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Gao B, Gorgen ARH, Bhatt R, Tano ZE, Morgan KL, Vo K, Zarandi SS, Ali SN, Jiang P, Patel RM, Clayman RV, Landman J. Avoiding "Needless" nephrectomy: What is the role of small renal mass biopsy in 2024? Urol Oncol 2024; 42:236-244. [PMID: 38643022 DOI: 10.1016/j.urolonc.2024.04.002] [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: 10/24/2023] [Revised: 02/14/2024] [Accepted: 04/01/2024] [Indexed: 04/22/2024]
Abstract
Current guidelines do not mandate routine preoperative renal mass biopsy (RMB) for small renal masses (SRMs), which results in a considerable rate (18%-26%) of needless nephrectomy/partial nephrectomy for benign renal tumors. In light of this ongoing practice, a narrative review was conducted to examine the role of routine RMB for SRM. First, arguments justifying the current non-biopsy approach to SRM are critically reviewed and contested. Second, as a standalone procedure, RMB is critically assessed; RMB was found to have higher sensitivity, specificity, and an equal or lower complication rate when compared with other commonly preoperatively biopsied solid organ tumors (e.g., breast, prostate, lung, pancreas, thyroid, and liver). Based on the foregoing information, we propose a paradigm shift in SRM management, advocating for an updated policy in which partial nephrectomy or nephrectomy for SRM invariably occurs only after a preoperative biopsy confirms that a SRM is indeed malignant.
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Affiliation(s)
- Bruce Gao
- Department of Urology, University of California, Irvine, Orange, CA.
| | | | - Rohit Bhatt
- Department of Urology, University of California, Irvine, Orange, CA
| | - Zachary E Tano
- Department of Urology, University of California, Irvine, Orange, CA
| | - Kalon L Morgan
- Department of Urology, University of California, Irvine, Orange, CA
| | - Kelvin Vo
- Department of Urology, University of California, Irvine, Orange, CA
| | | | - Sohrab N Ali
- Department of Urology, University of California, Irvine, Orange, CA
| | - Pengbo Jiang
- Department of Urology, University of California, Irvine, Orange, CA
| | - Roshan M Patel
- Department of Urology, University of California, Irvine, Orange, CA
| | - Ralph V Clayman
- Department of Urology, University of California, Irvine, Orange, CA
| | - Jaime Landman
- Department of Urology, University of California, Irvine, Orange, CA
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Dev R, Chauhan U, Nandolia KK. Multifaceted Imaging of Renal Lesions With an Emphasis on Cross-Sectional Imaging. Cureus 2024; 16:e59956. [PMID: 38854236 PMCID: PMC11161908 DOI: 10.7759/cureus.59956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction Renal lesions are common findings encountered in cross-sectional imaging. Ultrasonography (USG), computed tomography (CT), and magnetic resonance imaging (MRI) are available modalities for evaluating renal lesions. The Bosniak classification system aids in classifying a renal lesion into a particular category based on various imaging characteristics on contrast-enhanced CT (CECT). Materials and methods The CT report archives were searched for the keyword 'Bosniak' lesions, and six illustrative cases were selected to be included in the review. Results Six cases under Bosniak categories I to IV were included in the review. Operative follow-ups were added in cases where patients underwent surgery. Discussion We have reviewed the imaging features of various renal lesions with cross-sectional modalities, namely CT and MRI, with special emphasis on the Bosniak classification system, including its amendments. Conclusion The Bosniak system is widely used to classify and characterize renal lesions. The authors have presented a scoping review of the features of renal lesions and the Bosniak system.
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Affiliation(s)
- Rahul Dev
- Diagnostic and Intervention Radiology, All India Institute of Medical Sciences, Rishikesh, IND
| | - Udit Chauhan
- Diagnostic and Intervention Radiology, All India Institute of Medical Sciences, Rishikesh, IND
| | - Khanak K Nandolia
- Diagnostic and Intervention Radiology, All India Institute of Medical Sciences, Rishikesh, IND
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Faiella E, Vergantino E, Vaccarino F, Bruno A, Perillo G, Grasso RF, Zobel BB, Santucci D. A Review of the Paradigmatic Role of Adipose Tissue in Renal Cancer: Fat Measurement and Tumor Behavior Features. Cancers (Basel) 2024; 16:1697. [PMID: 38730649 PMCID: PMC11083503 DOI: 10.3390/cancers16091697] [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: 03/28/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
(1) Background: Renal-cell carcinoma (RCC) incidence has been steadily rising, with obesity identified as a potential risk factor. However, the relationship between obesity and RCC prognosis remains unclear. This systematic review aims to investigate the impact of different adipose tissue measurements on RCC behavior and prognosis. (2) Methods: A search of MEDLINE databases identified 20 eligible studies focusing on various fat measurements, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PRAT), and the Mayo adhesive probability (MAP) score. (3) Results: The review revealed conflicting findings regarding the association between adipose tissue measurements and RCC outcomes. While some studies suggested a protective role of certain fat deposits, particularly VAT, against disease progression and mortality, others reported contradictory results across different adipose metrics and RCC subtypes. (4) Conclusions: Methodological variations and limitations, such as retrospective designs and sample size constraints, pose challenges to standardization and generalizability. Further research is needed to understand these associations better and establish standardized approaches for adiposity assessment in RCC patients, which could inform clinical practice and therapeutic decision-making.
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Affiliation(s)
- Eliodoro Faiella
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Elva Vergantino
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Federica Vaccarino
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Amalia Bruno
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Gloria Perillo
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Rosario Francesco Grasso
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Bruno Beomonte Zobel
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Domiziana Santucci
- Operative Reasearch Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, 00128 Rome, Italy; (E.F.); (F.V.); (A.B.); (G.P.); (R.F.G.); (B.B.Z.); (D.S.)
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
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Anari PY, Lay N, Zahergivar A, Firouzabadi FD, Chaurasia A, Golagha M, Singh S, Homayounieh F, Obiezu F, Harmon S, Turkbey E, Merino M, Jones EC, Ball MW, Linehan WM, Turkbey B, Malayeri AA. Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results. Abdom Radiol (NY) 2024; 49:1194-1201. [PMID: 38368481 DOI: 10.1007/s00261-023-04172-w] [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: 09/29/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 02/19/2024]
Abstract
INTRODUCTION Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.
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Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Aryan Zahergivar
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Fatemeh Dehghani Firouzabadi
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Aditi Chaurasia
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - Mahshid Golagha
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Shiva Singh
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | | | - Fiona Obiezu
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Maria Merino
- Pathology Department, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Mark W Ball
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - W Marston Linehan
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA.
