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Blachura T, Matusik PS, Kowal A, Radzikowska J, Jarczewski JD, Skiba Ł, Popiela TJ, Chrzan R. Diagnostic accuracy of the Clear Cell Likelihood Score and selected MRI parameters in the characterization of indeterminate renal masses - a single-institution study. Abdom Radiol (NY) 2024:10.1007/s00261-024-04484-5. [PMID: 38980404 DOI: 10.1007/s00261-024-04484-5] [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: 04/19/2024] [Revised: 06/25/2024] [Accepted: 06/30/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE We aimed to assess the diagnostic accuracy of the clear cell likelihood score (ccLS) and value of other selected magnetic resonance imaging (MRI) features in the characterization of indeterminate small renal masses (SRMs). METHODS Fifty patients with indeterminate SRMs discovered on MRI between 2012 and 2023 were included. The ccLS for the characterization of clear cell renal cell carcinoma (ccRCC) was calculated and compared to the final diagnosis (ccRCC vs. 'all other' masses). RESULTS The ccLS = 5 had a satisfactory accuracy of 64.0% and a very high specificity of 96.3%; however, its sensitivity of 26.1% was relatively low. Receiver operating curve (ROC) analysis revealed that from the selected MRI features, only T1 ratio and arterial to delayed enhancement (ADER) were good discriminators between ccRCC and other types of renal masses (area under curve, AUC = 0.707, p = 0.01; AUC = 0.673, p = 0.03; respectively). The cut-off points determined in ROC analysis using the Youden index were 0.73 (p = 0.01) for T1 ratio and 0.99 for ADER (p = 0.03). The logistic regression model demonstrated that ccLS = 5 and T1 ratio (OR = 15.5 [1.1-218.72], p = 0.04; OR = 0.002 [0.00-0.81], p = 0.04) were significant predictors of ccRCC. CONCLUSIONS The ccLS algorithm offers an encouraging method for the standardization of imaging protocols to aid in the diagnosis and management of SRMs in daily clinical practice by enhancing detectability of ccRCC and reducing the number of unnecessary invasive procedures for benign or indolent lesions. However, its diagnostic performance needs multi-center large cohort studies to validate it before it can be incorporated as a diagnostic algorithm and will guide future iterations of clinical guidelines. The retrospective nature of our study and small patient population confined to a single clinical center may impact the generalizability of the results; thus, future studies are required to define whether employment of the T1 ratio or ADER parameter may strengthen the diagnostic accuracy of ccRCC diagnosis.
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Affiliation(s)
- Tomasz Blachura
- Department of Diagnostic Imaging, University Hospital, Kraków, 30-688, Poland
| | - Patrycja S Matusik
- Department of Diagnostic Imaging, University Hospital, Kraków, 30-688, Poland.
- Chair of Radiology, Jagiellonian University Medical College, Kraków, 30-688, Poland.
| | - Aleksander Kowal
- Department of Neurosurgery, Comprehensive Cancer Centre and Traumatology, Copernicus Memorial Hospital in Lodz, Lodz, Poland
| | - Julia Radzikowska
- Student's Scientific Group, Jagiellonian University Medical College, Kraków, 30-688, Poland
| | | | - Łukasz Skiba
- Student's Scientific Group, Jagiellonian University Medical College, Kraków, 30-688, Poland
| | - Tadeusz J Popiela
- Department of Diagnostic Imaging, University Hospital, Kraków, 30-688, Poland
- Chair of Radiology, Jagiellonian University Medical College, Kraków, 30-688, Poland
| | - Robert Chrzan
- Department of Diagnostic Imaging, University Hospital, Kraków, 30-688, Poland
- Chair of Radiology, Jagiellonian University Medical College, Kraków, 30-688, Poland
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Bellin MF, Valente C, Bekdache O, Maxwell F, Balasa C, Savignac A, Meyrignac O. Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches. Cancers (Basel) 2024; 16:1926. [PMID: 38792005 PMCID: PMC11120239 DOI: 10.3390/cancers16101926] [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: 03/21/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.
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Affiliation(s)
- Marie-France Bellin
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
- Faculté de Médecine, University of Paris-Saclay, 63 Rue Gabriel Péri, 94276 Le Kremlin-Bicêtre, France
- BioMaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, France
| | - Catarina Valente
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Omar Bekdache
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Florian Maxwell
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Cristina Balasa
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Alexia Savignac
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
| | - Olivier Meyrignac
- Service de Radiologie Diagnostique et Interventionnelle, Hôpital de Bicêtre AP-HP, 78 Rue du Général Leclerc, 94275 Le Kremlin-Bicêtre, France; (C.V.); (O.B.); (F.M.); (A.S.); (O.M.)
- Faculté de Médecine, University of Paris-Saclay, 63 Rue Gabriel Péri, 94276 Le Kremlin-Bicêtre, France
- BioMaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, France
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Toffoli T, Saut O, Etchegaray C, Jambon E, Le Bras Y, Grenier N, Marcelin C. Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy. J Pers Med 2023; 13:1444. [PMID: 37888055 PMCID: PMC10608459 DOI: 10.3390/jpm13101444] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 10/28/2023] Open
Abstract
PURPOSE The aim of this study was to ascertain whether radiomics data can assist in differentiating small (<4 cm) clear cell renal cell carcinomas (ccRCCs) from small oncocytomas using T2-weighted magnetic resonance imaging (MRI). MATERIAL AND METHODS This retrospective study incorporated 48 tumors, 28 of which were ccRCCs and 20 were oncocytomas. All tumors were less than 4 cm in size and had undergone pre-biopsy or pre-surgery MRI. Following image pre-processing, 102 radiomics features were evaluated. A univariate analysis was performed using the Wilcoxon rank-sum test with Bonferroni correction. We compared multiple radiomics pipelines of normalization, feature selection, and machine learning (ML) algorithms, including random forest (RF), logistic regression (LR), AdaBoost, K-nearest neighbor, and support vector machine, using a supervised ML approach. RESULTS No statistically significant features were identified via the univariate analysis with Bonferroni correction. The most effective algorithm was identified using a pipeline incorporating standard normalization, RF-based feature selection, and LR, which achieved an area under the curve (AUC) of 83%, accuracy of 73%, sensitivity of 79%, and specificity of 65%. Subsequently, the most significant features were identified from this algorithm, and two groups of uncorrelated features were established based on Pearson correlation scores. Using these features, an algorithm was established after a pipeline of standard normalization and LR, achieving an AUC of 90%, an accuracy of 77%, sensitivity of 83%, and specificity of 69% for distinguishing ccRCCs from oncocytomas. CONCLUSIONS Radiomics analysis based on T2-weighted MRI can aid in distinguishing small ccRCCs from small oncocytomas. However, it is not superior to standard multiparameter renal MRI and does not yet allow us to dispense with percutaneous biopsy.
