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Horvat-Menih I, Li H, Priest AN, Li S, Gill AB, Mendichovszky IA, Francis ST, Warren AY, O'Carrigan B, Welsh SJ, Jones JO, Riddick ACP, Armitage JN, Mitchell TJ, Stewart GD, Gallagher FA. High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades. Eur Radiol Exp 2024; 8:76. [PMID: 38981998 PMCID: PMC11233479 DOI: 10.1186/s41747-024-00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/25/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Clinical imaging tools to probe aggressiveness of renal masses are lacking, and T2-weighted imaging as an integral part of magnetic resonance imaging protocol only provides qualitative information. We developed high-resolution and accelerated T2 mapping methods based on echo merging and using k-t undersampling and reduced flip angles (TEMPURA) and tested their potential to quantify differences between renal tumour subtypes and grades. METHODS Twenty-four patients with treatment-naïve renal tumours were imaged: seven renal oncocytomas (RO); one eosinophilic/oncocytic renal cell carcinoma; two chromophobe RCCs (chRCC); three papillary RCCs (pRCC); and twelve clear cell RCCs (ccRCC). Median, kurtosis, and skewness of T2 were quantified in tumours and in the normal-adjacent kidney cortex and were compared across renal tumour subtypes and between ccRCC grades. RESULTS High-resolution TEMPURA depicted the tumour structure at improved resolution compared to conventional T2-weighted imaging. The lowest median T2 values were present in pRCC (high-resolution, 51 ms; accelerated, 45 ms), which was significantly lower than RO (high-resolution; accelerated, p = 0.012) and ccRCC (high-resolution, p = 0.019; accelerated, p = 0.008). ROs showed the lowest kurtosis (high-resolution, 3.4; accelerated, 4.0), suggestive of low intratumoural heterogeneity. Lower T2 values were observed in higher compared to lower grade ccRCCs (grades 2, 3 and 4 on high-resolution, 209 ms, 151 ms, and 106 ms; on accelerated, 172 ms, 160 ms, and 102 ms, respectively), with accelerated TEMPURA showing statistical significance in comparison (p = 0.037). CONCLUSIONS Both high-resolution and accelerated TEMPURA showed marked potential to quantify differences across renal tumour subtypes and between ccRCC grades. TRIAL REGISTRATION ClinicalTrials.gov, NCT03741426 . Registered on 13 November 2018. RELEVANCE STATEMENT The newly developed T2 mapping methods have improved resolution, shorter acquisition times, and promising quantifiable readouts to characterise incidental renal masses.
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Affiliation(s)
- Ines Horvat-Menih
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
| | - Hao Li
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- The Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Andrew N Priest
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - Shaohang Li
- The Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Andrew B Gill
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Iosif A Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - Anne Y Warren
- Department of Pathology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - Brent O'Carrigan
- Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - Sarah J Welsh
- Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - James O Jones
- Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - Antony C P Riddick
- Department of Urology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - James N Armitage
- Department of Urology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - Thomas J Mitchell
- Department of Urology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
- Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Grant D Stewart
- Department of Urology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
- Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
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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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Salles-Silva E, Lima EM, Amorim VB, Milito M, Parente DB. Clear cell likelihood score may improve diagnosis and management of renal masses. Abdom Radiol (NY) 2024:10.1007/s00261-024-04415-4. [PMID: 38900323 DOI: 10.1007/s00261-024-04415-4] [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: 04/29/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 06/21/2024]
Abstract
The detection of solid renal masses has increased over time due to incidental findings during imaging studies conducted for unrelated medical conditions. Approximately 20% of lesions measuring less than 4 cm are benign and 80% are malignant. Clear cell renal cell carcinoma (ccRCC) is the most frequent among renal carcinomas, responsible for 65-80% of cases. The increased detection of renal masses facilitates early diagnosis and treatment. However, it also leads to more invasive interventions, which result in higher morbidity and costs. Currently, only histological analysis can offer an accurate diagnosis. Surgical nephron loss significantly elevates morbidity and mortality rates. Active surveillance represents a conservative management approach for patients diagnosed with a solid renal mass that is endorsed by both American Urological Association and the European Society for Medical Oncology. However, active surveillance is used in a minority of patients and varies across institutions. The lack of clinical studies using a standardized approach to incidentally detected small renal masses precludes the widespread use of active surveillance. Hence, there is an urgent need for better patient selection, distinguishing those who require surgery from those suitable for active surveillance. The clear cell likelihood score (ccLS) represents a novel MRI tool for assessing the probability of a renal mass being a ccRCC. In this study, we present a comprehensive review of renal masses and their evaluation using the ccLS to facilitate shared decision between urologists and patients.
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Affiliation(s)
- Eleonora Salles-Silva
- Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Grupo Fleury, Rio de Janeiro, RJ, Brazil
| | - Elissandra Melo Lima
- Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Grupo Fleury, Rio de Janeiro, RJ, Brazil
| | - Viviane Brandão Amorim
- Grupo Fleury, Rio de Janeiro, RJ, Brazil
- Brazilian National Cancer Institute, Rio de Janeiro, RJ, Brazil
| | - Miguel Milito
- Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Daniella Braz Parente
- Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
- Grupo Fleury, Rio de Janeiro, RJ, Brazil.