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Shang W, Hong G, Li W. MRI for the detection of small malignant renal masses: a systematic review and meta-analysis. Front Oncol 2023; 13:1194128. [PMID: 37876965 PMCID: PMC10591109 DOI: 10.3389/fonc.2023.1194128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 09/12/2023] [Indexed: 10/26/2023] Open
Abstract
Objective We aimed to review the available evidence on the diagnostic performance of magnetic resonance imaging in differentiating malignant from benign small renal masses. Methods An electronic literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar was performed to identify relevant articles up to 31 January 2023. We included studies that reported the diagnostic accuracy of using magnetic resonance imaging to differentiate small (≤4 cm) malignant from benign renal masses. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the bivariate model and the hierarchical summary receiver operating characteristic model. The study quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results A total of 10 studies with 860 small renal masses (815 patients) were included in the current meta-analysis. The pooled sensitivity and specificity of the studies for the detection of malignant masses were 0.85 (95% CI 0.79-0.90) and 0.83 (95% CI 0.67-0.92), respectively. Conclusions MRI had a moderate diagnostic performance in differentiating small malignant renal masses from benign ones. Substantial heterogeneity was observed between studies for both sensitivity and specificity.
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Affiliation(s)
| | | | - Wei Li
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
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Maddalo M, Bertolotti L, Mazzilli A, Flore AGM, Perotta R, Pagnini F, Ziglioli F, Maestroni U, Martini C, Caruso D, Ghetti C, De Filippo M. Small Renal Masses: Developing a Robust Radiomic Signature. Cancers (Basel) 2023; 15:4565. [PMID: 37760532 PMCID: PMC10527518 DOI: 10.3390/cancers15184565] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38-87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.
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Affiliation(s)
- Michele Maddalo
- Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy; (M.M.); (A.M.); (C.G.)
| | - Lorenzo Bertolotti
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
| | - Aldo Mazzilli
- Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy; (M.M.); (A.M.); (C.G.)
| | | | - Rocco Perotta
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
| | - Francesco Pagnini
- Diagnostic Department, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy;
| | - Francesco Ziglioli
- Department of Urology, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy; (F.Z.); (U.M.)
| | - Umberto Maestroni
- Department of Urology, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy; (F.Z.); (U.M.)
| | - Chiara Martini
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
- Diagnostic Department, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy;
| | - Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza-University of Rome, 00100 Rome, Italy
| | - Caterina Ghetti
- Medical Physics Unit, University Hospital of Parma, 43126 Parma, Italy; (M.M.); (A.M.); (C.G.)
| | - Massimo De Filippo
- Department of Medicine and Surgery, Section of Radiology, University of Parma, Via Gramsci 14, 43126 Parma, Italy; (L.B.); (R.P.); (C.M.)
- Diagnostic Department, Parma University Hospital, Via Gramsci 14, 43126 Parma, Italy;
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7
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Aydoğan C, Cansu A, Aydoğan Z, Erdemi S, Teymur A, Bektaş O, Mungan S, Kazaz İO. Diagnostic performance of multiparametric magnetic resonance imaging in the differentiation of clear cell renal cell cancer. Abdom Radiol (NY) 2023; 48:2349-2360. [PMID: 37071122 DOI: 10.1007/s00261-023-03882-5] [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: 12/03/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 04/19/2023]
Abstract
PURPOSE This study aimed to evaluate the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) in the differentiation of renal cell carcinoma (RCC) subtypes. METHODS This is a retrospective diagnostic performance study, in which the diagnostic performances of mpMRI features were evaluated to differentiate clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC). Adult patients who were evaluated using a 3-Tesla dynamic contrast-enhanced mpMRI before undergoing partial or radical nephrectomy for possible malignant renal tumors were included in the study. Signal intensity change percentages (SICP) between contrast-enhanced phases and pre-administration period for both the tumor and normal renal cortex, and tumor-to-cortex enhancement index (TCEI); tumor apparent diffusion coefficient (ADC) values; tumor-to-cortex ADC ratio; and a scale which was developed according to the tumor signal intensities on the axial fat-suppressed T2-weighted Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images were used in ROC analysis to estimate the presence of ccRCC in the patients. The reference test positivity was the histopathologic examination of the surgical specimens. RESULTS Ninety-eight tumors from 91 patients were included in the study, and 59 of them were ccRCC, 29 were pRCC, and 10 were chRCC. The mpMRI features that had the three highest sensitivity rates were excretory phase SICP, T2-weighted HASTE scale score, and corticomedullary phase TCEI (93.2%, 91.5%, and 86.4%, respectively). However, those with the three highest specificity rates were nephrographic phase TCEI, excretory phase TCEI, and tumor ADC value (94.9%, 94.9%, and 89.7%, respectively). CONCLUSION Several parameters on mpMRI showed an acceptable performance to differentiate ccRCC from non-ccRCC.
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Affiliation(s)
- Cemal Aydoğan
- Department of Radiology, Trabzon Ahi Evren Thoracic and Cardiovascular Surgery Training and Research Hospital, Trabzon, Turkey.
