers 1 and 2, respectively.

CONCLUSION

A quantitative multiparametric approach statistically significantly improves diagnostic performance in differentiating clear cell RCC from other renal cortical tumors.

Is Multiparametric MRI Useful for Differentiating Oncocytomas From Chromophobe Renal Cell Carcinomas?

OBJECTIVE

The purpose of this study was to retrospectively evaluate the diagnostic accuracy of multiparametric MRI to differentiate oncocytoma from chromophobe renal cell carcinoma (RCC).

MATERIALS AND METHODS

In this retrospective study, 26 histologically confirmed oncocytomas and 16 chromophobe RCCs that underwent full MRI examination were identified in 42 patients (25 men and 17 women) over a 6-year period. Demographic data were recorded. Double-echo chemical-shift, dynamic contrast-enhanced T1- and T2-weighted images, and apparent diffusion coefficient (ADC) maps were reviewed independently by two radiologists blinded to pathologic results. Signal-intensity index (SII), tumor-to-spleen signal-intensity ratio, ADC ratio, three wash-in indexes, and two washout indexes were calculated and compared using univariate and ROC analyses. Sensitivity and specificity analyses were performed to calculate diagnostic accuracy.

RESULTS

All carcinomas and nine oncocytomas were resected; the remaining 17 oncocytomas were biopsied. Patient age (for oncocytomas: mean, 68.2 years; range, 43-84 years; for RCCs: mean, 60.8 years; range, 20-79 years) and tumor size (for oncocytomas: mean, 35.5 mm; range, 12-98 mm; for RCCs: mean, 37.2 mm; range, 9-101 mm) did not differ significantly across groups (p = 0.132 and 0.265, respectively). Good interobserver agreement was observed for all measurements but four. Oncocytomas presented significantly higher ADC (p = 0.002) and faster enhancement (p = 0.007-0.012) but lower SII (p = 0.03) than carcinomas. This combination provided sensitivity of 92.3% (24/26), specificity of 93.8% (15/16), and accuracy of 92.9% (39/42) for the detection of oncocytomas.

CONCLUSION

Multiparametric MRI helps to accurately differentiate oncocytomas from chromophobe RCCs with high sensitivity and specificity.

Small (<4 cm) clear cell renal cell carcinoma: correlation between CT findings and histologic grade

PURPOSE

To evaluate the correlation between CT findings and histologic grade of small clear cell renal cell carcinoma (ccRCC).

METHODS

CT scans of 101 patients with small ccRCC were reviewed independently by two radiologists for tumor size, shape, margin, encapsulation, enhancement pattern, and visual relative enhancement. Enhancement patterns were defined according to the percentage of uniform enhancement [pattern 1, homogeneous (≥90%); pattern 2, relatively homogeneous (≥75 and <90%); and pattern 3, heterogeneous (<75%)]. Quantitative parameters representing attenuation and degree of enhancement were calculated. Histologic grade was classified as low (Fuhrman grade I or II) and high (Fuhrman grade III or IV). CT imaging variables were analyzed using univariate and multivariate analyses.

RESULTS

A total of 63 low-grade and 38 high-grade small ccRCCs were assessed. Low-grade tumors differed from high-grade tumors with respect to enhancement pattern 1 or 2 (p < 0.001 and p < 0.001), smaller size (p = 0.002 and p = 0.001), and lower attenuation on unenhanced scan (p < 0.001 and p = 0.008). In multivariate analysis, enhancement pattern 1 or 2 and low attenuation (≤30 HU) were identified as independent predictors of low-grade ccRCC. Accuracy derived from logistic regression analysis was 79.2% for reader 1 and 70.3% for reader 2.

CONCLUSIONS

CT imaging features including tumor attenuation and enhancement pattern can be useful to predict the biologic behavior of small ccRCC for adequate treatment strategy.