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Pinto PVA, Coelho FMA, Schuch A, Zapparoli M, Baroni RH. Pre-operative imaging evaluation of renal cell carcinoma. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2024; 70:e2024S107. [PMID: 38865527 PMCID: PMC11164270 DOI: 10.1590/1806-9282.2024s107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 06/14/2024]
Affiliation(s)
- Paulo Victor Alves Pinto
- Hospital Israelita Albert Einstein, Brazilian College of Radiology Genitourinary Group, Department of Radiology – São Paulo (SP), Brazil
| | - Fernando Morbeck Almeida Coelho
- Hospital Israelita Albert Einstein, Brazilian College of Radiology Genitourinary Group, Department of Radiology – São Paulo (SP), Brazil
| | - Alice Schuch
- Hospital Moinhos de Vento, Brazilian College of Radiology Genitourinary Group, Department of Radiology – Porto Alegre (RS), Brazil
| | - Mauricio Zapparoli
- Advanced Imaging Diagnosis, Brazilian College of Radiology Genitourinary Group, Department of Radiology – Curitiba (PR), Brazil
| | - Ronaldo Hueb Baroni
- Hospital Israelita Albert Einstein, Brazilian College of Radiology Genitourinary Group, Department of Radiology – São Paulo (SP), Brazil
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Eldihimi F, Walsh C, Hibbert RM, Nasibi KA, Pickovsky JS, Schieda N. Evaluation of a multiparametric renal CT algorithm for diagnosis of clear-cell renal cell carcinoma among small (≤ 4 cm) solid renal masses. Eur Radiol 2024; 34:3992-4000. [PMID: 37968475 DOI: 10.1007/s00330-023-10434-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/11/2023] [Accepted: 09/13/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE To evaluate a recently proposed CT-based algorithm for diagnosis of clear-cell renal cell carcinoma (ccRCC) among small (≤ 4 cm) solid renal masses diagnosed by renal mass biopsy. METHODS This retrospective study included 51 small renal masses in 51 patients with renal-mass CT and biopsy between 2014 and 2021. Three radiologists independently evaluated corticomedullary phase CT for the following: heterogeneity and attenuation ratio (mass:renal cortex), which were used to inform the CT score (1-5). CT score ≥ 4 was considered positive for ccRCC. Diagnostic accuracy was calculated for each reader and overall using fixed effects logistic regression modelling. RESULTS There were 51% (26/51) ccRCC and 49% (25/51) other masses. For diagnosis of ccRCC, area under curve (AUC), sensitivity, specificity, and positive predictive value (PPV) were 0.69 (95% confidence interval 0.61-0.76), 78% (68-86%), 59% (46-71%), and 67% (54-79%), respectively. CT score ≤ 2 had a negative predictive value 97% (92-99%) to exclude diagnosis of ccRCC. For diagnosis of papillary renal cell carcinoma (pRCC), CT score ≤ 2, AUC, sensitivity, specificity, and PPV were 0.89 (0.81-0.98), 81% (58-94%), 98% (93-99%), and 85% (62-97%), respectively. Pooled inter-observer agreement for CT scoring was moderate (Fleiss weighted kappa = 0.52). CONCLUSION The CT scoring system for prediction of ccRCC was sensitive with a high negative predictive value and moderate agreement. The CT score is highly specific for diagnosis of pRCC. CLINICAL RELEVANCE STATEMENT The CT score algorithm may help guide renal mass biopsy decisions in clinical practice, with high sensitivity to identify clear-cell tumors for biopsy to establish diagnosis and grade and high specificity to avoid biopsy in papillary tumors. KEY POINTS • A CT score ≥ 4 had high sensitivity and negative predictive value for diagnosis of clear-cell renal cell carcinoma (RCC) among solid ≤ 4-cm renal masses. • A CT score ≤ 2 was highly specific for diagnosis of papillary RCC among solid ≤ 4-cm renal masses. • Inter-observer agreement for CT score was moderate.
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Affiliation(s)
- Fatma Eldihimi
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Cynthia Walsh
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Rebecca M Hibbert
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Khalid Al Nasibi
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Jana Sheinis Pickovsky
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada.