| | - Ayşegül Cansu
- Department of Radiology, Karadeniz Technical University Faculty of Medicine, Trabzon, Turkey
| | - Zeynep Aydoğan
- Department of Radiology, Karadeniz Technical University Faculty of Medicine, Trabzon, Turkey
| | - Sinan Erdemi
- Department of Radiology, Tokat State Hospital, Tokat, Turkey
| | - Aykut Teymur
- Department of Radiology, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
| | - Onur Bektaş
- Department of Radiology, Karadeniz Technical University Faculty of Medicine, Trabzon, Turkey
| | - Sevdegül Mungan
- Department of Pathology, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
| | - İlke Onur Kazaz
- Department of Urology, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
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Sri Charan KB, Kalawat T, Priya RR, Nallabothula AK, Manthri RG, Reddy SC, Narendra H, Rukmangadha N, Kale PKG, Ajit N. Utility of Fluorine18 Fluoro-2-deoxy-D-glucose Positron Emission Tomography/Computed Tomography in Metabolic Characterization of Solid Renal Mass Lesion and Localization of Extra Renal Lesions in the Body - A Prospective Study from the Tertiary Care Center in South India. Indian J Nucl Med 2022; 37:329-336. [PMID: 36817204 PMCID: PMC9930448 DOI: 10.4103/ijnm.ijnm_41_22] [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: 02/26/2022] [Revised: 02/26/2022] [Accepted: 05/19/2022] [Indexed: 12/04/2022] Open
Abstract
Purpose of the Study Renal mass lesions in majority of the cases are due to malignant etiology and about one-third of them are reported with metastatic lesions at the time of presentation. Thus proper investigational workup is needed for staging and thereby treatment planning. The current fluorine18 fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (F18-FDG PET/CT) study was designed to characterize renal mass lesions metabolically and identifying other metabolically active lesions in the body suggesting metastatic disease. Materials and Methods A total of 24 patients (males - 18 and females - 6) with a mean age of 53.8 ± 12.3 years were recruited in this study for dual time-point PET/CT scan. All patients with renal mass lesions underwent contrast-enhanced CT prior to PET/CT. Metabolic parameters such as maximum standardized uptake value (max.SUV) with a cut off ≥2.5 and retention index (RI) of ≥10% were used to label the lesion as malignant and remaining less than cutoff as benign. The final diagnosis of lesion on imaging was confirmed with a histopathological examination (HPE). Results Using max.SUV cut off value, 17/24 renal mass lesions were characterized as malignant and remaining 7/24 renal lesions of benign etiology. PET/CT showed sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 80%, 75%, 94.1%, 42.8%, and 79.1%, respectively, by considering HPE as a gold standard. Nine patients were diagnosed with distant site involvement suggestive of metastases. Conclusion F18-FDG PET/CT can efficiently characterize solid renal mass lesion as benign and malignant using metabolic parameters such as max.SUV and RI. In addition, whole-body survey identified distant site involvement in 25% of the patients, thus contributing change in management.
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Affiliation(s)
| | - Tekchand Kalawat
- Department of Nuclear Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | - Rallapeta Ramya Priya
- Department of Nuclear Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | - Anil Kumar Nallabothula
- Department of Urology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | - Ranadheer Gupta Manthri
- Department of Nuclear Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | | | - Hulikal Narendra
- Department of Surgical Oncology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | - Nandyala Rukmangadha
- Department of Pathology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | - Pavan Kumar G Kale
- Department of Radiology, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
| | - Nimmagadda Ajit
- Department of Nuclear Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
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Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images. Bioengineering (Basel) 2022; 9:bioengineering9090423. [PMID: 36134972 PMCID: PMC9495730 DOI: 10.3390/bioengineering9090423] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/11/2022] [Accepted: 08/23/2022] [Indexed: 12/24/2022] Open
Abstract
Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model.
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10
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Zhu AH, Hou XY, Tian S, Zhang WF. Diagnostic value of 18F-FDG PET/CT versus contrast-enhanced MRI for venous tumour thrombus and venous bland thrombus in renal cell carcinoma. Sci Rep 2022; 12:587. [PMID: 35022440 PMCID: PMC8755813 DOI: 10.1038/s41598-021-04541-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/20/2021] [Indexed: 11/09/2022] Open
Abstract
The purpose of this study was to compare the ability of 18F-FDG PET/CT and contrast-enhanced MRI (CEMRI) to detect and grade venous tumour thrombus (VTT) and venous bland thrombus (VBT) in RCC and assess invasion of the venous wall by VTT. The PET/CT and CEMRI data of 41 patients with RCC were retrieved. The difference in maximum standardized uptake value (SUVmax) between VTT and VBT was analysed. According to their pathological diagnosis, the patients were divided into those with and without venous wall invasion. The PET/CT and CEMRI features, including the SUVmax of the primary lesion and VTT, maximum venous diameter, complete occlusion of the vein by VTT, and VTT morphology, were compared between the two groups. All 41 patients had VTT, and eleven of the 41 patients had VBT. The mean SUVmax of the VTT (6.33 ± 4. 68, n = 41) was significantly higher than that of the VBT (1.37 ± 0.26, n = 11; P < 0.001). Ten of the 11 cases of VBT were correctly diagnosed by 18F-FDG PET/CT, and all 11 were diagnosed by CEMRI. Both 18F-FDG PET/CT and CEMRI can effectively detect VTT and distinguish VTT from VBT. 18F-FDG PET/CT is less effective in grading VTT than CEMRI. Complete venous occlusion by VTT indicates venous wall invasion.
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Affiliation(s)
- An-Hui Zhu
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China
| | - Xiao-Yan Hou
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China
| | - Shuai Tian
- Department of Radiology, Peking University Third Hospital, Beijing, 100191, China
| | - Wei-Fang Zhang
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China.
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11
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PET imaging in renal and bladder cancers. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00139-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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12
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Xu Q, Zhu Q, Liu H, Chang L, Duan S, Dou W, Li S, Ye J. Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists. J Magn Reson Imaging 2021; 55:1251-1259. [PMID: 34462986 DOI: 10.1002/jmri.27900] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/14/2021] [Accepted: 08/17/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Differentiating benign from malignant renal tumors is important for selection of the most effective treatment. PURPOSE To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists. STUDY TYPE Retrospective. POPULATION A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44). FIELD STRENGTH/SEQUENCE Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T. ASSESSMENT A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists. STATISTICAL TESTS The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance. RESULTS The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts. CONCLUSION Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Qing Xu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China
| | - QingQiang Zhu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China
| | - Hao Liu
- Yizhun Medical AI, Beijing, China
| | | | | | | | - SaiYang Li
- Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Jing Ye
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China
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13
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Targeted PET/CT imaging for clear cell renal cell carcinoma with radiolabeled antibodies: recent developments using girentuximab. Curr Opin Urol 2021; 31:249-254. [PMID: 33742975 DOI: 10.1097/mou.0000000000000872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
PURPOSE OF REVIEW Conventional imaging is unable to differentiate clear cell renal cell carcinoma (ccRCC) from other more indolent and benign renal tumors. Positron emission tomography/computed tomography (PET/CT) using radiolabeled antibodies may aid in detecting both localized and metastatic ccRCC. The purpose of this review is to summarize recent literature regarding the use of radiolabeled antibodies for imaging of ccRCC. RECENT FINDINGS Two recent studies evaluated the use of radiolabeled anticarbonic anhydrase IX antibody girentuximab for the imaging of ccRCC. PET/CT with 89zirconium-labeled girentuximab (89Zr-girentuximab PET/CT) was used to guide clinical decision making in 16 patients with localized disease. It had a high specificity for detecting ccRCC with 6/6 resected lesions with uptake proven to be ccRCC, all lesion without uptake showed to be indolent during follow-up. Clinical management was changed in 36% (5/14) of patients with the metastatic disease based on outcomes of the scan. Furthermore, in 42 patients 89Zr-girentuximab PET/CT combined with CT outperformed CT alone or combined with 18F fludeoxyglucose PET for detection of metastasis. SUMMARY 89Zr-girentuximab PET/CT has the ability to diagnose ccRCC in localized disease. In metastatic disease, it enables the differentiation of ccRCC from non-ccRCC cancers and the evaluation of disease extent. 89Zr-girentuximab PET/CT diagnostic accuracy is currently evaluated in a multicenter phase III trial.
