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Voicu IP, Dotta F, Napolitano A, Caulo M, Piccirilli E, D’Orazio C, Carai A, Miele E, Vinci M, Rossi S, Cacchione A, Vennarini S, Del Baldo G, Mastronuzzi A, Tomà P, Colafati GS. Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study. Cancers (Basel) 2024; 16:2578. [PMID: 39061217 PMCID: PMC11274924 DOI: 10.3390/cancers16142578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/02/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.
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Affiliation(s)
- Ioan Paul Voicu
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
| | - Francesco Dotta
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
- Department of Innovative Technologies in Medicine and Dentistry, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Antonio Napolitano
- Medical Physics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy;
| | - Eleonora Piccirilli
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy;
| | - Claudia D’Orazio
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
| | - Andrea Carai
- Neurosurgery Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Evelina Miele
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Maria Vinci
- Paediatric Cancer Genetics and Epigenetics Research Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Sabrina Rossi
- Pathology Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Antonella Cacchione
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Sabina Vennarini
- Pediatric Radiotherapy Unit, IRCCS Fondazione Istituto Nazionale Tumori, 20133 Milano, Italy;
| | - Giada Del Baldo
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Angela Mastronuzzi
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Paolo Tomà
- Radiology and Bioimaging Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Giovanna Stefania Colafati
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
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Tanaka F, Maeda M, Nakayama R, Inoue K, Kishi S, Kogue R, Umino M, Kitano Y, Obara M, Sakuma H. A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study. Diagnostics (Basel) 2024; 14:1236. [PMID: 38928651 PMCID: PMC11202847 DOI: 10.3390/diagnostics14121236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/06/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors. METHODS Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933. CONCLUSIONS The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.
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Affiliation(s)
- Fumine Tanaka
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Masayuki Maeda
- Department of Neuroradiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Ryohei Nakayama
- Department of Electronic and Computer Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu 5250058, Shiga, Japan
| | - Katsuhiro Inoue
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Seiya Kishi
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Ryota Kogue
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Maki Umino
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Yotaro Kitano
- Department of Neurosurgery, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Makoto Obara
- MR Clinical Science, Philips Japan, 2-13-37 Konan, Minato 1088507, Tokyo, Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
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Chen Y, Huang N, Zheng Y, Wang F, Cao D, Chen T. Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study. Eur J Radiol 2024; 170:111199. [PMID: 38104494 DOI: 10.1016/j.ejrad.2023.111199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 10/13/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors. METHOD From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index. RESULTS For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05). CONCLUSIONS Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.
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Affiliation(s)
- Yu Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Nan Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Yingyan Zheng
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Feng Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China; Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350212, China.
| | - Tanhui Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China.
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Jellema PEJ, Wijnen JP, De Luca A, Mutsaerts HJMM, Obdeijn IV, van Baarsen KM, Lequin MH, Hoving EW. Advanced intraoperative MRI in pediatric brain tumor surgery. Front Physiol 2023; 14:1098959. [PMID: 37123260 PMCID: PMC10134397 DOI: 10.3389/fphys.2023.1098959] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/29/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction: In the pediatric brain tumor surgery setting, intraoperative MRI (ioMRI) provides "real-time" imaging, allowing for evaluation of the extent of resection and detection of complications. The use of advanced MRI sequences could potentially provide additional physiological information that may aid in the preservation of healthy brain regions. This review aims to determine the added value of advanced imaging in ioMRI for pediatric brain tumor surgery compared to conventional imaging. Methods: Our systematic literature search identified relevant articles on PubMed using keywords associated with pediatrics, ioMRI, and brain tumors. The literature search was extended using the snowball technique to gather more information on advanced MRI techniques, their technical background, their use in adult ioMRI, and their use in routine pediatric brain tumor care. Results: The available literature was sparse and demonstrated that advanced sequences were used to reconstruct fibers to prevent damage to important structures, provide information on relative cerebral blood flow or abnormal metabolites, or to indicate the onset of hemorrhage or ischemic infarcts. The explorative literature search revealed developments within each advanced MRI field, such as multi-shell diffusion MRI, arterial spin labeling, and amide-proton transfer-weighted imaging, that have been studied in adult ioMRI but have not yet been applied in pediatrics. These techniques could have the potential to provide more accurate fiber tractography, information on intraoperative cerebral perfusion, and to match gadolinium-based T1w images without using a contrast agent. Conclusion: The potential added value of advanced MRI in the intraoperative setting for pediatric brain tumors is to prevent damage to important structures, to provide additional physiological or metabolic information, or to indicate the onset of postoperative changes. Current developments within various advanced ioMRI sequences are promising with regard to providing in-depth tissue information.