Grading glial tumors with amide proton transfer MR imaging: different analytical approaches

Amide proton transfer (APT) magnetic resonance imaging is gaining attention for its capability for grading glial tumors. Usually, a representative slice is analyzed. Different definitions of tumor areas have been employed in previous studies. We hypothesized that the accuracy of APT imaging for brain tumor grading may depend upon the analytical methodology used, such as selection of regions of interest (ROIs), single or multiple tumor slices, and whether or not there is normalization to the contralateral white matter. This study was approved by the institutional review board, and written informed consent was waived. Twenty-six patients with histologically proven glial tumors underwent preoperative APT imaging with a three-dimensional gradient-echo sequence. Two neuroradiologists independently analyzed APT asymmetry (APTasym) images by placing ROIs on both a single representative slice (RS) and all slices including tumor (i.e. whole tumor: WT). ROIs indicating tumor extent were separately defined on both FLAIR and, if applicable, contrast-enhanced T1-weighted images (CE-T1WI), yielding four mean APTasym values (RS-FLAIR, WT-FLAIR, RS-CE-T1WI, and WT-CE-T1WI). The maximum values were also measured using small ROIs, and their differences among grades were evaluated. Receiver operating characteristic (ROC) curve analysis was also conducted on mean and maximum values. Intra-class correlation coefficients for inter-observer agreement were excellent. Significant differences were observed between high- and low-grade gliomas for all five methods (P < 0.01). ROC curve analysis found no statistically significant difference among them. This study clarifies that single-slice APT analysis is robust despite tumor heterogeneity, and can grade glial tumors with or without the use of contrast material.

Tumor grade of clear cell renal cell carcinoma assessed by contrast-enhanced computed tomography

The purpose of this study was to clarify the association between CT findings and Fuhrman grade of clear cell renal cell carcinoma (ccRCC). The study group consisted of 214 surgically proven ccRCC in 214 patients. Contrast-enhanced CT studies were retrospectively assessed for tumor size, cystic versus solid, calcification, heterogeneity of lesions, percentage of non-enhancing (necrotic) areas, and growth pattern. CT findings and Fuhrman grade were compared. Nineteen of 22 (86.4%) cystic ccRCC were low grade (Fuhrman grades 1-2). There was no significant correlation between tumor size and grade in cystic ccRCC (P = 0.43). In predominantly solid ccRCC, there was significant correlation between tumor size and grade (P < 0.0001). Thirty-eight of 43 (88.4%) infiltrative ccRCC were high grade (Fuhrman grades 3-4). Logistic regression showed tumor size and infiltrative growth were significantly associated with grades 3-4 (P = 0.00083 and P = 0.0059). Cystic ccRCC tends to be low grade. Infiltrative growth and larger tumor size may increase the likelihood of high grade ccRCC.

Multiparametric magnetic resonance imaging for the differentiation of low and high grade clear cell renal carcinoma

PURPOSE

To retrospectively evaluate the ability of magnetic resonance (MR) imaging to differentiate low from high Fuhrman grade renal cell carcinoma (RCC).

MATERIALS AND METHODS

MR images from 80 consecutive pathologically proven RCC (57 clear cell, 16 papillary and 7 chromophobe) were evaluated. Double-echo chemical shift, dynamic contrast-enhanced T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps were reviewed independently. Signal intensity index (SII), tumour-to-spleen SI ratio (TSR), ADC ratio, wash-in (WiI) and wash-out indices (WoI) between different phases were calculated and compared to pathological grade and size. The Fuhrman scoring system was used. Low grade (score ≤ 2) and high grade (score ≥ 3) tumours were compared using univariate and multivariate analyses.

RESULTS

No associations between grade and imaging factors were found for papillary and chromophobe RCCs. For clear cell RCCs, there was a significant association between the grade and parenchymal WiI (WiI2) (P = 0.02) or ADCr (P = 0.03). A significant association between tumour grade and size (P = 0.01), WiI2 (P = 0.02) and ADCr (P = 0.05) remained in multivariate analysis.

CONCLUSIONS

Multiparametric MRI can be used to accurately differentiate low Fuhrman grade clear cell RCC from high grade. High Fuhrman grade (≥ 3) RCCs were larger, had lower parenchymal wash-in indices and lower ADC ratios than low grade.

KEY POINTS

• Fuhrman grade of clear cell RCC can be differentiated with multiparametric MR imaging. • Fuhrman grade significantly differed for size, parenchymal wash-in index and ADC ratio. • No significant associations were found for papillary and chromophobe renal cell carcinoma.