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14
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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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Merkx RIJ, Lobeek D, Konijnenberg M, Jiménez-Franco LD, Kluge A, Oosterwijk E, Mulders PFA, Rijpkema M. Phase I study to assess safety, biodistribution and radiation dosimetry for 89Zr-girentuximab in patients with renal cell carcinoma. Eur J Nucl Med Mol Imaging 2021; 48:3277-3285. [PMID: 33651116 PMCID: PMC8426244 DOI: 10.1007/s00259-021-05271-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/17/2021] [Indexed: 01/20/2023]
Abstract
PURPOSE In this phase I study, we evaluated the safety, biodistribution and dosimetry of [89Zr]Zr-DFO-girentuximab (89Zr-girentuximab) PET/CT imaging in patients with suspicion of clear cell renal cell carcinoma (ccRCC). METHODS Ten eligible patients received an intravenous administration of 37 MBq (± 10%) of 89Zr-girentuximab at mass doses of 5 mg or 10 mg. Safety was evaluated according to the NCI CTCAE (version 4.03). Biodistribution and normal organ dosimetry was performed based on PET/CT images acquired at 0.5, 4, 24, 72 and 168 h post-administration. Additionally, tumour dosimetry was performed in patients with confirmed ccRCC and visible tumour uptake on PET/CT imaging. RESULTS 89Zr-girentuximab was administered in ten patients as per protocol. No treatment-related adverse events ≥ grade 3 were reported. 89Zr-girentuximab imaging allowed successful differentiation between ccRCC and non-ccRCC lesions in all patients, as confirmed with histological data. Dosimetry analysis using OLINDA/EXM 2.1 showed that the organs receiving the highest doses (mean ± SD) were the liver (1.86 ± 0.40 mGy/MBq), the kidneys (1.50 ± 0.22 mGy/MBq) and the heart wall (1.45 ± 0.19 mGy/MBq), with a mean whole body effective dose of 0.57 ± 0.08 mSv/MBq. Tumour dosimetry was performed in the 6 patients with histologically confirmed ccRCC resulting in a median tumour-absorbed dose of 4.03 mGy/MBq (range 1.90-11.6 mGy/MBq). CONCLUSIONS This study demonstrates that 89Zr-girentuximab is safe and well tolerated for the administered activities and mass doses and allows quantitative assessment of 89Zr-girentuximab PET/CT imaging in patients with suspicion of ccRCC. TRIAL REGISTRATION NCT03556046-14th of June, 2018.
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Affiliation(s)
- Robin I J Merkx
- Department of Medical Imaging: Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands. .,Department of Urology, Radboudumc, Nijmegen, The Netherlands.
| | - Daphne Lobeek
- Department of Medical Imaging: Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mark Konijnenberg
- Department of Medical Imaging: Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | | | - Andreas Kluge
- ABX-CRO advanced pharmaceutical services, Dresden, Germany
| | | | | | - Mark Rijpkema
- Department of Medical Imaging: Nuclear Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
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16
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Machine learning based quantitative texture analysis of CT images for diagnosis of renal lesions. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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17
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Sweet DE, Ward RD, Wang Y, Tanaka H, Campbell SC, Remer EM. Infiltrative Renal Malignancies: Imaging Features, Prognostic Implications, and Mimics. Radiographics 2021; 41:487-508. [PMID: 33449838 DOI: 10.1148/rg.2021200123] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Infiltrative renal malignancies are a subset of renal masses that are morphologically characterized by a poorly defined interface with the renal parenchyma. Infiltrative renal malignancies are less common but more aggressive than more typical renal malignancies and carry an overall worse prognosis. Although an infiltrative renal process often represents a malignant neoplasm, infiltrative masses include a wide spectrum of diseases including primary renal cortical, medullary, and pelvic tumors; lymphoproliferative processes; metastases; and various infectious, inflammatory, immune-mediated, and vascular mimics. The imaging features of these masses are often nonspecific, but with the appropriate history, laboratory results, and clinical context, the radiologist can help narrow the diagnosis and guide further treatment. An invited commentary by Lee is available online.Online supplemental material is available for this article. ©RSNA, 2021.
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Affiliation(s)
- David E Sweet
- From the Imaging Institute (D.E.S., R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (Y.W., H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, L10, Cleveland, OH 44195; Department of Urology, First Hospital of Jilin University, Changchun, China (Y.W.); and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Ryan D Ward
- From the Imaging Institute (D.E.S., R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (Y.W., H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, L10, Cleveland, OH 44195; Department of Urology, First Hospital of Jilin University, Changchun, China (Y.W.); and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Yanbo Wang
- From the Imaging Institute (D.E.S., R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (Y.W., H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, L10, Cleveland, OH 44195; Department of Urology, First Hospital of Jilin University, Changchun, China (Y.W.); and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Hajime Tanaka
- From the Imaging Institute (D.E.S., R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (Y.W., H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, L10, Cleveland, OH 44195; Department of Urology, First Hospital of Jilin University, Changchun, China (Y.W.); and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Steven C Campbell
- From the Imaging Institute (D.E.S., R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (Y.W., H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, L10, Cleveland, OH 44195; Department of Urology, First Hospital of Jilin University, Changchun, China (Y.W.); and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Erick M Remer
- From the Imaging Institute (D.E.S., R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (Y.W., H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, L10, Cleveland, OH 44195; Department of Urology, First Hospital of Jilin University, Changchun, China (Y.W.); and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
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Devlin CM, Fowler S, Biyani CS, Forster JA. Changes in UK renal oncological surgical practice from 2008 to 2017: implications for cancer service provision and surgical training. BJU Int 2021; 128:206-217. [PMID: 33249738 DOI: 10.1111/bju.15310] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To determine and analyse the temporal changes in oncological nephrectomy practice and training opportunities using data obtained from the UK British Association of Urological Surgeons nephrectomy register from 2008 to 2017. PATIENT AND METHODS All nephrectomies within the dataset for this time period were analysed (n = 54 251). Cases were divided into radical nephrectomy (RN), partial nephrectomy (PN) and nephroureterectomy (NU). Simple nephrectomy, donor nephrectomy and benign PN were excluded. The annual frequencies for each oncological nephrectomy method, surgical approach, grade of surgeon, hospital caseload numbers and short-term surgical outcomes were determined. RESULTS Reported annual nephrectomy numbers increased by 2.5-fold in the 9-year time period. The number of hospitals performing nephrectomies decreased by 22%, however, more than 40% of centres performed more than 70 cases a year. There was a trend towards a decrease in overall length of hospital stay (9 vs 5 days; P < 0.01) and decreased transfusion rates. The proportion of minimally invasive procedures increased from 57% to 75%, with nephron-sparing rates increasing from 8.9% overall to 24.8%. With regard to surgical technique, robot-assisted surgery saw a mean annual increase of 222%. Overall, there was a 10% decrease in the proportion of PNs performed by trainee surgeons. CONCLUSIONS Renal surgery has changed considerably with regard to volume and also surgical approach, with rates of nephron-sparing surgery and minimally invasive surgery significantly increasing. Increasing hospital centralization and institutional experience, and a shift to robot-assisted surgery appear to have contributed to the observed improved patient outcomes. The increasing utilization of robot-assisted surgery has potential implications and challenges for the training of future urology surgeons.
