Corticomedullary Phase Images Research Articles

MP09-15 RADIOMICS ANALYSIS USING DYNAMIC COMPUTED TOMOGRAPHY FOR PREOPERATIVE RISK ASSESSMENT IN UPPER URINARY TRACT UROTHELIAL CARCINOMA

You have accessJournal of UrologyCME1 Apr 2023MP09-15 RADIOMICS ANALYSIS USING DYNAMIC COMPUTED TOMOGRAPHY FOR PREOPERATIVE RISK ASSESSMENT IN UPPER URINARY TRACT UROTHELIAL CARCINOMA Hiroki Kaneko, Koichiro Kimura, Hajime Tanaka, Wei Chen, Kasumi Yoshitomi, Takahiko Soma, Masaki Kobayashi, Motohiro Fujiwara, Yuki Nakamura, Bo Fan, Yudai Ishikawa, Shohei Fukuda, Yuma Waseda, Soichiro Yoshida, Minato Yokoyama, and Yasuhisa Fujii Hiroki KanekoHiroki Kaneko More articles by this author , Koichiro KimuraKoichiro Kimura More articles by this author , Hajime TanakaHajime Tanaka More articles by this author , Wei ChenWei Chen More articles by this author , Kasumi YoshitomiKasumi Yoshitomi More articles by this author , Takahiko SomaTakahiko Soma More articles by this author , Masaki KobayashiMasaki Kobayashi More articles by this author , Motohiro FujiwaraMotohiro Fujiwara More articles by this author , Yuki NakamuraYuki Nakamura More articles by this author , Bo FanBo Fan More articles by this author , Yudai IshikawaYudai Ishikawa More articles by this author , Shohei FukudaShohei Fukuda More articles by this author , Yuma WasedaYuma Waseda More articles by this author , Soichiro YoshidaSoichiro Yoshida More articles by this author , Minato YokoyamaMinato Yokoyama More articles by this author , and Yasuhisa FujiiYasuhisa Fujii More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The prognosis of patients with upper tract urothelial cancer (UTUC) is generally poor. Although neoadjuvant treatment may improve the survival outcomes after surgery, further explorations are required to identify the patients at high risk for recurrence preoperatively. In this study, we investigate the utility of radiomics analysis using computed tomography (CT) for preoperative risk assessment in UTUC. METHODS: We analyzed 55 patients with UTUC who underwent radical surgery (approval # M2019-192). All the patients were required to have dynamic CT before surgery, on which the tumor was identified and evaluable. Radiomics analysis was performed on the corticomedullary phase images using LIFEx software. The tumor contour was manually outlined on axial slices, and the volume of interest (VOI) of the tumor was set in each case. Based on tumor segmentation, 124 radiomic features were extracted from each VOI, and important features for predicting metastatic recurrence were identified using Boruta algorithm. Prognostic impact of the radiomic features was assessed using multivariable analysis incorporating preoperative clinical variables. RESULTS: The tumor was located in renal pelvis and ureter in 32 (58%) and 23 (42%) patients, respectively. Clinical T stages were Ta-1/2/≥3 in 30 (55%)/15 (27%)/10 (18%) patients, respectively. Hydronephrosis was observed in 15 (27%). On the surgical specimen, 18 cases (33%) had pathological ≥T3. During the median follow-up of 33 months, 8 patients (15%) experienced metastatic recurrence. The Boruta algorithm identified INTENSITY.BASED_90thPercentile.HU (IB90P) as an important feature for metastatic recurrence (Figure 1). The receiver operating characteristic curve defined the cut-off value of IB90P as 64.5. In multivariable Cox-proportional hazard analysis, IB90P <64.5 (HR=9.9, p=0.002) and hydronephrosis (HR=7.1, p=0.02) were independent risk factors of metastatic recurrence. Two-year metastasis-free survival were 45% and 95% in the patients with IB90P <64.5 and ≥64.5, respectively (Figure 1). CONCLUSIONS: Radiomics analysis using preoperative CT identified an independent risk factor for metastatic recurrence. It may help the preoperative risk stratification and decision making in the management of patients with UTUC. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e111 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hiroki Kaneko More articles by this author Koichiro Kimura More articles by this author Hajime Tanaka More articles by this author Wei Chen More articles by this author Kasumi Yoshitomi More articles by this author Takahiko Soma More articles by this author Masaki Kobayashi More articles by this author Motohiro Fujiwara More articles by this author Yuki Nakamura More articles by this author Bo Fan More articles by this author Yudai Ishikawa More articles by this author Shohei Fukuda More articles by this author Yuma Waseda More articles by this author Soichiro Yoshida More articles by this author Minato Yokoyama More articles by this author Yasuhisa Fujii More articles by this author Expand All Advertisement PDF downloadLoading ...