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Affiliation(s)
- Thibault Toffoli
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
| | - Olivier Saut
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, F-33400 Talence, France; (O.S.); (C.E.); (N.G.)
| | - Christele Etchegaray
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, F-33400 Talence, France; (O.S.); (C.E.); (N.G.)
| | - Eva Jambon
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
| | - Yann Le Bras
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
| | - Nicolas Grenier
- University of Bordeaux, IMB, UMR CNRS 5251, INRIA Project Team Monc, F-33400 Talence, France; (O.S.); (C.E.); (N.G.)
| | - Clément Marcelin
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Imaging and Interventional Radiology, Hôpital Pellegrin, 33000 Bordeaux, France; (T.T.); (E.J.); (Y.L.B.)
- Bordeaux Institute of Oncology, BRIC U1312, INSERM, Bordeaux University, 33000 Bordeaux, France
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Garnier C, Ferrer L, Vargas J, Gallinato O, Jambon E, Le Bras Y, Bernhard JC, Colin T, Grenier N, Marcelin C. A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75). Diagnostics (Basel) 2023; 13:2548. [PMID: 37568911 PMCID: PMC10417436 DOI: 10.3390/diagnostics13152548] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/05/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. PURPOSE This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. MATERIALS AND METHODS A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. RESULTS A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. CONCLUSION Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.
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Affiliation(s)
- Cassandre Garnier
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Loïc Ferrer
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Jennifer Vargas
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Olivier Gallinato
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Eva Jambon
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Yann Le Bras
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | | | - Thierry Colin
- SOPHiA GENETICS, Multimodal Research, Cité de la Photonique—Bâtiment GIENAH, 11 Avenue de Canteranne, 33600 Pessac, France; (L.F.); (J.V.); (O.G.); (T.C.)
| | - Nicolas Grenier
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
| | - Clément Marcelin
- Department of Imaging and Interventional Radiology, Hôpital Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux, France
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Chartier S, Arif-Tiwari H. MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach. Cancers (Basel) 2023; 15:2799. [PMID: 37345136 DOI: 10.3390/cancers15102799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 06/23/2023] Open
Abstract
Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.
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Affiliation(s)
- Stephane Chartier
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ 85724, USA
| | - Hina Arif-Tiwari
- Department of Medical Imaging, College of Medicine, The University of Arizona, Tucson, AZ 85724, USA
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Anush A, Rohini G, Nicola S, WalaaEldin EM, Eranga U. Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images. J Med Imaging (Bellingham) 2023; 10:024501. [PMID: 36950139 PMCID: PMC10026851 DOI: 10.1117/1.jmi.10.2.024501] [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: 05/18/2022] [Accepted: 02/22/2023] [Indexed: 03/24/2023] Open
Abstract
Purpose Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI). Approach In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation. Results The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively. Conclusions We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.
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Affiliation(s)
- Agarwal Anush
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| | - Gaikar Rohini
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| | - Schieda Nicola
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | | | - Ukwatta Eranga
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
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Das CJ, Aggarwal A, Singh P, Nayak B, Yadav T, Lal A, Gorsi U, Batra A, Shamim SA, Duara BK, Arulraj K, Kaushal S, Seth A. Imaging Recommendations for Diagnosis, Staging, and Management of Renal Tumors. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1759718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
AbstractRenal cell carcinomas accounts for 2% of all the cancers globally. Most of the renal tumors are detected incidentally. Ultrasound remains the main screening modality to evaluate the renal masses. A multi -phase contrast enhanced computer tomography is must for characterizing the renal lesions. Imaging plays an important role in staging, treatment planning and follow up of renal cancers. In this review , we discuss the imaging guidelines for the management of renal tumors.
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Affiliation(s)
- Chandan J Das
- Department of Radiodiagnosis and Interventional Radiology, AIIMS, New Delhi, India
| | - Ankita Aggarwal
- Department of Radiodiagnosis, VMMC and SJH, New Delhi, India
| | | | - B Nayak
- Department of Urology, AIIMS, New Delhi, India
| | - Taruna Yadav
- Department of Radiodiagnosis, Jodhpur, Rajasthan, India
| | - Anupam Lal
- Department of Radiodiagnosis, PGI, Chandigarh, India
| | - Ujjwal Gorsi
- Department of Radiodiagnosis, PGI, Chandigarh, India
| | - Atul Batra
- Department of Medical Oncology, AIIMS, IRCH, New Delhi, India
| | | | | | | | | | - Amlesh Seth
- Department of Urology, AIIMS, New Delhi, India
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Pietersen PI, Lynggård Bo Madsen J, Asmussen J, Lund L, Nielsen TK, Pedersen M, Engvad B, Graumann O. Multiparametric magnetic resonance imaging for characterizing renal tumors: A validation study of the algorithm presented by Cornelis et al. J Clin Imaging Sci 2023; 13:7. [PMID: 36908585 PMCID: PMC9992978 DOI: 10.25259/jcis_124_2022] [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: 10/15/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
Objectives In the last decade, the incidence of renal cell carcinoma (RCC) has been rising, with the greatest increase observed for solid tumors. Magnetic resonance imaging (MRI) protocols and algorithms have recently been available for classifying RCC subtypes and benign subtypes. The objective of this study was to prospectively validate the MRI algorithm presented by Cornelis et al. for RCC classification. Material and Methods Over a 7-month period, 38 patients with 44 renal tumors were prospectively included in the study and received an MRI examination in addition to the conventional investigation program. The MRI sequences were: T2-weighted, dual chemical shift MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced T1-weighted in wash-in and wash-out phases. The images were evaluated according to the algorithm by two experienced, blinded radiologists, and the histopathological diagnosis served as the gold standard. Results Of 44 tumors in 38 patients, only 8 tumors (18.2%) received the same MRI diagnosis according to the algorithm as the histopathological diagnosis. MRI diagnosed 16 angiomyolipoma, 14 clear cell RCC (ccRCC), 12 chromophobe RCC (chRCC), and two papillary RCC (pRCC), while histopathological examination diagnosed 24 ccRCC, four pRCC, one chRCC, and one mixed tumor of both pRCC and chRCC. Malignant tumors were statistically significantly larger than the benign (3.16 ± 1.34 cm vs. 2.00 ± 1.04 cm, P = 0.006). Conclusion This prospective study could not reproduce Cornelis et al.'s results and does not support differentiating renal masses using multiparametric MRI without percutaneous biopsy in the future. The MRI algorithm showed few promising results to categorize renal tumors, indicating histopathology for clinical decisions and follow-up regimes of renal masses are still required.