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Affiliation(s)
- Conor M Devlin
- Urology Department, Bradford Royal Infirmary, Bradford Teaching Hospitals Foundation Trust, Bradford, UK
| | - Sarah Fowler
- British Association of Urological Surgeons, London, UK
| | - Chandra Shekhar Biyani
- Department of Urology, St James's University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - James A Forster
- Urology Department, Bradford Royal Infirmary, Bradford Teaching Hospitals Foundation Trust, Bradford, UK
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19
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Prevalence of benign pathology after partial nephrectomy for suspected renal tumor: A systematic review and meta-analysis. Int J Surg 2020; 84:161-170. [PMID: 33220454 DOI: 10.1016/j.ijsu.2020.11.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/13/2020] [Accepted: 11/09/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVE To investigate the overall prevalence of benign pathology after partial nephrectomy (PN) and identify predictive factors for benign pathology after PN. METHODS A systematic review was performed following the PRISMA guidelines. PubMed/Medline, Embase, and the Cochrane Library were searched up to January 2019PRISMA guidelines. The data for the meta-analysis and network meta-analysis were pooled using a random-effects model. RESULTS There were 144 studies included in the final analysis, which was comprised of 79 observational studies (n = 37,300) and 65 comparative studies (n = 18,552). The overall prevalence rate of benign pathology after PN was 0.19 (95% CI: 0.18-0.21). According to the procedure types, the prevalence rate of benign pathology was 0.17 (95% CI: 0.15-0.19), 0.24 (95% CI: 0.22-0.27), and 0.16 (95% CI: 0.15-0.18) in open partial nephrectomy, laparoscopic partial nephrectomy, and robot-assisted laparoscopic partial nephrectomy, respectively. The significant moderating factors were gender, publication year, the origin of the study, and procedure types. The three most common benign pathology types were oncocytomas, angiomyolipomas, and renal cysts (44.50%, 30.20%, and 10.99%, respectively). CONCLUSIONS The overall prevalence of benign pathology after PN was not low and it was affected by female gender, studies published before 2010, studies originating from Western areas, and laparoscopic procedure types.
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Wang ZJ, Nikolaidis P, Khatri G, Dogra VS, Ganeshan D, Goldfarb S, Gore JL, Gupta RT, Hartman RP, Heilbrun ME, Lyshchik A, Purysko AS, Savage SJ, Smith AD, Wolfman DJ, Wong-You-Cheong JJ, Lockhart ME. ACR Appropriateness Criteria® Indeterminate Renal Mass. J Am Coll Radiol 2020; 17:S415-S428. [PMID: 33153554 DOI: 10.1016/j.jacr.2020.09.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 12/15/2022]
Abstract
Renal masses are increasingly detected in asymptomatic individuals as incidental findings. CT and MRI with intravenous contrast and a dedicated multiphase protocol are the mainstays of evaluation for indeterminate renal masses. A single-phase postcontrast dual-energy CT can be useful when a dedicated multiphase renal protocol CT is not available. Contrast-enhanced ultrasound with microbubble agents is a useful alternative for characterizing renal masses, especially for patients in whom iodinated CT contrast or gadolinium-based MRI contrast is contraindicated. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
- Zhen J Wang
- University of California San Francisco School of Medicine, San Francisco, California.
| | | | - Gaurav Khatri
- Panel Vice-Chair, UT Southwestern Medical Center, Dallas, Texas
| | - Vikram S Dogra
- University of Rochester Medical Center, Rochester, New York
| | | | - Stanley Goldfarb
- University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; American Society of Nephrology
| | - John L Gore
- University of Washington, Seattle, Washington; American Urological Association
| | - Rajan T Gupta
- Duke University Medical Center, Durham, North Carolina
| | | | | | - Andrej Lyshchik
- Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | | | - Stephen J Savage
- Medical University of South Carolina, Charleston, South Carolina; American Urological Association
| | - Andrew D Smith
- University of Alabama at Birmingham, Birmingham, Alabama
| | - Darcy J Wolfman
- Johns Hopkins University School of Medicine, Washington, District of Columbia
| | | | - Mark E Lockhart
- Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama
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Untanas A, Trakymas M, Lekienė I, Briedienė R. Von Hippel-Lindau syndrome and renal tumours: radiological diagnostic and treatment options. A case report and literature review. Acta Med Litu 2020; 27:25-32. [PMID: 32577093 DOI: 10.6001/actamedica.v27i1.4263] [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: 11/27/2022] Open
Abstract
Background Von Hippel-Lindau disease (VHL) is a rare autosomal dominant syndrome diagnosed for 1 out of 36000-45000 newborns and 90% of the patients have a clinical manifestation before 65 years of age. Affected individuals have an increased risk of developing tumours in several organs or their systems. The most common tumours are retinal or central nervous system hemangioblastomas (60-80%) and VHL-associated renal lesions. Contrast-enhanced computer tomography (CECT) is the gold standard for the diagnosis and characterization of renal tumours. The best treatment option for VHL syndrome-caused renal tumours are nephron-sparing treatment techniques (cryotherapy, radiofrequency, or microwave ablation), which require imaging control. All these innovative treatment techniques are extremely important for VHL patients, because they increase the quality of life by staving off renal dialysis and preventing distant metastases. Case report Our case report presents a 16-year-old female with multiple renal cysts observed on ultrasound examination and clinically and molecularly diagnosed with Von Hippel-Lindau syndrome (deletion of the entire VHL gene). After that, for past 11 years multiple renal tumours were removed by cryoablation and patient monitoring on contrast-enhanced magnetic resonance (MRI) and CECT control scans was conducted. Conclusions Active multidisciplinary patient follow-up, routine radiological examinations, and correct treatment tactics allow controlling the progression of renal cell carcinoma and other tumours associated with VHL syndrome, maintaining a normal organ function for a long time, and preventing distant metastases and fatal disease outcomes.