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Chen KY, Lange MJ, Qiu JX, Lambert D, Mithqal A, Krupski TL, Schenkman NS, Lobo JM. Cost-Effectiveness Analysis of the Clear Cell Likelihood Score Against Renal Mass Biopsy for Evaluating Small Renal Masses. Urology 2024; 188:111-117. [PMID: 38648945 PMCID: PMC11193637 DOI: 10.1016/j.urology.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/21/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVE To examine the cost-effectiveness of the clear cell likelihood score compared to renal mass biopsy (RMB) alone. METHODS The clear cell likelihood score, a new grading system based on multiparametric magnetic resonance imaging, has been proposed as a possible alternative to percutaneous RMB for identifying clear cell renal carcinoma in small renal masses and expediting treatment of high-risk patients. A decision analysis model was developed to compare a RMB strategy where all patients undergo biopsy and a clear cell likelihood score strategy where only patients that received an indeterminant score of 3 undergo biopsy. Effectiveness was assigned 1 for correct diagnoses and 0 for incorrect or indeterminant diagnoses. Costs were obtained from institutional fees and Medicare reimbursement rates. Probabilities were derived from literature estimates from radiologists trained in the clear cell likelihood score. RESULTS In the base case model, the clear cell likelihood score was both more effective (0.77 vs 0.70) and less expensive than RMB ($1629 vs $1966). Sensitivity analysis found that the nondiagnostic rate of RMB and the sensitivity of the clear cell likelihood score had the greatest impact on the model. In threshold analyses, the clear cell likelihood score was the preferred strategy when its sensitivity was greater than 62.7% and when an MRI cost less than $5332. CONCLUSION The clear cell likelihood score is a more cost-effective option than RMB alone for evaluating small renal masses for clear cell renal carcinoma.
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Affiliation(s)
- Katherina Y Chen
- Department of Urology, University of Virginia, Charlottesville, VA
| | - Moritz J Lange
- University of Virginia School of Medicine, Charlottesville, VA
| | - Jessica X Qiu
- University of Virginia School of Medicine, Charlottesville, VA
| | - Drew Lambert
- Department of Radiology and Medical Imaging, Charlottesville, VA
| | - Ayman Mithqal
- Department of Radiology and Medical Imaging, Charlottesville, VA
| | - Tracey L Krupski
- Department of Urology, University of Virginia, Charlottesville, VA
| | - Noah S Schenkman
- Department of Urology, University of Virginia, Charlottesville, VA
| | - Jennifer M Lobo
- Department of Urology, University of Virginia, Charlottesville, VA; Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA.
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Xu H, Taylor-Cho IA, Sara Jiang X, Foo WC. Diagnostic accuracy and clinical impact of renal biopsy cytology. Diagn Cytopathol 2024. [PMID: 38801188 DOI: 10.1002/dc.25357] [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: 03/02/2024] [Revised: 04/29/2024] [Accepted: 05/14/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND The role of fine needle biopsy cytology in the workup of renal mass lesions remains controversial. With advances in imaging technology and clinical management for renal masses, a critical reevaluation of the role of renal biopsy is needed. This study was designed to provide a comprehensive evaluation of the performance and clinical impact of fine needle biopsy in patients with renal masses. METHODS A 5-year retrospective study of ultrasound or computer tomography (CT)-guided fine needle biopsies of renal masses diagnosed via cytopathology was conducted. Overall diagnostic rate, sensitivity, and diagnostic accuracy were calculated. Further analysis of the impact of fine needle biopsy cytology on clinical management was performed. RESULTS A total of 227 cases of fine-needle aspiration and/or biopsy (FNA/B) of renal masses were identified, including 76 with subsequent nephrectomies. Complications were rare (<1%). The diagnostic rate and sensitivity of FNA/B were 83.3% and 89.5%, respectively. Diagnostic accuracy was 98.7% at the major categorical level and 94.7% at the tumor subtype level. Subsequent clinical actions were associated with a definitive cytologic diagnosis of malignancy/neoplasia (p < .05) and were affected by tumor subtype (p < .05). CONCLUSION This study demonstrates that FNA/B of renal masses is a safe and reliable minimally invasive diagnostic tool with excellent accuracy in confirmation of malignancy and subclassification of tumors. Diagnoses made on FNA/B play a key role in guiding a personalized clinical treatment plan.
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Affiliation(s)
- Hongzhi Xu
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - Ian A Taylor-Cho
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - Xiaoyin Sara Jiang
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - Wen-Chi Foo
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
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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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Hao YW, Ning XY, Wang H, Bai X, Zhao J, Xu W, Zhang XJ, Yang DW, Jiang JH, Ding XH, Cui MQ, Liu BC, Guo HP, Ye HY, Wang HY. Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study. J Magn Reson Imaging 2024. [PMID: 38738786 DOI: 10.1002/jmri.29392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/23/2024] [Accepted: 03/25/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension. PURPOSE To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs. STUDY TYPE Retrospective. POPULATION 797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses. FIELD STRENGTH/SEQUENCES 3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging. ASSESSMENT Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions. STATISTICAL TESTS Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05. RESULTS The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993). CONCLUSION The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yu-Wei Hao
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
- Department of Radiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xue-Yi Ning
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xu Bai
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
- Department of Radiology, Fifth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jian Zhao
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Wei Xu
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xiao-Jing Zhang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Da-Wei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jia-Hui Jiang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiao-Hui Ding
- Department of Pathology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Meng-Qiu Cui
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Bai-Chuan Liu
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hui-Ping Guo
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hui-Yi Ye
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hai-Yi Wang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
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Dai C, Xiong Y, Zhu P, Yao L, Lin J, Yao J, Zhang X, Huang R, Wang R, Hou J, Wang K, Shi Z, Chen F, Guo J, Zeng M, Zhou J, Wang S. Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT. Radiology 2024; 311:e232178. [PMID: 38742970 DOI: 10.1148/radiol.232178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.