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Affiliation(s)
| | - Janni Lynggård Bo Madsen
- Research and Innovation Unit, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jon Asmussen
- Department of Radiology, Odense University Hospital, Odense, Denmark
| | - Lars Lund
- Department of Urology, Odense University Hospital, Odense, Denmark
| | | | - Michael Pedersen
- Department of Clinical Medicine - Comparative Medicine Lab, Aarhus University Hospital, Aarhus, Denmark
| | - Birte Engvad
- Department of Pathology, Odense University Hospital, Odense, Denmark
| | - Ole Graumann
- Department of Radiology, Odense University Hospital, Odense, Denmark
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French AFU Cancer Committee Guidelines - Update 2022-2024: management of kidney cancer. Prog Urol 2022; 32:1195-1274. [DOI: 10.1016/j.purol.2022.07.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
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10
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Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol 2022; 9:243-252. [PMID: 36035341 PMCID: PMC9399557 DOI: 10.1016/j.ajur.2022.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/07/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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Pedrosa I, Cadeddu JA. How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score. Radiology 2021; 302:256-269. [PMID: 34904873 PMCID: PMC8805575 DOI: 10.1148/radiol.210034] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The widespread use of cross-sectional imaging has led to a continuous increase in the number of incidentally detected indeterminate renal masses. Frequently, these clinical scenarios involve an older patient with comorbidities and a small renal mass (≤4 cm). Despite aggressive treatment in early stages of the disease, a clear positive effect in reducing kidney cancer-specific mortality is lacking, indicating that many renal cancers exhibit an indolent oncologic behavior. Furthermore, in general, one in five small renal masses is histologically benign and may not benefit from aggressive treatment. Although active surveillance is increasingly recognized as a management option for some patients, the absence of reliable clinical and imaging predictive biologic markers of aggressiveness can contribute to patient anxiety and limit its use in clinical practice. A standardized approach to the image interpretation of solid renal masses has not been broadly implemented. The clear cell likelihood score (ccLS) derived from multiparametric MRI is useful in noninvasively identifying the clear cell subtype, the most common and aggressive form of kidney cancer. Herein, a review of the ccLS is presented, including a step-by-step guide for image interpretation and additional guidance for its implementation in clinical practice.
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Affiliation(s)
- Ivan Pedrosa
- From the Department of Radiology (I.P., J.A.C.), Department of Urology (I.P., J.A.C.), and Advanced Imaging Research Center (I.P.), University of Texas Southwestern, 5323 Harry Hines Blvd, Clements Imaging Bldg, Ste 2202, MC 9085, Dallas, TX 75390
| | - Jeffrey A. Cadeddu
- From the Department of Radiology (I.P., J.A.C.), Department of Urology (I.P., J.A.C.), and Advanced Imaging Research Center (I.P.), University of Texas Southwestern, 5323 Harry Hines Blvd, Clements Imaging Bldg, Ste 2202, MC 9085, Dallas, TX 75390
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Quantitative 3-tesla multiparametric MRI in differentiation between renal cell carcinoma subtypes. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-020-00405-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
MRI provides several distinct quantitative parameters that may better differentiate renal cell carcinoma (RCC) subtypes. The purpose of the study is to evaluate the diagnostic accuracy of apparent diffusion coefficient (ADC), chemical shift signal intensity index (SII), and contrast enhancement in differentiation between different subtypes of renal cell carcinoma.
Results
There were 63 RCC as regard surgical histopathological analysis: 43 clear cell (ccRCC), 12 papillary (pRCC), and 8 chromophobe (cbRCC). The mean ADC ratio for ccRCC (0.75 ± 0.13) was significantly higher than that of pRCC (0.46 ± 0.12, P < 0.001) and cbRCC (0.41 ± 0.15, P < 0.001). The mean ADC value for ccRCC (1.56 ± 0.27 × 10−3 mm2/s) was significantly higher than that of pRCC (0.96 ± 0.25 × 10−3 mm2/s, P < 0.001) and cbRCC (0.89 ± 0.29 × 10−3 mm2/s, P < 0.001). The mean SII of pRCC (1.49 ± 0.04) was significantly higher than that of ccRCC (0.93 ± 0.01, P < 0.001) and cbRCC (1.01 ± 0.16, P < 0.001). The ccRCC absolute corticomedullary enhancement (196.7 ± 81.6) was significantly greater than that of cbRCC (177.8 ± 77.7, P < 0.001) and pRCC (164.3 ± 84.6, P < 0.001).
Conclusion
Our study demonstrated that multiparametric MRI is able to afford some quantitative features such as ADC ratio, SII, and absolute corticomedullary enhancement which can be used to accurately distinguish different subtypes of renal cell carcinoma.
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Schieda N, Krishna S, Pedrosa I, Kaffenberger SD, Davenport MS, Silverman SG. Active Surveillance of Renal Masses: The Role of Radiology. Radiology 2021; 302:11-24. [PMID: 34812670 DOI: 10.1148/radiol.2021204227] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Active surveillance of renal masses, which includes serial imaging with the possibility of delayed treatment, has emerged as a viable alternative to immediate therapeutic intervention in selected patients. Active surveillance is supported by evidence that many benign masses are resected unnecessarily, and treatment of small cancers has not substantially reduced cancer-specific mortality. These data are a call to radiologists to improve the diagnosis of benign renal masses and differentiate cancers that are biologically aggressive (prompting treatment) from those that are indolent (allowing treatment deferral). Current evidence suggests that active surveillance results in comparable cancer-specific survival with a low risk of developing metastasis. Radiology is central in this. Imaging is used at the outset to estimate the probability of malignancy and degree of aggressiveness in malignant masses and to follow up masses for growth and morphologic change. Percutaneous biopsy is used to provide a more definitive histologic diagnosis and to guide treatment decisions, including whether active surveillance is appropriate. Emerging applications that may improve imaging assessment of renal masses include standardized assessment of cystic and solid masses and radiomic analysis. This article reviews the current and future role of radiology in the care of patients with renal masses undergoing active surveillance.