Four-phase computed tomography helps differentiation of renal oncocytoma with central hypodense areas from clear cell renal cell carcinoma

To explore the utility of four-phase computed tomography (CT) in distinguishing renal oncocytoma with central hypodense areas from clear cell renal cell carcinoma (ccRCC). Eighteen patients with oncocytoma and 63 patients with ccRCC presenting with central hypodense areas were included in this study. All patients underwent four-phase CT imaging including the excretory phases later than 20 min after contrast injection. Two blinded experienced radiologists visually reviewed the enhancement features of the central hypodense areas in the excretory phase images and selected the area demonstrating the greatest degree of enhancement of the tumor in the corticomedullary phase images. Regions of interest (ROIs) were placed in the same location in each of the three contrast-enhanced imaging phases. Additionally, ROIs were placed in the adjacent normal renal cortex for normalization. The ratio of the lesion to cortex attenuation (L/C) for the three contrast-enhanced imaging phases and absolute de-enhancement were calculated. The receiver operating characteristic curve was used to obtain the cut-off values. Complete enhancement inversion of the central areas was observed in 12 oncocytomas (66.67%) and 16 ccRCCs (25.40%) (P = 0.003). Complete enhancement inversion combined with L/C in the corticomedullary phase lower than 1.0 (P < 0.001) or absolute de-enhancement lower than 42.5 HU (P < 0.001) provided 86.42% and 85.19% accuracy, 61.11% and 55.56% sensitivity, 93.65% and 93.65% specificity, 73.33% and 71.43% positive predictive value (PPV), and 89.39% and 88.06% negative predictive value (NPV), respectively, for the diagnosis of oncocytomas. Combined with complete enhancement inversion, L/C in the corticomedullary phase lower than 1.0 and absolute de-enhancement lower than 42.5 HU provided 87.65%, 55.56%, 96.83%, 83.33%, and 88.41% of accuracy, sensitivity, specificity, PPV, and NPV, respectively, for the diagnosis of oncocytomas. The combination of enhancement features of the central hypodense areas and the peripheral tumor parenchyma can help distinguish oncocytoma with central hypodense areas from ccRCC.

Utilisation of virtual non-contrast images and virtual mono-energetic images acquired from dual-layer spectral CT for renal cell carcinoma: image quality and radiation dose