[Quantitative imaging in uro-oncology]

INTRODUCTION

Imaging currently performed in uro-oncology could provide useful information. The use of all this information could help to better understand tumor growth and response to treatment. Therefore, it seems interesting to review the knowledge, to describe the main techniques currently available in many centers or in process and to clarify their results.

MATERIALS AND METHODS

A systematic literature review was conducted in the PubMed database to identify all imaging techniques performed for therapeutic evaluation in uro-oncology. The keywords used were: cancer, kidney, bladder, prostate, urology biomarkers, imaging, ultrasound, CT-scan, MRI, PET-CT, RECIST, BOLD, ASL, gold DWI Diffusion, contrast, F-miso. The first publications identified were analyzed to search unidentified studies by the selected keywords.

RESULTS

From simple to more complex morphology data from functional imaging (PET, MRI), data obtained from imaging helps to better understand tumor growth and response to treatment. Although optimizations are coming, all the techniques reported are available in many centers or going to be.

CONCLUSION

The imaging evaluation in onco-urology can bring a large amount of information. Integrating to research protocols is now essential to sustain this activity.

MRI phenotype in renal cancer: is it clinically relevant?

Renal cell carcinoma (RCC) is most commonly diagnosed as an incidental finding on cross-sectional imaging and represents a significant clinical challenge. Although most patients have a surgically curable lesion at the time of diagnosis, the variability in the biologic behavior of the different histologic subtypes and tumor grade of RCC, together with the increasing array of management options, creates uncertainty for the optimal clinical approach to individual patients. State-of-the-art magnetic resonance imaging (MRI) provides a comprehensive assessment of renal lesions that includes multiple forms of tissue contrast as well as functional parameters, which in turn provides information that helps to address this dilemma. In this article, we review this evolving and increasingly comprehensive role of MRI in the detection, characterization, perioperative evaluation, and assessment of the treatment response of renal neoplasms. We emphasize the ability of the imaging "phenotype" of renal masses on MRI to help predict the histologic subtype, grade, and clinical behavior of RCC.

Routinely performed multiparametric magnetic resonance imaging helps to differentiate common subtypes of renal tumours

OBJECTIVES

To retrospectively evaluate the ability of multiparametric magnetic resonance (MR) imaging to differentiate renal tumours.

METHODS

MR images from 100 consecutive pathologically proven solid renal tumours without macroscopic fat [57 clear cell, 16 papillary and 7 chromophobe renal cell carcinomas (RCCs), 16 oncocytomas and 4 minimal fat angiomyolipomas (AMLs)] between 2009 and 2012 were evaluated. Two radiologists blinded to pathology results independently reviewed double-echo chemical shift, dynamic contrast-enhanced T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps. Signal intensity index (SII), tumour-to-spleen SI ratio (TSR), ADC ratio, wash-in (WiI) and wash-out indices (WoI) between different phases were calculated.

RESULTS

There were significant differences between papillary RCCs and other renal tumours for arterial WiI (P < 0.001), initial WoI (P = 0.006) and ADC ratio (P < 0.001); between chromophobe RCCs and oncocytomas for TSR (P = 0.02), parenchymal WiI (P = 0.03), late WiI (P = 0.02), initial WoI (P = 0.03) and late WoI (P = 0.04); and between clear cell RCCs and oncocytomas for SII (P = 0.01) and parenchymal WiI (P = 0.01). Papillary RCCs were distinguished from other tumours (sensitivity 37.5 %, specificity 100 %) and oncocytomas from chromophobe RCCs (sensitivity 25 %, specificity 100 %) and clear cell RCCs (sensitivity 100 %, specificity 94.2 %).

CONCLUSION

MR imaging provides criteria able to accurately distinguish papillary RCCs from other tumours and oncocytomas from chromophobe and clear cell RCCs.

KEY POINTS

• Multiparametric MR parameters accurately distinguish papillary RCCs with high specificity (100 %). • Oncocytomas can be distinguished from chromophobe RCCs with high specificity (100 %). • Oncocytomas can be distinguished from clear cell RCCs with high specificity (94.2 %). • In oncocytomatosis, imaging follow-up with such parameters analysis could be promoted.