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Affiliation(s)
- Nicola Schieda
- From the Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Ave, Ottawa, ON, Canada K1H 1H6 (N.S.); Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Canada (S.K.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (I.P.); Departments of Urology (S.D.K., M.S.D.) and Radiology (M.S.D.), Michigan Medicine, University of Michigan, Ann Arbor, Mich; and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (S.G.S.)
| | - Satheesh Krishna
- From the Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Ave, Ottawa, ON, Canada K1H 1H6 (N.S.); Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Canada (S.K.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (I.P.); Departments of Urology (S.D.K., M.S.D.) and Radiology (M.S.D.), Michigan Medicine, University of Michigan, Ann Arbor, Mich; and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (S.G.S.)
| | - Ivan Pedrosa
- From the Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Ave, Ottawa, ON, Canada K1H 1H6 (N.S.); Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Canada (S.K.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (I.P.); Departments of Urology (S.D.K., M.S.D.) and Radiology (M.S.D.), Michigan Medicine, University of Michigan, Ann Arbor, Mich; and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (S.G.S.)
| | - Samuel D Kaffenberger
- From the Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Ave, Ottawa, ON, Canada K1H 1H6 (N.S.); Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Canada (S.K.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (I.P.); Departments of Urology (S.D.K., M.S.D.) and Radiology (M.S.D.), Michigan Medicine, University of Michigan, Ann Arbor, Mich; and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (S.G.S.)
| | - Matthew S Davenport
- From the Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Ave, Ottawa, ON, Canada K1H 1H6 (N.S.); Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Canada (S.K.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (I.P.); Departments of Urology (S.D.K., M.S.D.) and Radiology (M.S.D.), Michigan Medicine, University of Michigan, Ann Arbor, Mich; and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (S.G.S.)
| | - Stuart G Silverman
- From the Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Ave, Ottawa, ON, Canada K1H 1H6 (N.S.); Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Canada (S.K.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (I.P.); Departments of Urology (S.D.K., M.S.D.) and Radiology (M.S.D.), Michigan Medicine, University of Michigan, Ann Arbor, Mich; and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (S.G.S.)
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Tsili AC, Moulopoulos LA, Varakarakis IΜ, Argyropoulou MI. Cross-sectional imaging assessment of renal masses with emphasis on MRI. Acta Radiol 2021; 63:1570-1587. [PMID: 34709096 DOI: 10.1177/02841851211052999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Magnetic resonance imaging (MRI) is a useful complementary imaging tool for the diagnosis and characterization of renal masses, as it provides both morphologic and functional information. A core MRI protocol for renal imaging should include a T1-weighted sequence with in- and opposed-phase images (or, alternatively with DIXON technique), T2-weighted and diffusion-weighted images as well as a dynamic contrast-enhanced sequence with subtraction images, followed by a delayed post-contrast T1-weighted sequence. The main advantages of MRI over computed tomography include increased sensitivity for contrast enhancement, less sensitivity for detection of calcifications, absence of pseudoenhancement, and lack of radiation exposure. MRI may be applied for renal cystic lesion characterization, differentiation of renal cell carcinoma (RCC) from benign solid renal tumors, RCC histologic grading, staging, post-treatment follow-up, and active surveillance of patients with treated or untreated RCC.
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Affiliation(s)
- Athina C Tsili
- Department of Clinical Radiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - Lia-Angela Moulopoulos
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, Athens, Greece
| | - Ioannis Μ Varakarakis
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanoglio Hospital, Athens, Greece
| | - Maria I Argyropoulou
- Department of Clinical Radiology, School of Medicine, University of Ioannina, Ioannina, Greece
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Mussi TC, Martins T, Yamauchi FI, Zanini LAP, Baroni RH. Which criteria can be used to predict benignity in solid renal lesions lower-equal to 2 cm? Abdom Radiol (NY) 2021; 46:4873-4880. [PMID: 34097117 DOI: 10.1007/s00261-021-03158-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/19/2021] [Accepted: 05/27/2021] [Indexed: 01/31/2023]
Abstract
PURPOSE To evaluate magnetic resonance imaging (MRI) criteria of solid renal lesions lower-equal to 2 cm to differentiate benign and malignant tumors, using histopathology as gold standard. METHODS Three radiologists independently evaluated objective and subjective MRI criteria of focal renal lesions. A total of 105 nodules of patients who had MRI and histopathological results in our institution were included. Subjective criteria evaluated were signal on T2-weighted imaging, presence of microscopic and macroscopic fat, hemosiderin, hemorrhage, central scar, segmented inversion enhancement and enhancement type; objective criteria were gender, ADC value, heterogeneity on T2-weighted imaging and proportion of enhancement in late post-contrast phases. Finally, the readers classified the lesions in probably benign or malignant. Interobserver agreement was evaluated by the Gwet method, and the quantitative variables by intraclass correlation coefficients. To adjust the predictive model, the logistic regression model was used considering the benignity variable as outcome. RESULTS A total of 26 nodules (24.5%) were benign and 79 (75.2%) were malignant, with size ranging from 7 to 20 mm (median: 14 mm). The most frequent subtype was papillary renal cell carcinoma (RCC) (35.2%), followed by clear-cell RCC (24.8%) and oncocytoma (12.4%). The univariate and multivariate analysis showed, among all categories evaluated, that microscopic fat (p: 0.072), intermediate (p: 0.004) and hyper-enhancement (p: 0.031) and female sex (p: 0.0047) had the best outcome for benignity, within odds ratios of 4.29, 5.75, 4.07 and 2.86, respectively. CONCLUSION In small solid renal lesions lower-equal to 2 cm, microscopic fat, moderate and hyper-enhancement and female sex were associated with benignity.