BackgroundRenal cell carcinoma (RCC) is the most common renal malignant tumour. We evaluated the potential value and dose reduction of virtual non-contrast (VNC) images and virtual monoenergetic images (VMIs) from dual-layer spectral CT (DL-CT) in the diagnosis of RCC.ResultsSixty-two patients with pathologically confirmed RCC who underwent contrast-enhanced DL-CT were retrospectively analysed. For the comparison between true non-contrast (TNC) and VNC images of the excretory phase, the attenuation, image noise, signal-to-noise ratio (SNR) and subjective image quality of tumours and different abdominal organs and tissues were evaluated. To compare corticomedullary phase images and low keV VMIs (40 to 100 keV) from the nephrographic phase, the attenuation, image noise, SNR and subjective lesion visibility of the tumours and renal arteries were evaluated. For the tumours, significant differences were not observed in attenuation, noise or SNR between TNC and VNC images (p > 0.05). For the abdominal organs and tissues, except for fat, the difference in attenuation was 100% within 15 HU and 96.78% within 10 HU. The subjective image quality of TNC and VNC images was equivalent (p > 0.05). The attenuation of lesions in 40 keV VMIs and renal arteries in 60 keV VMIs were similar to those in the corticomedullary images (p > 0.05). The subjective lesion visibility in low keV VMIs is slightly lower than that in the corticomedullary images (p < 0.05). Using VNC and VMIs instead of TNC and corticomedullary phase images could decrease the radiation dose by 50.5%.ConclusionVNC images and VMIs acquired from DL-CT can maintain good image quality and decrease the radiation dose for diagnosis of RCC.

Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.

To develop a 3D U-Net-based deep learning model for automated segmentation of kidney and renal mass, and detection of renal mass in corticomedullary phase of computed tomography urography (CTU). Data on 882 kidneys obtained from CTU data of 441 patients with renal mass were used to learn and evaluate the deep learning model. The CTU data of 35 patients with small renal tumors (diameter ≤ 1.5 cm) were used for additional testing. The ground truth data for the kidney, renal tumor, and cyst were manually annotated on corticomedullary phase images of CTU. The proposed segmentation model for kidney and renal mass was constructed based on a 3D U-Net. The segmentation accuracy was evaluated through the Dice similarity coefficient (DSC). The volume of the maximum 3D volume of interest of renal tumor and cyst in the predicted segmentation by the model was used as an identification indicator, while the detection performance of the model was evaluated by the area under the receiver operation characteristic curve. The proposed model showed a high accuracy in segmentation of kidney and renal tumor, with average DSC of 0.973 and 0.844, respectively. It performed moderately in the renal cyst segmentation, with an average DSC of 0.536 in the test set. Also, this model showed good performance in detecting renal tumor and cyst. The proposed automated segmentation and detection model based on 3D U-Net shows promising results for the segmentation of kidney and renal tumor, and the detection of renal tumor and cyst. • The segmentation model based on 3D U-Net showed high accuracy in segmentation of kidney and renal neoplasm, and good detection performance of renal neoplasm and cyst in corticomedullary phase of CTU. • The segmentation model based on 3D U-Net is a fully automated aided diagnostic tool that could be used to reduce the workload of radiologists and improve the accuracy of diagnosis. • The segmentation model based on 3D U-Net would be helpful to provide quantitative information for diagnosis, treatment, surgical planning, etc.

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning.

OBJECTIVE. This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. MATERIALS AND METHODS. This retrospective study included 1807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 patients between 2012 and 2016. Masses were classified as malignant (n = 136) or benign (n = 32). The dataset was randomly divided into five subsets: four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images). The Inception-v3 architecture convolutional neural network (CNN) model was used. The AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Multivariate logistic regression analysis was also performed. RESULTS. Malignant and benign lesions showed no significant difference of size. The AUC value of corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022). The highest accuracy (88%) was achieved in corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, sex, and lesion size. CONCLUSION. A deep learning method with a CNN allowed acceptable differentiation of small (≤ 4 cm) solid renal masses in dynamic CT images, especially in the corticomedullary image model.

Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images

To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell carcinomas (ccRCCs). The multidetector computed tomography images of 227 ccRCCs were retrospectively analyzed. For each ccRCC, 14 conventional image features (CIFs) were extracted manually by two radiologists, and 556 texture features (TFs) were extracted by a free software application, MaZda (version 4.6). The high-dimensional dataset of these RSs was reduced using the least absolute shrinkage and selection operator. Five minimum mean squared error models (minMSEMs) for predicting the ccRCC histopathological grades were constructed from the CIFs, the TFs of the corticomedullary phase images (CMP), and the TFs of the parenchyma phase (PP) images and their combinations, respectively abbreviated as CIF-minMSEM, CMP-minMSEM, PP-minMSEM, CIF+CMP-minMSEM, and CIF+PP-minMSEM. The RSs of each model were fed 30 times consecutively into an ANN for machine learning, and the predictive accuracy of each time ML was recorded for the statistical analysis. The five predictive models were constructed from 12, 19, and 10 features selected from the CIFs, the TFs of the CMP images, and that of PP images, respectively. On the basis of their accuracy across the whole cohort, the five models were ranked as follows: CIF+CMP-minMSEM (accuracy: 94.06% ± 1.14%), CIF + PP-minMSEM (accuracy: 93.32% ± 1.23%), CIF-minMSEM (accuracy: 92.26% ± 1.65%), CMP-minMSEM (accuracy: 91.76% ± 1.74%), and PP-minMSEM (accuracy: 90.89% ± 1.47%). Machine learning based on ANN helped establish an optimal predictive model, and TFs contributed to the development of high accuracy predictive models. The CIF+CMP-minMSEM showed the greatest accuracy for differentiating low- and high-grade ccRCCs.

Diagnostic Accuracy of Attenuation Difference and Iodine Concentration Thresholds at Rapid-Kilovoltage-Switching Dual-Energy CT for Detection of Enhancement in Renal Masses.

OBJECTIVE. The objective of our study was to evaluate iodine concentration and attenuation change in Hounsfield unit (ΔHU) thresholds to diagnose enhancement in renal masses at rapid-kilovoltage-switching dual-energy CT (DECT). MATERIALS AND METHODS. We evaluated 30 consecutive histologically confirmed solid renal masses (including nine papillary renal cell carcinomas [RCCs]) and 27 benign cysts (17 simple and 10 hemorrhagic or proteinaceous cysts) with DECT December 2016 and May 2018. A blinded radiologist measured iodine concentration (in milligrams per milliliter) and ΔHU (attenuation on enhanced CT - attenuation on unenhanced CT) using 70-keV corticomedullary (CM) phase virtual monochromatic and 120-kVp nephrographic (NG) phase images. The accuracies of previously described enhancement thresholds were compared by ROC curve analysis. RESULTS. An iodine concentration of ≥ 2.0 mg/mL and an iodine concentration of ≥ 1.2 mg/mL achieved sensitivity, specificity, and the area under the ROC curve (AUC) of 73.3%, 100.0%, and 0.87 and 86.7%, 100.0%, and 0.93, respectively. On 70-keV CM phase images, ΔHU ≥ 20 HU and ΔHU ≥ 15 HU yielded sensitivity, specificity, and AUC of 80.0%, 100.0%, and 0.90 and 90.0%, 100.0%, and 0.95, respectively. The numbers of incorrectly classified papillary RCCs were as follows: iodine concentration of ≥ 2.0 mg/mL, 77.8% (7/9; range, 0.7-1.6 mg/mL); iodine concentration of ≥ 1.2 mg/mL, 44.4% (4/9; range, 0.7-0.9 mg/mL); ΔHU ≥ 20 HU on 70-keV CM phase images, 66.7% (6/9; range, 4-17 HU); and ΔHU ≥ 15 HU on 70-keV DECT images, 33.3% (3/9; 4-12 HU). No cyst pseudoenhancement occurred on DECT. For 120-kVp NG phase DECT, ΔHU ≥ 20 HU and ΔHU ≥ 15 HU yielded sensitivity, specificity, and AUC of 93.3%, 96.3%, and 0.95 and 100.0%, 88.9%, and 0.94, respectively. With ΔHU ≥ 20 HU, 22.2% (2/9) (range, 15-18 HU) of papillary RCCs were misclassified and there was one pseudoenhancing cyst. With ΔHU ≥ 15 HU, no papillary RCCs were misclassified but 11.1% (3/27) of cysts showed pseudoenhancement. Only an iodine concentration of ≥ 2.0 mg/mL showed significantly lower accuracy than other measures (p = 0.031-0.045). CONCLUSION. DECT applied in the CM phase performed best using an iodine concentration of ≥ 1.2 mg/mL or a 70-keV ΔHU ≥ 15 HU; these parameters improved sensitivity for the detection of enhancement in renal masses without instances of cyst pseudoenhancement.