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Affiliation(s)
- Audrius Untanas
- Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | | | - Indrė Lekienė
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Rūta Briedienė
- National Cancer Institute, Vilnius, Lithuania.,Faculty of Medicine, Vilnius University, Vilnius, Lithuania
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Cazzato RL, Garnon J, De Marini P, Auloge P, Koch G, Dalili D, Buy X, Palussiere J, Rao PP, Tricard T, Lang H, Gangi A. Is percutaneous image-guided renal tumour ablation ready for prime time? Br J Radiol 2020; 93:20200284. [PMID: 32543890 DOI: 10.1259/bjr.20200284] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
In the last few decades, thermal ablation (TA) techniques have been increasingly applied to treat small localised renal cell carcinomas. Despite this trend, there is still an underuse of TA compared to surgery and a substantial lack of high-quality evidence derived from large, prospective, randomised controlled trials comparing the long-term oncologic outcomes of TA and surgery. Therefore, in this narrative review, we assess published guidelines and recent literature concerning the diagnosis and management of kidney-confined renal cell carcinoma to understand whether percutaneous image-guided TA is ready to be proposed as a first-line treatment.
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Affiliation(s)
- Roberto Luigi Cazzato
- Interventional Radiology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Julien Garnon
- Interventional Radiology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Pierre De Marini
- Interventional Radiology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Pierre Auloge
- Interventional Radiology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Guillaume Koch
- Interventional Radiology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Danoob Dalili
- Department of Diagnostic and Interventional Radiology, Guy's and St. Thomas' Hospitals NHS Foundation Trust, 0 St Thomas St, London SE1 9RS, United Kingdom
| | - Xavier Buy
- Interventional Radiology, Institut Bergonié, 229 Cours de l'Argonne, 33000 Bordeaux, France
| | - Jean Palussiere
- Interventional Radiology, Institut Bergonié, 229 Cours de l'Argonne, 33000 Bordeaux, France
| | - Pramod Prabhakar Rao
- Interventional Radiology, Civil Hospital of Colmar; 39 Avenue de la Liberté, 68024 Colmar, France
| | - Thibault Tricard
- Urology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Hervé Lang
- Urology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
| | - Afshin Gangi
- Interventional Radiology, University Hospital of Strasbourg; 1 place de l'hôpital, 67000, Strasbourg, France
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23
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Oberai A, Varghese B, Cen S, Angelini T, Hwang D, Gill I, Aron M, Lau C, Duddalwar V. Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT. Br J Radiol 2020; 93:20200002. [PMID: 32356484 DOI: 10.1259/bjr.20200002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network. METHODS In this institutional review board approved study of 143 patients with predominantly solid, lipid-poor, contrast enhancing renal lesions (46 benign and 97 malignant), patients with a pre-operative multiphase CECT of the abdomen and pelvis obtained between June 2009 and June 2015 were retrospectively queried. Benign renal masses included oncocytoma and lipid-poor angiomyolipoma and the malignant group included clear cell, papillary, and chromophobe carcinomas.Region of interests of whole tumor volumes were manually segmented, and CT phase images with the largest cross-section of the segmented tumor in the axial plane were used for assessment. Post-surgical pathological evaluation was used to establish diagnosis.The segmented images of renal masses were used as input to a CNN. The data were augmented and split into training (83.9%) and validation sets (16.1%) to determine the hyperparameters of the CNN. Thereafter. the performance of the resulting CNN was quantified using eightfold cross-validation. RESULTS The CNN-based classifier demonstrated an overall accuracy of 78% (95% confidence interval: 76-80%), sensitivity of 70% (95% confidence interval: 66-74%), specificity of 81% (79-83%) and an area under the curve of 0.82. CONCLUSION A CNN-based classifier to diagnose solid enhancing malignant renal masses based on multiphase CECT images was developed. ADVANCES IN KNOWLEDGE It was established that a CNN-based classifier could be trained to accurately distinguish malignant renal lesions.
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Affiliation(s)
- Assad Oberai
- Department of Aerospace and Mechanical Engineering, Univ. of Southern California, Los Angeles, CA, USA
| | - Bino Varghese
- Department of Radiology, Univ. of Southern California, Los Angeles, CA, USA
| | - Steven Cen
- Department of Radiology, Univ. of Southern California, Los Angeles, CA, USA
| | - Tomas Angelini
- Department of Computer Science, Univ. of Southern California, Los Angeles, CA, USA
| | - Darryl Hwang
- Department of Radiology, Univ. of Southern California, Los Angeles, CA, USA
| | - Inderbir Gill
- Institute of Urology, Univ. of Southern California, Los Angeles, CA, USA
| | - Manju Aron
- Department of Pathology, Univ. of Southern California, Los Angeles, CA, USA
| | - Christopher Lau
- Department of Radiology, Univ. of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Univ. of Southern California, Los Angeles, CA, USA.,Institute of Urology, Univ. of Southern California, Los Angeles, CA, USA
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24
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Joe WB, Zarzour JG, Gunn AJ. Renal Cell Carcinoma Ablation: Preprocedural, Intraprocedural, and Postprocedural Imaging. Radiol Imaging Cancer 2019; 1:e190002. [PMID: 33778679 DOI: 10.1148/rycan.2019190002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 08/13/2019] [Accepted: 08/16/2019] [Indexed: 01/20/2023]
Abstract
The rising incidence of renal cell carcinoma (RCC) in recent decades necessitates careful consideration of additional treatment options, especially for patients who may be poor surgical candidates. An emerging body of evidence suggests that ablation may be performed effectively and safely even in patients with multiple comorbidities. Accordingly, clinical guidelines now include thermal ablation as an alternative for such patients with localized tumors that are 4.0 cm or smaller. Recent experience with these minimally invasive techniques has led to a greater understanding of the imaging findings that merit close attention when ablation is anticipated, or after it is performed. These imaging findings may guide the interventionalist's perception of the risks, technical challenges, and likelihood of treatment success associated with RCC ablation. The present review provides an overview of clinically relevant radiologic findings during the preprocedural, intraprocedural, and postprocedural period in the context of image-guided renal ablation. Keywords: Interventional-Body, Kidney, Percutaneous, Urinary © RSNA, 2019.