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Affiliation(s)
- Chenchen Dai
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Ying Xiong
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Pingyi Zhu
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Linpeng Yao
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jinglai Lin
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jiaxi Yao
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Xue Zhang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Risheng Huang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Run Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jun Hou
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Kang Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Zhang Shi
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Feng Chen
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jianming Guo
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Mengsu Zeng
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jianjun Zhou
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Shuo Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
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Vazquez LC, Xi Y, Rasmussen RG, Venzor JER, Kapur P, Zhong H, Dai JC, Morgan TN, Cadeddu JA, Pedrosa I. Characterization of Demographical Histologic Diversity in Small Renal Masses With the Clear Cell Likelihood Score. J Comput Assist Tomogr 2024; 48:370-377. [PMID: 38213063 DOI: 10.1097/rct.0000000000001567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
OBJECTIVE This study aimed to develop a diagnostic model to estimate the distribution of small renal mass (SRM; ≤4 cm) histologic subtypes for patients with different demographic backgrounds and clear cell likelihood score (ccLS) designations. MATERIALS AND METHODS A bi-institution retrospective cohort study was conducted where 347 patients (366 SRMs) underwent magnetic resonance imaging and received a ccLS before pathologic confirmation between June 2016 and November 2021. Age, sex, race, ethnicity, socioeconomic status, body mass index (BMI), and the ccLS were tabulated. The socioeconomic status for each patient was determined using the Area Deprivation Index associated with their residential address. The magnetic resonance imaging-derived ccLS assists in the characterization of SRMs by providing a likelihood of clear cell renal cell carcinoma (ccRCC). Pathological subtypes were grouped into four categories (ccRCC, papillary renal cell carcinoma, other renal cell carcinomas, or benign). Generalized estimating equations were used to estimate probabilities of the pathological subtypes across different patient subgroups. RESULTS Race and ethnicity, BMI, and ccLS were significant predictors of histology (all P < 0.001). Obese (BMI, ≥30 kg/m 2 ) Hispanic patients with ccLS of ≥4 had the highest estimated rate of ccRCC (97.1%), and normal-weight (BMI, <25 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 had the lowest (0.2%). The highest estimated rates of papillary renal cell carcinoma were found in overweight (BMI, 25-30 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 (92.3%), and the lowest, in obese Hispanic patients with ccLS ≥4 (<0.1%). CONCLUSIONS Patient race, ethnicity, BMI, and ccLS offer synergistic information to estimate the probabilities of SRM histologic subtypes.
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Affiliation(s)
| | - Yin Xi
- From the Department of Radiology, University of Texas Southwestern School of Medicine
| | - Robert G Rasmussen
- From the Department of Radiology, University of Texas Southwestern School of Medicine
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Reizine E, Blain M, Pescatori L, Longère B, Ingels A, Boughamni W, Bouanane M, Mulé S, Luciani A. Applicability of Bosniak 2019 for renal mass classification on portal venous phase at the era of spectral CT imaging using rapid kV-switching dual-energy CT. Eur Radiol 2024; 34:1816-1824. [PMID: 37667141 DOI: 10.1007/s00330-023-10145-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: 05/30/2023] [Revised: 05/30/2023] [Accepted: 07/10/2023] [Indexed: 09/06/2023]
Abstract
OBJECTIVES To evaluate the applicability of Bosniak 2019 criteria on a monophasic portal venous phase using rapid kilovoltage-switching DECT (rsDECT). MATERIALS AND METHODS One hundred twenty-seven renal masses assessed on rsDECT were included, classified according to Bosniak 2019 classification using MRI as the reference standard. Using the portal venous phase, virtual monochromatic images at 40, 50, and 77 keV; virtual unenhanced (VUE) images; and iodine map images were reconstructed. Changes in attenuation values between VUE and 40 keV, 50 keV, and 77 keV measurements were computed and respectively defined as ∆HU40keV, ∆HU50keV, and ∆HU77keV. The values of ∆HU40keV, ∆HU50keV, and ∆HU77keV thresholds providing the optimal diagnostic performance for the detection of internal enhancement were determined using Youden index. RESULTS Population study included 25 solid renal masses (25/127, 20%) and 102 cystic renal masses (102/127, 80%). To differentiate solid to cystic masses, the specificity of the predefined 20 HU threshold reached 88% (95%CI: 82, 93) using ∆HU77keV and 21% (95%CI: 15, 28) using ∆HU40keV. The estimated optimal threshold of attenuation change was 19 HU on ∆HU77keV, 69 HU on ∆HU50eV, and 111 HU on ∆HU40eV. The rsDECT classification was highly similar to that of MRI for solid renal masses (23/25, 92%) and for Bosniak 1 masses (62/66, 94%). However, 2 hyperattenuating Bosniak 2 renal masses (2/26, 8%) were classified as solid renal masses on rsDECT. CONCLUSION DECT is a promising tool for Bosniak classification particularly to differentiate solid from Bosniak I-II cyst. However, known enhancement thresholds must be adapted especially to the energy level of virtual monochromatic reconstructions. CLINICAL STATEMENT DECT is a promising tool for Bosniak classification; however, known enhancement thresholds must be adapted according to the types of reconstructions used and especially to the energy level of virtual monochromatic reconstructions. KEY POINTS • To differentiate solid to cystic renal masses, predefined 20 HU threshold had a poor specificity using 40 keV virtual monochromatic images. • Most of Bosniak 1 masses according to MRI were also classified as Bosniak 1 on rapid kV-switching dual-energy CT (rsDECT). • Bosniak 2 hyperattenuating renal cysts mimicked solid lesion on rsDECT.