Predictive ability of renal cortex enhancement in dynamic computed tomography for residual renal function after nephroureterectomy: Comparison with 99mTc‐diethylenetriaminopentacetic acid renography and validation study

To estimate postoperative residual renal function after radical nephroureterectomy for upper tract urothelial carcinoma using the preoperative dynamic computed tomography renal cortex enhancement ratio in comparison with the split kidney glomerular filtration rate measured by 99m Tc-diethylenetriaminopentacetic acid renography. A total of 47 patients who received radical nephroureterectomy and underwent both preoperative dynamic computed tomography and renography were the model-development cohort; and 109 patients who underwent dynamic computed tomography alone were the validation cohort. Postoperative renal function of the unremoved kidney was estimated using the following formulas: preoperative estimated glomerular filtration rate × the percentage of total renal cortex radiodensity for the intact kidney in Hounsfield units obtained from corticomedullary phase images in the computed tomography-based model, or the percentage of the total glomerular filtration rate measured by renography in the nuclear model. The correlation between observed and estimated postoperative renal function was determined. The computed tomography-based prediction model derived from linear regression analysis was validated externally. The correlation of computed tomography-based split renal function with the observed postoperative estimated glomerular filtration rate (r = 0.80) was equivalent to that of nuclear split renal function (r = 0.78). In the validation cohort, the computed tomography-based prediction model showed an equivalently strong correlation (r = 0.78). The present study showed that the percentage of total renal cortex radiodensity for the intact kidney is a useful tool for predicting unremoved kidney function in upper tract urothelial carcinoma patients, thereby allowing appropriate patient selection for perioperative cisplatin-based combination chemotherapy.

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation

ObjectiveTo develop externally validated, reproducible, and generalizable models for distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning-based quantitative computed tomography (CT) texture analysis (qCT-TA). Materials and methodsSixty-eight RCCs were included in this retrospective study for model development and internal validation. Another 26 RCCs were included from public databases (The Cancer Genome Atlas-TCGA) for independent external validation. Following image preparation steps (reconstruction, resampling, normalization, and discretization), 275 texture features were extracted from unenhanced and corticomedullary phase CT images. Feature selection was firstly done with reproducibility analysis by three radiologists, and; then, with a wrapper-based classifier-specific algorithm. A nested cross-validation was performed for feature selection and model optimization. Base classifiers were the artificial neural network (ANN) and support vector machine (SVM). Base classifiers were also combined with three additional algorithms to improve generalizability performance. Classifications were done with the following groups: (i), non-clear cell RCC (non-cc-RCC) versus clear cell RCC (cc-RCC) and (ii), cc-RCC versus papillary cell RCC (pc-RCC) versus chromophobe cell RCC (chc-RCC). Main performance metric for comparisons was the Matthews correlation coefficient (MCC). ResultsNumber of the reproducible features is smaller for the unenhanced images (93 out of 275) compared to the corticomedullary phase images (232 out of 275). Overall performance metrics of the machine learning-based qCT-TA derived from corticomedullary phase images were better than those of unenhanced images. Using corticomedullary phase images, ANN with adaptive boosting algorithm performed best for discrimination of non-cc-RCCs from cc-RCCs (MCC = 0.728) with an external validation accuracy, sensitivity, and specificity of 84.6%, 69.2%, and 100%, respectively. On the other hand, the performance of the machine learning-based qCT-TA is rather poor for distinguishing three major subtypes. The SVM with bagging algorithm performed best for discrimination of pc-RCC from other RCC subtypes (MCC = 0.804) with an external validation accuracy, sensitivity, and specificity of 69.2%, 71.4%, and 100%, respectively. ConclusionsMachine learning-based qCT-TA can distinguish non-cc-RCCs from cc-RCCs with a satisfying performance. On the other hand, the performance of the method for distinguishing three major subtypes is rather poor. Corticomedullary phase CT images provide much more valuable texture parameters than unenhanced images.