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Affiliation(s)
- Thais C Mussi
- Radiology and Diagnostic Imaging Department, Hospital Israelita Albert Einstein, Av. Albert Einstein, 627/701 - Jardim Leonor, São Paulo, SP, 05652-900, Brazil.
| | - Tatiana Martins
- Ecoar Medicina Diagnóstica, Av. do Contorno, 6760 - Lourdes, Belo Horizonte, MG, 30110-110, Brazil
| | - Fernando Ide Yamauchi
- Radiology and Diagnostic Imaging Department, Hospital Israelita Albert Einstein, Av. Albert Einstein, 627/701 - Jardim Leonor, São Paulo, SP, 05652-900, Brazil
| | - Lilian A P Zanini
- Radiology and Diagnostic Imaging Department, Hospital Israelita Albert Einstein, Av. Albert Einstein, 627/701 - Jardim Leonor, São Paulo, SP, 05652-900, Brazil
| | - Ronaldo H Baroni
- Radiology and Diagnostic Imaging Department, Hospital Israelita Albert Einstein, Av. Albert Einstein, 627/701 - Jardim Leonor, São Paulo, SP, 05652-900, Brazil
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Tsili AC, Andriotis E, Gkeli MG, Krokidis M, Stasinopoulou M, Varkarakis IM, Moulopoulos LA. The role of imaging in the management of renal masses. Eur J Radiol 2021; 141:109777. [PMID: 34020173 DOI: 10.1016/j.ejrad.2021.109777] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/09/2021] [Accepted: 05/14/2021] [Indexed: 12/26/2022]
Abstract
The wide availability of cross-sectional imaging is responsible for the increased detection of small, usually asymptomatic renal masses. More than 50 % of renal cell carcinomas (RCCs) represent incidental findings on noninvasive imaging. Multimodality imaging, including conventional US, contrast-enhanced US (CEUS), CT and multiparametric MRI (mpMRI) is pivotal in diagnosing and characterizing a renal mass, but also provides information regarding its prognosis, therapeutic management, and follow-up. In this review, imaging data for renal masses that urologists need for accurate treatment planning will be discussed. The role of US, CEUS, CT and mpMRI in the detection and characterization of renal masses, RCC staging and follow-up of surgically treated or untreated localized RCC will be presented. The role of percutaneous image-guided ablation in the management of RCC will be also reviewed.
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Affiliation(s)
- Athina C Tsili
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece.
| | - Efthimios Andriotis
- Department of Newer Imaging Methods of Tomography, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Myrsini G Gkeli
- 1st Department of Radiology, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Miltiadis Krokidis
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, 11528, Athens, Greece; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
| | - Myrsini Stasinopoulou
- Department of Newer Imaging Methods of Tomography, General Anti-Cancer Hospital Agios Savvas, 11522, Athens, Greece.
| | - Ioannis M Varkarakis
- 2nd Department of Urology, National and Kapodistrian University of Athens, Sismanoglio Hospital, 15126, Athens, Greece.
| | - Lia-Angela Moulopoulos
- 1st Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, 11528, Athens, Greece.
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Bensalah K, Bigot P, Albiges L, Bernhard J, Bodin T, Boissier R, Correas J, Gimel P, Hetet J, Long J, Nouhaud F, Ouzaïd I, Rioux-Leclercq N, Méjean A. Recommandations françaises du Comité de cancérologie de l’AFU – actualisation 2020–2022 : prise en charge du cancer du rein. Prog Urol 2020; 30:S2-S51. [DOI: 10.1016/s1166-7087(20)30749-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Lopes Vendrami C, McCarthy RJ, Villavicencio CP, Miller FH. Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics. Abdom Radiol (NY) 2020; 45:2797-2809. [PMID: 32666233 DOI: 10.1007/s00261-020-02637-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/23/2020] [Accepted: 07/04/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Solid renal masses (SRM) are difficult to differentiate based on standard MR features. The purpose of this study was to assess MR imaging features of SRM to evaluate performance of ensemble methods of classifying SRM subtypes. MATERIALS AND METHODS MR images of SRM (n = 330) were retrospectively evaluated for standard and multiparametric (mp) features. Models of MR features for predicting malignant and benign lesions as well as subtyping SRM were developed using a training dataset and performance was evaluated in a test data-set using recursive partitioning (RP), gradient booting machine (GBM), and random forest (RF) methods. RESULTS In the test dataset, GBM and RF models demonstrated an accuracy of 86% (95% CI 75% to 93%) for predicting benign versus malignant SRM compared to 83% (95% CI 71% to 91%) for the RP model. RF had the greatest accuracy in predicting SRM subtypes, 81.2% (95% CI 69.5% to 89.9%) compared with GBM 73.4% (95% CI 60.9% to 83.7%) or RP 70.3% (95% CI 57.6% to 81.1%). Marginal homogeneity was reduced by the RF model compared with the RP model (P < 0.001), but not the GBM model (P = 0.135). All models had high sensitivity and specificity for clear cell and papillary renal cell carcinomas (RCC), but performed less well in differentiating chromophobe RCC, oncocytomas, and fat-poor angiomyolipomas. CONCLUSION Ensemble methods for prediction of SRM from radiologist-assessed image characteristics have high accuracy for distinguishing benign and malignant lesions. SRM subtype classification is limited by the ability to categorize chromophobe RCCs, oncocytomas, and fat-poor angiomyolipomas.
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Affiliation(s)
- Camila Lopes Vendrami
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St. Suite 800, Chicago, IL, 60611, USA
| | - Robert J McCarthy
- Department of Anesthesiology, Rush University, Chicago, IL, 60612, USA
| | - Carolina Parada Villavicencio
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St. Suite 800, Chicago, IL, 60611, USA
| | - Frank H Miller
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N. St. Clair St. Suite 800, Chicago, IL, 60611, USA.