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Affiliation(s)
- Edouard Reizine
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France.
- Faculté de Médecine, Université Paris Est Creteil, 94010, Creteil, France.
- INSERM Unit U 955, Equipe 18, 94010, Creteil, France.
- Imagerie Médicale, CHU Henri Mondor, 51 Avenue du Marechal de Lattre de Tassigny, 94010, Créteil, France.
| | - Maxime Blain
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
- Faculté de Médecine, Université Paris Est Creteil, 94010, Creteil, France
| | - Lorenzo Pescatori
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
| | - Benjamin Longère
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
- University Lille, U1011 - European Genomic Institute for Diabetes, 59000, Lille, France
- INSERM U1011, 59000, Lille, France
- Department of Cardiovascular Radiology, CHU Lille, 59000, Lille, France
- Institut Pasteur Lille, 59000, Lille, France
| | | | - Wafa Boughamni
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
| | - Mohamed Bouanane
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
| | - Sébastien Mulé
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
- Faculté de Médecine, Université Paris Est Creteil, 94010, Creteil, France
- INSERM Unit U 955, Equipe 18, 94010, Creteil, France
| | - Alain Luciani
- Department of Radiology, APHP, HU Henri Mondor, Creteil, Val-de-Marne, France
- Faculté de Médecine, Université Paris Est Creteil, 94010, Creteil, France
- INSERM Unit U 955, Equipe 18, 94010, Creteil, France
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Hao YW, Zhang Y, Guo HP, Xu W, Bai X, Zhao J, Ding XH, Gao S, Cui MQ, Liu BC, Ye HY, Wang HY. Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score. Abdom Radiol (NY) 2023; 48:3714-3727. [PMID: 37747536 DOI: 10.1007/s00261-023-04034-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023]
Abstract
PURPOSE Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC. METHODS We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)]. RESULTS In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1-2) and 2 to 5 (76.7% in scores 4-5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780). CONCLUSION The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.
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Affiliation(s)
- Yu-Wei Hao
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yun Zhang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Department of Radiology, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hui-Ping Guo
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Wei Xu
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Xu Bai
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Jian Zhao
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Xiao-Hui Ding
- Department of Pathology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Sheng Gao
- Department of Radiology, Linyi Central Hospital, Shandong, China
| | - Meng-Qiu Cui
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Bai-Chuan Liu
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Hui-Yi Ye
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Hai-Yi Wang
- Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China.
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14
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Li S, Liao Z, He K, Shen Y, Hu S, Li Z. Association of sex-specific abdominal adipose tissue with WHO/ISUP grade in clear cell renal cell carcinoma. Insights Imaging 2023; 14:194. [PMID: 37980639 PMCID: PMC10657923 DOI: 10.1186/s13244-023-01494-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 11/21/2023] Open
Abstract
OBJECTIVES To explore the association between computed tomography (CT)-measured sex-specific abdominal adipose tissue and the pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS This retrospective study comprised 560 patients (394 males and 166 females) with pathologically proven ccRCC (467 low- and 93 high-grade). Abdominal CT images were used to assess the adipose tissue in the subcutaneous, visceral, and intermuscular regions. Subcutaneous fat index (SFI), visceral fat index (VFI), intermuscular fat index (IFI), total fat index (TFI), and relative visceral adipose tissue (rVAT) were calculated. Univariate and multivariate logistic regression analyses were performed according to sex to identify the associations between fat-related parameters and pathological grade. RESULTS IFI was significantly higher in high-grade ccRCC patients than in low-grade patients for both men and women. For male patients with high-grade tumors, the SFI, VFI, TFI, and rVAT were significantly lower, but not for female patients. In both univariate and multivariate studies, the IFI continued to be a reliable and independent predictor of high-grade ccRCC, regardless of sex. CONCLUSIONS Intermuscular fat index proved to be a valuable biomarker for the pathological grade of ccRCC and could be used as a reliable independent predictor of high-grade ccRCC for both males and females. CRITICAL RELEVANCE STATEMENT Sex-specific fat adipose tissue can be used as a new biomarker to provide a new dimension for renal tumor-related research and may provide new perspectives for personalized tumor management decision-making approaches. KEY POINTS • There are sex differences in distribution of subcutaneous fat and visceral fat. • The SFI, VFI, TFI, and rVAT were significantly lower in high-grade ccRCC male patients, but not for female patients. • Intermuscular fat index can be used as a reliable independent predictor of high-grade ccRCC for both males and females.