Enhancement Threshold of Small (< 4 cm) Solid Renal Masses on CT.

The objective of our study was to determine whether small (< 4 cm) solid renal cell carcinomas (RCCs) enhance on CT below the published enhancement thresholds. For this retrospective study, we reviewed CT examinations of patients with pathologically characterized small solid renal masses. Patients with lipid-rich angiomyolipomas were excluded. The study included consecutive patients who had pathologically characterized small (< 4 cm) renal masses and had undergone CT during the un-enhanced phase and nephrographic phase (NP). Most CT examinations also included imaging during the corticomedullary phase (CMP); four patients did not have CMP images. The CT examinations were performed on a 64-MDCT scanner. Similar-sized ROIs were placed on each lesion on unenhanced and enhanced images and enhancement (in Hounsfield units) was calculated. Masses were considered solid with a threshold enhancement greater than either 15 or 20 HU. There were 137 masses in 137 patients, 90 men and 47 women (age range, 21-91 years; median age, 65 years). The renal masses were 1.0-3.9 cm (median, 2.4 cm). Of the 137 masses, 117 (85.4%) were malignant and 20 (14.6%) were benign. One malignant mass and three benign masses did not have CMP images. Twenty of the 116 (17.2%) renal cell carcinomas (RCCs) did not reach the 15-HU threshold enhancement on CMP images, nine of 117 masses (7.7%) did not reach the 15-HU enhancement threshold on NP images, and four of 117 masses (3.4%) did not reach the 15-HU enhancement threshold on both CMP and NP images. The numbers of masses that did not reach the 20-HU enhancement threshold were 24 of 116 masses (20.7%) on CMP images, 14 of 117 masses (11.9%) on NP images, and 11 of 117 masses (9.4%) on both CMP and NP images. Depending on the phase or enhancement threshold, 13.6-59.1% of papillary RCCs did not reach the enhancement threshold. A minority of small RCCs do not reach either a 15- or 20-HU enhancement threshold and might be misinterpreted as a hyperattenuating cyst. Most RCCs below these enhancement thresholds are papillary RCC.

Variability in computed tomography diameter measurements of solid renal masses

To determine whether the frequency of intra-observer measurement discrepancies ≥5 mm for solid renal masses varies by renal mass characteristics and CT contrast phase. This HIPAA-compliant retrospective study was approved by our IRB. We selected single CT images performed during the nephrographic phase (NP) of renal enhancement in 97 patients, each with a single solid renal mass. Mass location, margin, heterogeneity, and growth pattern were assessed. Six readers measured each mass on two occasions >3 weeks apart. Readers also measured the masses on images in 50 patients who had corticomedullary phase (CMP) images obtained during the same study. Results were assessed using Chi-square/Fisher's exact and Wilcoxon Signed Rank tests, and logistic regression analyses. For NP to NP comparisons, intra-reader measurement differences ≥5 mm were seen for 3.7% (17/463) of masses <4 cm, but increased to 16.8% (20/119) for masses >4 cm (p < 0.0001). Masses with poorly defined margins (15.9% [22/138] vs. 3.4% [15/444] for well-defined margins, p < 0.0001) and heterogeneity (15.3% [22/144], vs. 5.0% [14/282] for minimally heterogeneous, vs. 0.6% [1/156] for homogeneous, p < 0.0001), were more frequently associated with measurement differences ≥5 mm. Differences ≥5 mm were more frequent when only CMP images were utilized (14% [42/299]), or when CMP images were compared with NP images (26% [77/299]). A ≥5 mm intra-reader variation in measured size of solid renal masses <4 cm is uncommon for NP to NP comparisons. Variation increases when masses are ≥4 cm, poorly defined, or heterogeneous; or when CMP images are utilized.