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Affiliation(s)
- Winston B Joe
- University of Alabama at Birmingham School of Medicine, Birmingham, Ala (W.B.J.); and Divisions of Abdominal Imaging (J.G.Z.) and Vascular and Interventional Radiology (A.J.G.), Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35249
| | - Jessica G Zarzour
- University of Alabama at Birmingham School of Medicine, Birmingham, Ala (W.B.J.); and Divisions of Abdominal Imaging (J.G.Z.) and Vascular and Interventional Radiology (A.J.G.), Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35249
| | - Andrew J Gunn
- University of Alabama at Birmingham School of Medicine, Birmingham, Ala (W.B.J.); and Divisions of Abdominal Imaging (J.G.Z.) and Vascular and Interventional Radiology (A.J.G.), Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35249
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25
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Kim JH, Li S, Khandwala Y, Chung KJ, Park HK, Chung BI. Association of Prevalence of Benign Pathologic Findings After Partial Nephrectomy With Preoperative Imaging Patterns in the United States From 2007 to 2014. JAMA Surg 2019; 154:225-231. [PMID: 30516801 DOI: 10.1001/jamasurg.2018.4602] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Importance Although the intent of nephron-sparing surgery is to eradicate malignant tumors found on preoperative imaging, benign masses often cannot be differentiated from malignant tumors. However, in the past there have been discrepancies in the reported percentages of benign masses removed by partial nephrectomy (PNx). Objective To investigate the annual trend of prevalence of benign pathologic findings after PNx and to investigate what potential factors are associated with this prevalence. Design, Setting, and Participants A total of 18 060 patients who underwent PNx between 2007 and 2014 were selected from Truven Health MarketScan Research Databases. We selected those patients who underwent PNx as an inpatient from 2007 and set the surgery date as the index date. Overall, a total of 21 445 patients with International Classification of Diseases, Ninth Revision, Clinical Modification code of 55.4 were identified from 2007 to 2015. Main Outcomes and Measures The annual trend of benign pathologic findings was described as an actual number and as a proportion. Univariate and multiple analyses were performed to investigate factors predictive of a benign final pathologic diagnosis, including type of preoperative imaging modality or performance of a renal mass biopsy. Results Among the 18 060 patients, mean (SD) age was 57 (12) years, and there were 10637 (58.9%) men and 7423 (41.1%) women. The overall prevalence of benign pathologic findings was 30.9% and the annual trends demonstrated a prevalence of over 30% for nearly every year of the study period. On univariate analysis, the performance of magnetic resonance imaging (MRI) and renal mass biopsy was associated with benign pathologic findings (P = .02 and P < .001, respectively). On multivariable analysis, female sex (odds ratio [OR], 0.62; 95% CI, 0.58-0.66; P < .001), older age (>65 years) (OR, 0.99; 95% CI, 0.99-0.99; P < .001), and computed tomography (CT) only preoperative imaging (OR, 1.16; 95% CI, 1.05-1.28; P = .004) were associated with benign pathologic findings after PNx. Conclusions and Relevance We found that the overall prevalence of benign pathologic findings after PNx was higher than the literature suggests, with consistent year-over-year rates exceeding 30%. Female sex, older age (>65 years), and CT only preoperative imaging were predictive of a benign tumor. Further elucidation concerning covariates associated with a benign diagnosis should be the focus of future investigations to identify a cohort of patients who could potentially avoid unnecessary surgical intervention.
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Affiliation(s)
- Jae Heon Kim
- Department of Urology, Stanford University Medical Center, Stanford, California.,Department of Urology, Soonchunhyang University Hospital, Soonchuhyang University Medical College, Seoul, Korea
| | - Shufeng Li
- Department of Urology and Dermatology, Stanford University Medical Center, Stanford, California
| | - Yash Khandwala
- Department of Urology, Stanford University Medical Center, Stanford, California.,San Diego School of Medicine, University of California, San Diego
| | - Kyung Jin Chung
- Department of Urology, Stanford University Medical Center, Stanford, California
| | - Hyung Keun Park
- Department of Urology, Stanford University Medical Center, Stanford, California
| | - Benjamin I Chung
- Department of Urology, Stanford University Medical Center, Stanford, California
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26
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Kim JH, Li S, Khandwala Y, Del Giudice F, Chung KJ, Park HK, Chung BI. National trends of preoperative imaging modalities before partial nephrectomy for renal masses in the U.S. from 2007-2015. Can Urol Assoc J 2019; 13:E89-E94. [PMID: 30169151 DOI: 10.5489/cuaj.5414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Although the performance of partial nephrectomies (PN) for renal masses has increased rapidly over the years, only a few studies have investigated the frequency and patterns of preoperative imaging modalities. The aim of this study was to investigate the frequency and patterns in preoperative imaging modalities before PN. METHODS A total of 21 445 patients who underwent PN between 2007 and 2015 were selected from a national representative population in the MarketScan database and included in this study. The annual incidence and proportion of PN, as well as the use of each preoperative imaging modality were analyzed. RESULTS Both annual crude number and frequency rate of PN decreased or became static since 2012. Computed tomography (CT) shows the greatest proportion of the crude number and percentage; despite a slight decrease in percentage, it is still >80%. Among the combinations, CT alone and CT combined with ultrasonography showed the highest performance rate during the complete observational period. The proportion of all other combinations, which include other complex combinations except CT alone, CT plus ultrasonography, CT plus magnetic resonance imaging (MRI), and CT plus MRI plus ultrasonography, was 13.95% in 2007, but increased to 19.04% in 2014. CONCLUSIONS CT still plays a major role in preoperative imaging for renal masses, whereby CT alone and CT combined with ultrasonography account for a major proportion of the preoperative imaging patterns. The use of other imaging combinations, as well as renal biopsies, shows an increasing trend. Additional studies are needed to investigate whether this trend in preoperative imaging is related to the frequency rate of PN.