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Affiliation(s)
- Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhouyan Liao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shan Hu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Pickovsky JS, Alo Nasiyabi K, Eldihimi F, Schieda N. Utility of multiparametric renal CT for differentiation of low-grade from high-grade cT1a clear cell renal cell carcinoma. Br J Radiol 2023; 96:20221087. [PMID: 37428147 PMCID: PMC10546453 DOI: 10.1259/bjr.20221087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVE To determine if CT can differentiate low-grade from high-grade clear cell renal cell carcinoma (ccRCC) in cT1a solid ccRCC. METHODS AND MATERIALS This retrospective cross-sectional study evaluated 78 < 4 cm solid (>25% enhancing) ccRCC in 78 patients with renal CT within 12 months of surgery between January 2016 and December 2019. Two radiologists (R1/R2), blinded to pathology, independently recorded mass:size, calcification, attenuation and heterogeneity (5-point Likert scale) and recorded a 5-point ccRCC CT Score. Multivariate logistic regression (LR) was performed. RESULTS There were 64.1% (50/78) low-grade (5/50 Grade 1 and 45/50 Grade 2) and 35.9% (28/78) high-grade (27/28 Grade 3 and 1/28 Grade 4) tumors.Unenhanced CT attenuation was higher (35.9±10.3 R1 and 34.9±10.7 R2 high-grade vs 29.7±10.2 R1 and 29.5±9.8 R2 low-grade, p=0.01-0.02), absolute corticomedullary phase attenuation ratio (CMphase-ratio; 0.67±0.16 R1 and 0.66±0.16 R2 vs 0.93±0.83 R1 and 0.80±0.33 R2, p=0.04-0.05) and 3-tiered stratification of CMphase-ratio (p=0.02) lower in high-grade tumors.A two-variable LR-model including unenhanced CT attenuation and CM.phase-ratio achieved area under the receiver operator characteristic curve of: 73% (95% confidence intervals 59-86%) and 72% (59-84%) for R1 and R2.ccRCC CT score differed by ccRCC grade (p<0.01 R1, R2) with high-grade tumors occurring most commonly in moderately enhancing ccRCC score 4 (46.4% [13/28] R1 and 54% [15/28]). CONCLUSION Among cT1a ccRCC, high-grade tumors have higher unenhanced CT attenuation and are less avidly enhancing. ADVANCES IN KNOWLEDGE High-grade ccRCC have higher attenuation (possibly due to less microscopic fat) and lower corticomedullary phase enhancement compared to low-grade tumors. This may result in categorization of high-grade tumors in lower ccRCC diagnostic algorithm categories.
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Affiliation(s)
- Jana S Pickovsky
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | | | | | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
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16
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Ludwig DR, Thacker Y, Luo C, Narra A, Mintz AJ, Siegel CL. CT-derived textural analysis parameters discriminate high-attenuation renal cysts from solid renal neoplasms. Clin Radiol 2023; 78:e782-e790. [PMID: 37586966 DOI: 10.1016/j.crad.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
AIM To assess the utility of textural features on computed tomography (CT) to differentiate high-attenuation cysts from solid renal neoplasms among indeterminate renal lesions detected incidentally on CT. MATERIALS AND METHODS Patients were included if they had an indeterminate renal lesion on CT that was subsequently characterised on ultrasound or magnetic resonance imaging (MRI). Up to three lesions per patient were included if they had a size ≥10 mm and density of 20-70 HU on unenhanced CT or any single phase of contrast-enhanced CT. Cases were categorised as benign or most likely benign cysts (Bosniak II and IIF) versus indeterminate (Bosniak III), mixed solid and cystic (Bosniak IV), or solid renal lesions. A random forest model was generated using 95 textural parameters and four clinical parameters for each lesion. RESULTS Two hundred and thirty-four patients were included who had a total of 278 lesions. Of these, 193 (69%) were benign or most likely benign cysts and 85 (31%) were indeterminate, mixed cystic and solid, or solid renal lesions. The random forest model had an area under the curve of 0.71 (95% confidence interval [CI]: 0.65, 0.78), with a sensitivity and specificity of 81.2% and 38.9%, respectively. CONCLUSION A multivariate model including textural and clinical parameters had moderate overall performance for discriminating benign or likely benign cysts from indeterminate, mixed solid and cystic, or solid renal lesions. This study serves as a proof of concept and may reduce the need for further follow-up by characterising a significant portion of indeterminate lesions on CT as benign.
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Affiliation(s)
- D R Ludwig
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA.
| | - Y Thacker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C Luo
- Division of Public Health Sciences, Washington University School of Medicine, Saint Louis, MO, USA
| | - A Narra
- St George's University School of Medicine, Grenada, West Indies
| | - A J Mintz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - C L Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
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17
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Russell G, Navale P, Ludwig DR, Strnad BS, Itani M. Peripheral nodular enhancement in adrenal and renal hematomas: A report of 3 cases. Radiol Case Rep 2023; 18:3371-3375. [PMID: 37502475 PMCID: PMC10369399 DOI: 10.1016/j.radcr.2023.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023] Open
Abstract
There are a wide range of benign and malignant pathologies that the radiologist may encounter in the adrenal glands and kidneys, often incidentally when imaging is performed for other indications. Many imaging modalities including CT, MR, and US are often used in an attempt to characterize these lesions. A definitive radiological diagnosis, however, is not always possible. This is at times due to atypical presentations of typical lesions which may be mistaken for more aggressive or concerning pathologic conditions. Adrenal lesions that do not demonstrate characteristic benign imaging features might require surgical excision. Similarly, cystic renal lesions that demonstrate nodular enhancement are concerning for Bosniak IV lesions and require surgical management. We report 3 cases in 3 different patients of incidentally discovered hematomas with peripheral enhancement, 2 involving the adrenal gland and 1 involving the kidney. All 3 of these histologically proven hematomas demonstrated similar radiological manifestations of peripheral nodular progressive enhancement, mimicking neoplastic conditions, and necessitating surgical removal.