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Zaharie A, Bolboacă SD, Moisoiu T, Burghelea D, Iacob G, Ghervan L, Elec FI. Renal Tumors in Young Adults: Is Preoperative Computer Tomography Imaging Suggestive for the Nature of the Tumors? Diagnostics (Basel) 2020; 10:diagnostics10060380. [PMID: 32517346 PMCID: PMC7345588 DOI: 10.3390/diagnostics10060380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/25/2020] [Accepted: 06/04/2020] [Indexed: 01/20/2023] Open
Abstract
Renal cell carcinoma (RCC) accounts for 2–3% of all adult malignant neoplasms and is even rarer in patients under 45 years old. Clear-cell carcinoma represents most of the pathological subtypes. Our study aimed to investigate the association between preoperative computer tomography imagistic evaluation and histopathological diagnosis of renal tumors in young adults. Patients younger than 45 years old with renal tumors who were referred for medical treatment at the Clinical Institute of Urology and Renal Transplantation Cluj-Napoca from 2012 to 2019 were considered eligible for the study. Medical charts were retrospectively reviewed, and patients with complete data regarding preoperative diagnostic, histopathological evaluation, and follow-up data, regardless of gender, were included in the study. Sixteen patients younger than 45 years fulfilled all the inclusion criteria and were evaluated. With two exceptions, the evaluated patients were in a T1 and T2 stage, with no vascular invasion or of the adjacent organs. Two-thirds of our patients had a clear-cell renal cell carcinoma. None of our patients fitted in the low complexity surgery category of the R.E.N.A.L. Nephrometry Score and 37.5% of them benefited from partial nephrectomy. Half of the suppositions made based on imaging were concordant with the histopathology report. Fifteen of the patients showed no recurrence during the respective follow-up interval. Computer tomography imaging reports showed on our sample a higher concordance with the histopathological report in the more common subtypes (namely Renal Clear Cell RCC), with typical appearances.
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Affiliation(s)
- Andreea Zaharie
- Department of Medical Imaging, “Leon Daniello” Pulmonology Hospital, Cluj-Napoca, Bogdan Petriceicu Hașdeu Street, no. 6, 400332 Cluj-Napoca, Romania
- Correspondence: (A.Z.); (S.D.B.); (F.I.E.); Tel.: +40-762-298-833 (A.Z.); +40-374-834-506 (S.D.B.); +40-722-822-740 (F.I.E.)
| | - Sorana D. Bolboacă
- Department of Medical Informatics and Biostatistics, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, Louis Pasteur Str., no. 6, 400349 Cluj-Napoca, Romania
- Correspondence: (A.Z.); (S.D.B.); (F.I.E.); Tel.: +40-762-298-833 (A.Z.); +40-374-834-506 (S.D.B.); +40-722-822-740 (F.I.E.)
| | - Tudor Moisoiu
- Department of Urology, Clinical Institute of Urology and Renal Transplantation, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania; (T.M.); (D.B.); (L.G.)
| | - Dan Burghelea
- Department of Urology, Clinical Institute of Urology and Renal Transplantation, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania; (T.M.); (D.B.); (L.G.)
| | - Gheorghita Iacob
- Department of Pathology, Clinical Institute of Urology and Renal Transplantation, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania;
| | - Liviu Ghervan
- Department of Urology, Clinical Institute of Urology and Renal Transplantation, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania; (T.M.); (D.B.); (L.G.)
- Department of Urology, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania
| | - Florin Ioan Elec
- Department of Urology, Clinical Institute of Urology and Renal Transplantation, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania; (T.M.); (D.B.); (L.G.)
- Department of Urology, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, Clinicilor Str., no. 4–6, 400006 Cluj-Napoca, Romania
- Correspondence: (A.Z.); (S.D.B.); (F.I.E.); Tel.: +40-762-298-833 (A.Z.); +40-374-834-506 (S.D.B.); +40-722-822-740 (F.I.E.)
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Rouvière O, Cornelis F, Brunelle S, Roy C, André M, Bellin MF, Boulay I, Eiss D, Girouin N, Grenier N, Hélénon O, Lapray JF, Lefèvre A, Matillon X, Ménager JM, Millet I, Ronze S, Sanzalone T, Tourniaire J, Rocher L, Renard-Penna R. Imaging protocols for renal multiparametric MRI and MR urography: results of a consensus conference from the French Society of Genitourinary Imaging. Eur Radiol 2020; 30:2103-2114. [PMID: 31900706 DOI: 10.1007/s00330-019-06530-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 09/19/2019] [Accepted: 10/18/2019] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To develop technical guidelines for magnetic resonance imaging aimed at characterising renal masses (multiparametric magnetic resonance imaging, mpMRI) and at imaging the bladder and upper urinary tract (magnetic resonance urography, MRU). METHODS The French Society of Genitourinary Imaging organised a Delphi consensus conference with a two-round Delphi survey followed by a face-to-face meeting. Two separate questionnaires were issued for renal mpMRI and for MRU. Consensus was strictly defined using a priori criteria. RESULTS Forty-two expert uroradiologists completed both survey rounds with no attrition between the rounds. Fifty-six of 84 (67%) statements of the mpMRI questionnaire and 44/71 (62%) statements of the MRU questionnaire reached final consensus. For mpMRI, there was consensus that no injection of furosemide was needed and that the imaging protocol should include T2-weighted imaging, dual chemical shift imaging, diffusion-weighted imaging (use of multiple b-values; maximal b-value, 1000 s/mm2) and fat-saturated single-bolus multiphase (unenhanced, corticomedullary, nephrographic) contrast-enhanced imaging; late imaging (more than 10 min after injection) was judged optional. For MRU, the patients should void their bladder before the examination. The protocol must include T2-weighted imaging, anatomical fast T1/T2-weighted imaging, diffusion-weighted imaging (use of multiple b-values; maximal b-value, 1000 s/mm2) and fat-saturated single-bolus multiphase (unenhanced, corticomedullary, nephrographic, excretory) contrast-enhanced imaging. An intravenous injection of furosemide is mandatory before the injection of contrast medium. Heavily T2-weighted cholangiopancreatography-like imaging was judged optional. CONCLUSION This expert-based consensus conference provides recommendations to standardise magnetic resonance imaging of kidneys, ureter and bladder. KEY POINTS • Multiparametric magnetic resonance imaging (mpMRI) aims at characterising renal masses; magnetic resonance urography (MRU) aims at imaging the urinary bladder and the collecting systems. • For mpMRI, no injection of furosemide is needed. • For MRU, an intravenous injection of furosemide is mandatory before the injection of contrast medium; heavily T2-weighted cholangiopancreatography-like imaging is optional.
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Affiliation(s)
- Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, 5, place d'Arsonval, 69347, Lyon, France.