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Affiliation(s)
- Jae Heon Kim
- Department of Urology, Stanford University Medical Center, Stanford, CA, United States.,Department of Urology, Soonchunhyang University Hospital, Soonchuhyang University Medical College, Seoul, Korea
| | - Shufeng Li
- Department of Urology and Dermatology, Stanford University Medical Center, CA, United States
| | - Yash Khandwala
- Department of Urology, Stanford University Medical Center, Stanford, CA, United States.,University of California, San Diego School of Medicine, San Diego, CA, United States
| | | | - Kyung Jin Chung
- Department of Urology, Stanford University Medical Center, Stanford, CA, United States
| | - Hyung Keun Park
- Department of Urology, Stanford University Medical Center, Stanford, CA, United States
| | - Benjamin I Chung
- Department of Urology, Stanford University Medical Center, Stanford, CA, United States
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27
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Durai P, Wu QH, Chiong E. Clinical Aspects and Investigations in Genitourinary Cancer. Urol Oncol 2019. [DOI: 10.1007/978-3-319-42623-5_46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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28
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Vishwanath V, Mayer D, Fu D, Wnorowski A, Siddiqui MM. Hyperpolarized 13C magnetic resonance imaging, using metabolic imaging to improve the detection and management of prostate, bladder, and kidney urologic malignancies. Transl Androl Urol 2018; 7:855-863. [PMID: 30456188 PMCID: PMC6212626 DOI: 10.21037/tau.2018.08.27] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Approximately 25% of the 2 million new cancer diagnoses in the United States in 2018 were comprised of malignancies of the urogenital system. Of these cancers, 75% occurred in the kidney/renal pelvis, prostate, and urinary bladder. Early diagnosis is beneficial to long-term survival. Currently, urologists rely heavily on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and positron emission tomography (PET) to both diagnose and offer prognoses, but these techniques are limited in their resolution and are more effective when cancers have reached macroscopic size in later stages. Recent developments in cancer metabolomics have revealed that cancerous cells preferentially upregulate specific metabolic pathways as a means of conserving their resources and maximizing their growth potential. This has opened a new avenue for early diagnosis with much higher resolution, reliability, and accuracy through 13C hyperpolarized MRI. Preferential cancer pathways can be elucidated through this technique using 13C-labeled molecules utilized for energy generation and tumor growth. As these pathways are identified, targeted therapies are being designed to inhibit these pathways to allow for treatment that is cytotoxic to malignant cells but preserves native cells. In this paper, we review the current understanding of urologic cancer metabolomics, specifically in the kidney, prostate, and bladder. We will review the basic physics of MRI and demonstrate how hyperpolarized 13C MRI offers an innovative solution to early diagnosis as well as creates novel avenues for more targeted therapy.
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Affiliation(s)
- Vijay Vishwanath
- Division of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Dirk Mayer
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.,Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Dexue Fu
- Division of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Amelia Wnorowski
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mohummad Minhaj Siddiqui
- Division of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA.,Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.,The Veterans Health Administration Research and Development Service, Baltimore, MD, USA
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29
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Ward RD, Tanaka H, Campbell SC, Remer EM. 2017 AUA Renal Mass and Localized Renal Cancer Guidelines: Imaging Implications. Radiographics 2018; 38:2021-2033. [DOI: 10.1148/rg.2018180127] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Ryan D. Ward
- From the Imaging Institute (R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, A21, Cleveland, OH 44195; and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Hajime Tanaka
- From the Imaging Institute (R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, A21, Cleveland, OH 44195; and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Steven C. Campbell
- From the Imaging Institute (R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, A21, Cleveland, OH 44195; and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
| | - Erick M. Remer
- From the Imaging Institute (R.D.W., E.M.R.) and Glickman Urological and Kidney Institute (H.T., S.C.C., E.M.R.), Cleveland Clinic, 9500 Euclid Ave, A21, Cleveland, OH 44195; and Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan (H.T.)
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30
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van Oostenbrugge TJ, Fütterer JJ, Mulders PFA. Diagnostic Imaging for Solid Renal Tumors: A Pictorial Review. KIDNEY CANCER 2018; 2:79-93. [PMID: 30740580 PMCID: PMC6364093 DOI: 10.3233/kca-180028] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The prognosis of renal tumors depends on histologic subtype. The increased use of abdominal imaging has resulted in an increase in the number of small renal incidentaloma in recent decades. Of these incidentally discovered tumors, 20% are benign lesions warranting conservative management, but most are renal cell carcinomas that warrant a more aggressive therapeutic approach due to their malignant potential. Dedicated diagnostic renal imaging is important for characterization of renal tumors to facilitate treatment planning. This review discusses the ability to detect and differentiate renal cell carcinoma subtypes, angiomyolipoma and oncocytoma based on ultrasound imaging, computed tomography, multiparametric magnetic resonance, and nuclear imaging.
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Affiliation(s)
| | - Jurgen J Fütterer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter F A Mulders
- Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands
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31
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Clinical Aspects and Investigations in Genitourinary Cancer. Urol Oncol 2018. [DOI: 10.1007/978-3-319-42603-7_46-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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32
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Tricard T, Tsoumakidou G, Lindner V, Garnon J, Albrand G, Cathelineau X, Gangi A, Lang H. Thérapies ablatives dans le cancer du rein : indications. Prog Urol 2017; 27:926-951. [DOI: 10.1016/j.purol.2017.07.245] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 07/22/2017] [Indexed: 12/19/2022]
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33
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Abdel-Rahman O. Impact of tumor size on the outcome of patients with small renal cell carcinoma. Expert Rev Anticancer Ther 2017; 17:769-773. [PMID: 28593803 DOI: 10.1080/14737140.2017.1340838] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND This study aims to establish potential correlation between tumor size and outcomes in patients with T1a kidney cancer registered within the surveillance, epidemiology and end results (SEER) database. METHODS SEER database (2004-2013) has been accessed through SEER*Stat program to determine the correlation between tumor size and cancer-specific survival in patients with T1a kidney cancer. Survival analysis was conducted through Kaplan-Meier analysis and log-rank testing. RESULTS Five year kidney cancer-specific survival rates show progressive decline with increasing tumor size. Moreover, kidney cancer-specific survival has been compared according to the initial local treatment modality (observation, ablation, partial or radical nephrectomy) across different size categories (<1 cm, 1-2 cm, 2-3 cm and 3-4 cm). Survival curves of different treatment modalities were almost overlapping for patients with renal mass < 1cm. For patients with tumor size 1-2 cm, treatment modalities were overlapping at the first 60 months then the curve of observation diverged (P <0.0001). For patients with tumor size 2-3 cm and 3-4 cm, the curve of observation diverged early in the time course (P <0.0001). CONCLUSION Primary tumor size is an important factor that should be taken into consideration when evaluating the different treatment options for patients with small kidney cancers..
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Affiliation(s)
- Omar Abdel-Rahman
- a Clinical Oncology Department, Faculty of Medicine , Ain Shams University , Cairo , Egypt
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