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Affiliation(s)
- Gentry Russell
- Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, Washington University School of Medicine, St Louis, MO, USA
| | - Pooja Navale
- Department of Pathology, Washington University School of Medicine, St Louis, MO, USA
| | - Daniel R. Ludwig
- Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin S. Strnad
- Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, Washington University School of Medicine, St Louis, MO, USA
| | - Malak Itani
- Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, Washington University School of Medicine, St Louis, MO, USA
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Schawkat K, Krajewski KM. Insights into Renal Cell Carcinoma with Novel Imaging Approaches. Hematol Oncol Clin North Am 2023; 37:863-875. [PMID: 37302934 DOI: 10.1016/j.hoc.2023.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.
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Affiliation(s)
- Khoschy Schawkat
- Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School
| | - Katherine M Krajewski
- Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School; Dana-Farber Cancer Institute, 440 Brookline Avenue, Building MA Floor L1 Room 04AC, Boston, MA 02215, USA.
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19
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Pedrosa I. Invited Commentary: MRI Clear Cell Likelihood Score for Indeterminate Solid Renal Masses: Is There a Path for Broad Clinical Adoption? Radiographics 2023; 43:e230042. [PMID: 37319027 PMCID: PMC10323227 DOI: 10.1148/rg.230042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 03/14/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Ivan Pedrosa
- From the Department of Radiology, University of Texas Southwestern
Medical Center, 2201 Inwood Rd, 2nd Floor, Ste 202, Dallas, TX 75390-9085
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20
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Shetty AS, Fraum TJ, Ballard DH, Hoegger MJ, Itani M, Rajput MZ, Lanier MH, Cusworth BM, Mehrsheikh AL, Cabrera-Lebron JA, Chu J, Cunningham CR, Hirschi RS, Mokkarala M, Unteriner JG, Kim EH, Siegel CL, Ludwig DR. Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide. Radiographics 2023; 43:e220209. [PMID: 37319026 DOI: 10.1148/rg.220209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.
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Affiliation(s)
- Anup S Shetty
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Tyler J Fraum
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - David H Ballard
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mark J Hoegger
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Malak Itani
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mohamed Z Rajput
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Michael H Lanier
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Brian M Cusworth
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Amanda L Mehrsheikh
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Jorge A Cabrera-Lebron
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Jia Chu
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Christopher R Cunningham
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Ryan S Hirschi
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mahati Mokkarala
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Jackson G Unteriner
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Eric H Kim
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Cary L Siegel
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Daniel R Ludwig
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
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21
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Sun J, Xing Z, Pan L, Wang Q, Xing W, Chen J. Using the "2 standard deviations" rule with Dixon MRI to differentiate renal cell carcinoma types. Clin Imaging 2023; 101:113-120. [PMID: 37329638 DOI: 10.1016/j.clinimag.2023.06.011] [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: 01/22/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Clear cell and non-clear cell renal cell carcinoma (RCC) are distinguishable based on microscopic fat, detectable by chemical shift MRI. However, these assessments are often subjective. Conversely, Dixon MRIs and the "2 standard deviations" rule (2SDR) are quantitative methods that may decrease diagnostic subjectivity. Therefore, this study assessed the value of the 2SDR for detecting microscopic fat and thus differentiating clear cell and non-clear cell RCC using Dixon MRI. METHODS This retrospective study included patients with RCC who underwent preoperative Dixon MRI. The patients were grouped based on tumor type: clear cell RCC and non-clear cell RCC. The 2SDR value was calculated based on in-phase and opposed-phase images and then compared between the two groups. 2SDR values >0 indicated clear cell RCCs, whereas values <0 indicated non-clear cell RCC. RESULTS We included 151 patients; 114 patients had clear cell RCC, of which 106 had a 2SDR value >0. Furthermore, 37 patients had non-clear cell RCC, of which 3 had a 2SDR value >0. The 2SDR value was significantly higher in the clear cell RCC group than in the non-clear cell RCC group (p = 0.000). Overall, 93.0% (106/114) and 8.1% (3/37) of patients with clear cell and non-clear cell RCC, respectively, had microscopic fat. The evaluation indices for this 2SDR method were: accuracy: 92.72%, sensitivity: 92.98%, specificity: 91.89%, positive predictive value: 97.25%, and negative predictive value: 80.95%. CONCLUSIONS 2SDR values calculated from Dixon magnetic resonance images can differentiate clear cell from non-clear cell RCCs by detecting microscopic fat. PRECIS The "2 standard deviations" rule value calculated from Dixon MR images differentiates clear cell from non-clear cell renal cell carcinoma with high efficiency by detecting microscopic fat.