- Faculté de médecine Lyon Est, Université de Lyon, Université Lyon 1, Lyon, France.
| | - François Cornelis
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Serge Brunelle
- Department of Radiology, Institut Paoli-Calmettes, Marseille, France
| | - Catherine Roy
- Department of Radiology B, CHU de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Marc André
- Department of Radiology, Hôpital Européen, Marseille, France
| | - Marie-France Bellin
- Department of Diagnostic and Interventional Radiology, Groupe Hospitalier Paris Sud, Assistance Publique-Hôpitaux de Paris, Le Kremlin Bicêtre, France
- Université Paris Sud, Le Kremlin Bicêtre, France
- IR4M, UMR 8081, Service hospitalier Joliot Curie, Orsay, France
| | - Isabelle Boulay
- Department of Radiology, Fondation Hôpital Saint Joseph, Paris, France
| | - David Eiss
- Department of Adult Radiology, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | | | - Nicolas Grenier
- Department of Diagnostic and Interventional Adult Imaging, CHU de Bordeaux, Bordeaux, France
- Université de Bordeaux, Bordeaux, France
| | - Olivier Hélénon
- Department of Adult Radiology, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | | | - Arnaud Lefèvre
- Centre d'Imagerie Médicale Tourville, Paris, France
- Department of Radiology, American Hospital of Paris, Neuilly, France
| | - Xavier Matillon
- Faculté de médecine Lyon Est, Université de Lyon, Université Lyon 1, Lyon, France
- Department of Urology and Transplantation, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
- CarMeN Laboratory, INSERM U1060, Lyon, France
| | | | - Ingrid Millet
- Department of Radiology, Hôpital Lapeyronie, Montpellier, France
- Université de Montpellier, Montpellier, France
| | - Sébastien Ronze
- Imagerie médicale Val d'Ouest Charcot (IMVOC), Ecully, France
| | - Thomas Sanzalone
- Department of Radiology, Centre Hospitalier de Valence, Valence, France
| | - Jean Tourniaire
- Department of Radiology, Clinique Rhône Durance, Avignon, France
| | - Laurence Rocher
- Department of Diagnostic and Interventional Radiology, Groupe Hospitalier Paris Sud, Assistance Publique-Hôpitaux de Paris, Le Kremlin Bicêtre, France
- Université Paris Sud, Le Kremlin Bicêtre, France
- IR4M, UMR 8081, Service hospitalier Joliot Curie, Orsay, France
| | - Raphaële Renard-Penna
- Academic Department of Radiology, Hôpital Pitié-Salpêtrière and Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, Paris, France
- Sorbonne Universités, GRC no 5, ONCOTYPE-URO, Paris, France
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21
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Cornelis FH, Bernhard JC. Diagnostic and interventional radiology is a milestone in the management of renal tumors in Birt-Hugg-Dubé syndrome. Diagn Interv Imaging 2019; 100:657-658. [PMID: 31285159 DOI: 10.1016/j.diii.2019.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- F H Cornelis
- Department of Radiology, Sorbonne Université, Tenon Hospital, 4, rue de la Chine, 75020 Paris, France.
| | - J-C Bernhard
- Department of Urology, Bordeaux University, Pellegrin Hospital, place Amélie-Raba-Léon, 33076 Bordeaux, France
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22
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Update on Gadolinium-Based Contrast Agent-Enhanced Imaging in the Genitourinary System. AJR Am J Roentgenol 2019; 212:1223-1233. [PMID: 30973785 DOI: 10.2214/ajr.19.21137] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE. The purpose of this article is to review gadolinium-based contrast agent (GBCA)-enhanced MRI applications in the genitourinary system. CONCLUSION. Nephrogenic systemic fibrosis is rare or nonexistent with standard dosing of group II GBCAs. Gadolinium retention, cost, and examination times are emerging considerations affecting GBCA use. GBCA is unnecessary to diagnose adrenal adenomas, simple cysts, and some Bosniak category II cysts; however, it is required to determine solid or septal renal mass enhancement. Biparametric prostate MRI requires high-quality and reproducible DWI; therefore, dynamic contrast-enhanced MRI remains valuable in selected prostate MRI examinations.
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Abstract
OBJECTIVE. Renal masses comprise a heterogeneous group of pathologic conditions, including benign and indolent diseases and aggressive malignancies, complicating management. In this article, we explore the emerging role of imaging to provide a comprehensive noninvasive characterization of a renal mass-so-called "virtual biopsy"-and its potential use in the management of patients with renal tumors. CONCLUSION. Percutaneous renal mass biopsy (RMB) remains a valuable method to provide a presurgical histopathologic diagnosis of renal masses, but it is an invasive procedure and is not always feasible. Accumulating data support the use of imaging features to predict histopathology of renal masses. Imaging may help address some of the inherent limitations of RMB, and in certain settings, a multimodal clinical approach may allow decreasing the need for RMB.
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Schieda N, Davenport MS, Pedrosa I, Shinagare A, Chandarana H, Curci N, Doshi A, Israel G, Remer E, Wang J, Silverman SG. Renal and adrenal masses containing fat at MRI: Proposed nomenclature by the society of abdominal radiology disease-focused panel on renal cell carcinoma. J Magn Reson Imaging 2019; 49:917-926. [PMID: 30693607 DOI: 10.1002/jmri.26542] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 09/16/2018] [Accepted: 09/17/2018] [Indexed: 12/15/2022] Open
Abstract
This article proposes a consensus nomenclature for fat-containing renal and adrenal masses at MRI to reduce variability, improve understanding, and enhance communication when describing imaging findings. The MRI appearance of "macroscopic fat" occurs due to a sufficient number of aggregated adipocytes and results in one or more of: 1) intratumoral signal intensity (SI) loss using fat-suppression techniques, or 2) chemical shift artifact of the second kind causing linear or curvilinear India-ink (etching) artifact within or at the periphery of a mass at macroscopic fat-water interfaces. "Macroscopic fat" is most commonly observed in adrenal myelolipoma and renal angiomyolipoma (AML) and only rarely encountered in other adrenal cortical tumors and renal cell carcinomas (RCC). Nonlinear noncurvilinear signal intensity loss on opposed-phase (OP) compared with in-phase (IP) chemical shift MRI (CSI) may be referred to as "microscopic fat" and is due to: a) an insufficient amount of adipocytes, or b) the presence of fat within tumor cells. Determining whether the signal intensity loss observed on CSI is due to insufficient adipocytes or fat within tumor cells cannot be accomplished using CSI alone; however, it can be inferred when other imaging features strongly suggest a particular diagnosis. Fat-poor AML are homogeneously hypointense on T2 -weighted (T2 W) imaging and avidly enhancing; signal intensity loss at OP CSI is uncommon, but when present is usually focal and is caused by an insufficient number of adipocytes within adjacent voxels. Conversely, clear-cell RCC are heterogeneously hyperintense on T2 W imaging and avidly enhancing, with the signal intensity loss observed on OP CSI being typically diffuse and due to fat within tumor cells. Adrenal adenomas, adrenal cortical carcinoma, and adrenal metastases from fat-containing primary malignancies also show signal intensity loss on OP CSI due to fat within tumor cells and not from intratumoral adipocytes. Level of Evidence: 5 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;49:917-926.