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Affiliation(s)
- Pien E. J. Jellema
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, Netherlands
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, Netherlands
- *Correspondence: Pien E. J. Jellema,
| | - Jannie P. Wijnen
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Alberto De Luca
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, Netherlands
- Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Henk J. M. M. Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Iris V. Obdeijn
- Centre for Image Sciences, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Kirsten M. van Baarsen
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, Netherlands
- Department of Neurosurgery, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Maarten H. Lequin
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, Netherlands
- Department of Radiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Eelco W. Hoving
- Department of Pediatric Neuro-Oncology, Princess Máxima Centre for Pediatric Oncology, Utrecht, Netherlands
- Department of Neurosurgery, University Medical Centre Utrecht, Utrecht, Netherlands
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Developing a Predictive Grading Model for Children with Gliomas Based on Diffusion Kurtosis Imaging Metrics: Accuracy and Clinical Correlations with Patient Survival. Cancers (Basel) 2022; 14:cancers14194778. [PMID: 36230701 PMCID: PMC9563289 DOI: 10.3390/cancers14194778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/19/2022] [Accepted: 09/24/2022] [Indexed: 11/20/2022] Open
Abstract
Purpose: To develop a predictive grading model based on diffusion kurtosis imaging (DKI) metrics in children affected by gliomas, and to investigate the clinical impact of the predictive model by correlating with overall survival and progression-free survival. Materials and methods: 59 patients with a histological diagnosis of glioma were retrospectively studied (33 M, 26 F, median age 7.2 years). Patients were studied on a 3T scanner with a standardized MR protocol, including conventional and DKI sequences. Mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and apparent diffusion coefficient (ADC) maps were obtained. Whole tumour volumes (VOIs) were segmented semi-automatically. Mean DKI values were calculated for each metric. The quantitative values from DKI-derived metrics were used to develop a predictive grading model to develop a probability prediction of a high-grade glioma (pHGG). Three models were tested: DTI-based, DKI-based, and combined (DTI and DKI). The grading accuracy of the resulting probabilities was tested with a receiver operating characteristics (ROC) analysis for each model. In order to account for dataset imbalances between pLGG and pHGG, we applied a random synthetic minority oversampling technique (SMOTE) analysis. Lastly, the most accurate model predictions were correlated with progression-free survival (PFS) and overall survival (OS) using the Kaplan−Meier method. Results: The cohort included 46 patients with pLGG and 13 patients with pHGG. The developed model predictions yielded an AUC of 0.859 (95%CI: 0.752−0.966) for the DTI model, of 0.939 (95%CI: 0.879−1) for the DKI model, and of 0.946 (95%CI: 0.890−1) for the combined model, including input from both DTI and DKI metrics, which resulted in the most accurate model. Sample estimation with the random SMOTE analysis yielded an AUC of 0.98 on the testing set. Model predictions from the combined model were significantly correlated with PFS (25.2 months for pHGG vs. 40.0 months for pLGG, p < 0.001) and OS (28.9 months for pHGG vs. 44.9 months for pLGG, p < 0.001). Conclusions: a DKI-based predictive model was highly accurate for pediatric glioma grading. The combined model, derived from both DTI and DKI metrics, proved that DKI-based model predictions of tumour grade were significantly correlated with progression-free survival and overall survival.
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Baba A, Kurokawa R, Kurokawa M, Hassan O, Ota Y, Srinivasan A. ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer: A Systematic Review and Meta-analysis. AJNR Am J Neuroradiol 2022; 43:442-447. [PMID: 35210272 PMCID: PMC8910821 DOI: 10.3174/ajnr.a7431] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/31/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Previous studies reported that the ADC values of recurrent head and neck cancer lesions are lower than those of posttreatment changes, however, the utility of ADC to differentiate them has not been definitively summarized and established. PURPOSE Our aim was to evaluate the diagnostic benefit of ADC calculated from diffusion-weighted imaging in differentiating recurrent lesions from posttreatment changes in head and neck cancer. DATA SOURCES MEDLINE, Scopus, and EMBASE data bases were searched for studies. STUDY SELECTION The review identified 6 prospective studies with a total of 365 patients (402 lesions) who were eligible for the meta-analysis. DATA ANALYSIS Forest plots were used to assess the mean difference in ADC values. Heterogeneity among the studies was evaluated using the Cochrane Q test and the I2 statistic. DATA SYNTHESIS Among included studies, the overall mean of ADC values of recurrent lesions was 1.03 × 10-3mm2/s and that of the posttreatment changes was 1.51 × 10-3mm2/s. The ADC value of recurrence was significantly less than that of posttreatment changes in head and neck cancer (pooled mean difference: -0.45; 95% CI, -0.59-0.32, P < .0001) with heterogeneity among studies. The threshold of ADC values between recurrent lesions and posttreatment changes was suggested to be 1.10 × 10-3mm2/s. LIMITATIONS Given the heterogeneity of the data of the study, the conclusions should be interpreted with caution. CONCLUSIONS The ADC values in recurrent head and neck cancers are lower than those of posttreatment changes, and the threshold of ADC values between them was suggested.
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Affiliation(s)
- A. Baba
- From the Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - R. Kurokawa
- From the Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - M. Kurokawa
- From the Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - O. Hassan
- From the Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Y. Ota
- From the Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Srinivasan
- From the Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
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