Dual-Source Dual-Energy CT Evaluation of Complex Cystic Renal Masses

The purpose of this study was to assess the value of dual-source dual-energy CT in the evaluation of complex cystic renal masses. Seventy patients underwent contrast-enhanced dual-energy CT that included true unenhanced images acquired in single-energy mode, corticomedullary phase images acquired in dual-energy mode, and nephrographic phase images acquired in single-energy mode. Virtual unenhanced, blended weighted-average, and color-coded iodine overlay images were reconstructed. The acceptance level and image quality of virtual and true unenhanced images were evaluated. Contrast enhancement on both true unenhanced or blended weighted-average images and color-coded iodine overlay images was evaluated with both calculation in regions of interest and use of confidence level scales. Radiation dose parameters were estimated. Virtual unenhanced images of 70 lesions (97.2%) and true unenhanced images of 72 lesions (100%) were judged acceptable (p = 0.5). The mean quality score of virtual unenhanced images was 2.0 ± 0.7 and of true unenhanced images was 1.5 ± 0.5 (p < 0.001). Mean contrast enhancement measured on true unenhanced and blended weighted-average images was 45.9 ± 15.9 HU (range, 21-78 HU) and on color-coded iodine overlay images was 47.3 ± 16.8 HU (range, 22-75 HU) with no significant differences. Enhancement was excluded on color-coded iodine overlay images with a significantly (p < 0.03) higher level of confidence than it was on true unenhanced and blended weighted-average images. The mean dose reduction with use of a combined dual- and single-energy dual-phase CT protocol was 29.1% ± 11.9% (p < 0.001). Dual-source dual-energy CT is a reliable imaging technique in the evaluation of complex cystic renal masses. True unenhanced images can be replaced by virtual unenhanced images with considerable radiation dose reduction. The color-coded iodine overlay technique is a useful tool for both excluding and identifying endocystic enhancement.

Renal Cell Carcinoma: Dynamic Contrast-enhanced MR Imaging for Differentiation of Tumor Subtypes—Correlation with Pathologic Findings

To retrospectively evaluate whether the enhancement patterns of pathologically proved clear cell, papillary, and chromophobe renal cell carcinomas (RCCs) measured on clinical dynamic contrast agent-enhanced magnetic resonance (MR) images permit accurate diagnosis of RCC subtype. This study was Institutional Review Board approved and HIPAA compliant; informed consent was waived. One hundred twelve patients (76 men, 36 women; age range, 25-88 years; mean age, 58.1 years) underwent MR imaging of 113 renal masses (mean diameter, 5.4 cm) with pathologic diagnoses of clear cell (n = 75), papillary (n = 28), or chromophobe (n = 10) RCC. A 1.5-T clinical MR protocol was used before and after (corticomedullary and nephrographic phases) intravenous administration of contrast agent. Region-of-interest measurements within tumor and uninvolved renal cortex were used to calculate percentage signal intensity change and tumor-to-cortex enhancement index. Subtype groups were compared by using linear mixed-effects models. Receiver operating characteristic (ROC) curve analysis was performed for the comparison of clear cell and papillary RCCs. On both the corticomedullary and nephrographic phase images, clear cell RCCs showed greater signal intensity change (205.6% and 247.1%, respectively) than did papillary RCCs (32.1% and 96.6%, respectively) (P < .001). Chromophobe RCCs showed intermediate change (109.9% and 192.5%, respectively). The tumor-to-cortex enhancement indexes at corticomedullary and nephrographic phases were largest for clear cell RCCs (1.4 and 1.2, respectively), smallest for papillary RCCs (0.2 and 0.4, respectively), and intermediate for chromophobe RCCs (0.6 and 0.8, respectively). Signal intensity changes on corticomedullary phase images were the most effective parameter for distinguishing clear cell and papillary RCC (area under ROC curve, 0.99); a threshold value of 84% permitted distinction with 93% sensitivity and 96% specificity. Clear cell, papillary, and chromophobe RCCs demonstrate different patterns of enhancement on two-time point clinical dynamic contrast-enhanced MR images, allowing their differentiation with high sensitivity and specificity.