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Affiliation(s)
- Nicola Schieda
- Department of Medical Imaging, From the University of Ottawa, Ottawa Hospital, Ottawa, Ontario, Canada
| | | | - Ivan Pedrosa
- Department of Radiology, UT Southwestern, Dallas, Texas, USA
| | - Atul Shinagare
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Hersch Chandarana
- Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Nicole Curci
- Department of Radiology, Michigan University, Ann Arbor, Michigan, USA
| | - Ankur Doshi
- Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Gary Israel
- Department of Radiology, Yale University, New Haven, Connecticut, USA
| | - Erick Remer
- Department Radiology and Diagnostic Imaging, Cleveland Clinic, Cleveland, Ohio, USA
| | - Jane Wang
- Department of Radiology, UCSF, San Francisco, California, USA
| | - Stuart G Silverman
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
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25
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Evaluation of a free-breathing respiratory-triggered (Navigator) 3-D T1-weighted (T1W) gradient recalled echo sequence (LAVA) for detection of enhancement in cystic and solid renal masses. Eur Radiol 2018; 29:2507-2517. [PMID: 30506224 DOI: 10.1007/s00330-018-5839-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 09/15/2018] [Accepted: 10/17/2018] [Indexed: 01/14/2023]
Abstract
OBJECTIVES To evaluate free-breathing Navigator-triggered 3-D T1-weighted MRI (NAV-LAVA) compared to breath-hold (BH)-LAVA among cystic and solid renal masses. MATERIALS AND METHODS With an IRB waiver, 44 patients with 105 renal masses (71 non-enhancing cysts and 14 cystic and 20 solid renal masses) underwent MRI between 2016 and 2017 where BH-LAVA and NAV-LAVA were performed. Subtraction images were generated for BH-LAVA and NAV-LAVA using pre- and 3-min post-gadolinium-enhanced images and were evaluated by two blinded radiologists for overall image quality, image sharpness, motion artifact, and quality of subtraction (using 5-point Likert scales) and presence/absence of enhancement. Percentage signal intensity change (Δ%SI) = ([SI.post-gadolinium-SI.pre-gadolinium]/SI.pre-gadolinium)*100, was measured on BH-LAVA and NAV-LAVA. Likert scores were compared using Wilcoxon's sign-rank test and accuracy for detection of enhancement compared using receiver operator characteristic (ROC) analysis. RESULTS Overall image quality (p = 0.002-0.141), image sharpness (p = 0.002-0.031), and motion artifact were better (p = 0.002) comparing BH-LAVA to NAV-LAVA for both radiologists; however, quality of image subtraction did not differ between groups (p = 0.09-0.14). Sensitivity/specificity/area under ROC curve for enhancement in cystic and solid renal masses using subtraction and %SIΔ were (1) BH-LAVA: 64.7%/98.6%/0.82 (radiologist 1), 61.8%/95.8%/0.79 (radiologist 2), and 70.6%/81.7%/0.76 (%SIΔ) versus 2) NAV-LAVA: 58.8%/95.8%/0.79 (radiologist 1, p = 0.16), 58.8%/88.7%/0.73 (radiologist 2, p = 0.37), and 73.5%/76.1%/0.75 (%SIΔ, p = 0.74). CONCLUSIONS NAV-LAVA showed similar quality of subtraction and ability to detect enhancement compared to BH-LAVA in renal masses albeit with lower image quality, image sharpness, and increased motion artifact. NAV-LAVA may be considered in renal MRI for patients where BH is suboptimal. KEY POINTS • Free-breathing Navigator (NAV) 3-D subtraction MRI is comparable to breath-hold (BH) images. • Accuracy for subjective and quantitative diagnosis of enhancement in renal masses on NAV 3-D T1W is comparable to BH MRI. • NAV 3-D T1W renal MRI is useful in patients who may not be able to adequately BH.
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Abstract
The increase in serendipitous detection of solid renal masses on imaging has not resulted in a reduction in mortality from renal cell carcinoma. Consequently, efforts for improved lesion characterization have been pursued and incorporated into management algorithms for distinguishing clinically significant tumors from those with favorable histology or benign conditions. Although diagnostic imaging strategies have evolved for optimized lesion detection, distinction between benign tumors and both indolent and aggressive malignant neoplasms remain an important diagnostic challenge. Recent advances in cross-sectional imaging have expanded the role of these tests in the noninvasive characterization of solid renal tumors.
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Affiliation(s)
- Fernando U Kay
- Department of Radiology; UT Southwestern Medical Center, 2201 Inwood Road, Suite 210, Dallas, TX 75390, USA
| | - Ivan Pedrosa
- Department of Radiology; UT Southwestern Medical Center, 2201 Inwood Road, Suite 210, Dallas, TX 75390, USA.
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Schieda N, Lim RS, McInnes MDF, Thomassin I, Renard-Penna R, Tavolaro S, Cornelis FH. Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development. Diagn Interv Imaging 2018; 99:443-455. [PMID: 29606371 DOI: 10.1016/j.diii.2018.03.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/07/2018] [Indexed: 12/15/2022]
Abstract
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.
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Affiliation(s)
- N Schieda
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, Ottawa, ON, Canada
| | - R S Lim
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, Ottawa, ON, Canada
| | - M D F McInnes
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, Ottawa, ON, Canada
| | - I Thomassin
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France
| | - R Renard-Penna
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France
| | - S Tavolaro
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France
| | - F H Cornelis
- Sorbonne Université, Institut des Sciences du Calcul et des Données, Department of Radiology, Tenon Hospital - HUEP - APHP, 4 rue de la Chine, 75020 Paris, France.
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