Different phases of renal enhancement: role in detecting and characterizing renal masses during helical CT.

Although helical CT offers greater flexibility in data acquisition and reconstruction than does conventional axial CT, new opportunities for error must be understood so that optimal protocols are used that minimize the likelihood of misdiagnosis. Most nonrenal abdominal helical CT scans are obtained late in the corticomedullary phase of renal enhancement because of the preferable enhancement of other parenchymal organs. CT evaluation of the kidneys during the corticomedullary phase or at an intermediate phase between the corticomedullary and nephrographic phases has significant limitations. Therefore, dedicated renal CT performed for the detection of suspected renal masses or for the characterization of known renal masses must include images obtained during later phases of enhancement (i.e., nephrographic or excretory phase). Nephrographic or excretory phase images appear to be similar to one another but superior to corticomedullary phase images in the ability to both detect and characterize renal masses. Corticomedullary phase images should always be obtained when information about the renal vasculature is desired or when there is a possibility that a detected renal mass may represent an aneurysm or an arteriovenous malformation or fistula. Optimal technique of helical CT for staging renal cancers may require use of both corticomedullary and nephrographic or excretory phase images, although work in this area is still preliminary.

Multiphasic renal CT: comparison of renal mass enhancement during the corticomedullary and nephrographic phases.

To evaluate thin-section computed tomography (CT) performed during the corticomedullary and nephrographic phases of contrast material enhancement in the characterization of renal masses. A prospective study of 30 patients was undertaken with CT to characterize 31 "indeterminate" renal masses. In all patients, 5-mm-thick, contiguous, high-tube-current (320-340-mA) scans were obtained through the kidneys before (axial mode), during (helical mode, 25-second delay, corticomedullary-phase images), and after (axial mode, 120-second delay, nephrographic-phase images) administration of a 117-second biphasic injection of intravenous contrast material. Eight of 16 neoplasms measured less than 20 HU on CT scans obtained without contrast material enhancement; measurements of two of these corresponded to "cyst attenuation" during the corticomedullary phase. Enhancement of 10 HU or greater was demonstrated in 11 neoplasms during the corticomedullary phase and in all neoplasms in the nephrographic phase. No enhancement was seen in 15 radiologically benign cysts. Both renal neoplasms and normal renal cortex demonstrated significantly greater enhancement in the nephrographic phase compared with that in the corticomedullary phase (P = .0002 and P < .0001, respectively). Enhancement of renal neoplasms is time dependent and may not be evident in hypovascular tumors analyzed during the early corticomedullary phase. Reliance on absolute CT attenuation measurements, without use of internal standards as controls, may lead to misdiagnosis of neoplasms as cysts.

Renal masses: assessment of corticomedullary-phase and nephrographic-phase CT scans.

To evaluate the role of thin-section helical computed tomography (CT) performed during the corticomedullary phase (CMP) and nephrographic phase (NP) of contrast enhancement in the detection and characterization of renal masses. Renal CT scans and medical records of 33 patients were retrospectively reviewed. In all examinations, 5-mm-thick, contiguous, helical-mode scans were obtained before and 40 seconds after initiation of dynamic bolus injection of contrast material (CMP images); 5-mm-thick, contiguous, axial-mode scans were obtained after completion of CMP scanning (NP images). At review of CMP, NP, and combination images, 259, 389, and 417 lesions, respectively, were identified. The greatest difference in detection occurred in the renal medulla, with 25 lesions identified on CMP images and 111 lesions identified on NP images. False-positive results occurred when CMP images were reviewed without NP images. CT scans obtained only during the CMP of contrast enhancement fail to depict many renal masses that are easily seen on NP images.