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Affiliation(s)
- Jun Sun
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China
| | - Zhaoyu Xing
- Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China
| | - Liang Pan
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China
| | - Qing Wang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China
| | - Wei Xing
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China.
| | - Jie Chen
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China.
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22
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Chung A, Raman SS. Radiologist's Disease: Imaging for Renal Cancer. Urol Clin North Am 2023; 50:161-180. [PMID: 36948664 DOI: 10.1016/j.ucl.2023.01.006] [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: 03/22/2023]
Abstract
There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.
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Affiliation(s)
- Alex Chung
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Steven S Raman
- David Geffen School of Medicine at UCLA, 757 Westwood Bl, RRMC, Los Angeles, CA, USA.
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23
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Frank RA, Dawit H, Bossuyt PMM, Leeflang M, Flood TA, Breau RH, McInnes MDF, Schieda N. Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta-analysis. J Magn Reson Imaging 2023; 57:1172-1184. [PMID: 36054467 DOI: 10.1002/jmri.28397] [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: 06/13/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Biparametric (bp)-MRI and multiparametric (mp)-MRI may improve the diagnostic accuracy of renal mass histology. PURPOSE To evaluate the available evidence on the diagnostic accuracy of bp-MRI and mp-MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology. STUDY TYPE Systematic review. POPULATION MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched. FIELD STRENGTH/SEQUENCE 1.5 or 3 Tesla. ASSESSMENT Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion-weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS-2. STATISTICAL TESTS Meta-analysis using a bivariate logitnormal random effects model. RESULTS We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%-99%), and specificity was 63% (95% CI: 46%-77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%-90%) and specificity was 77% (95% CI: 73%-81%). DATA CONCLUSION Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Robert A Frank
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Haben Dawit
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.,School of Epidemiology, Public Health and Preventative Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Patrick M M Bossuyt
- Amsterdam University Medical Centers, University of Amsterdam, Epidemiology and Data Science, Amsterdam, the Netherlands
| | - Mariska Leeflang
- Amsterdam University Medical Centers, University of Amsterdam, Epidemiology and Data Science, Amsterdam, the Netherlands
| | - Trevor A Flood
- Department of Anatomical Pathology, University of Ottawa, Ottawa, Canada
| | - Rodney H Breau
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.,Department of Surgery, University of Ottawa, Ottawa, Canada
| | - Matthew D F McInnes
- Department of Radiology, University of Ottawa, Ottawa, Canada.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nicola Schieda
- Department of Radiology, University of Ottawa, Ottawa, Canada.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
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24
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Ibrahim A, Pelsser V, Anidjar M, Kaitoukov Y, Camlioglu E, Moosavi B. Performance of clear cell likelihood scores in characterizing solid renal masses at multiparametric MRI: an external validation study. Abdom Radiol (NY) 2023; 48:1033-1043. [PMID: 36639532 DOI: 10.1007/s00261-023-03799-z] [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: 09/15/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/15/2023]
Abstract
PURPOSE The purpose of this study is to evaluate the accuracy and interobserver agreement of ccLS in diagnosing clear cell renal cell carcinoma (ccRCC). METHODS This retrospective single-center study evaluated consecutive patients with solid renal masses who underwent mpMRI followed by percutaneous biopsy and/or surgical excision between January 2010 and December 2020. Predominantly (> 75%) cystic masses, masses with macroscopic fat and infiltrative masses were excluded. Two abdominal radiologists independently scored each renal mass according to the proposed ccLS algorithm. The diagnostic performance of ccLS categories for ccRCC was calculated using logistic regression modeling. Diagnostic accuracy for predicting ccRCC was calculated using 2 × 2 contingency tables. Interobserver agreement for ccLS was evaluated with Cohen's k statistic. RESULTS A total of 79 patients (mean age, 63 years ± 12 [SD], 50 men) with 81 renal masses were evaluated. The mean size was 36 mm ± 28 (range 10-160). Of the renal masses included, 44% (36/81) were ccRCC. The area under the receiver operating characteristic curve was 0.87 (95% CI 0.79-0.95). Using ccLS ≥ 4 to diagnose ccRCC, the sensitivity, specificity, and positive predictive value were 93% (95% CI 79, 99), 63% (95% CI 48, 77), and 67% (95% CI 58, 75), respectively. The negative predictive value of ccLS ≤ 2 was 93% (95% CI 64, 99). The proportion of ccRCC by ccLS category 1 to 5 were 10%, 0%, 10%, 57%, and 84%, respectively. Interobserver agreement was moderate (k = 0.47). CONCLUSION In this study, clear cell likelihood score had moderate interobserver agreement and resulted in 96% negative predictive value in excluding ccRCC.
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Affiliation(s)
- Aisin Ibrahim
- Department of Radiology, McGill University Health Center, McGill University, 1650 Cedar Avenue, Montreal, QC, Canada
| | - Vincent Pelsser
- Department of Radiology, Jewish General Hospital, McGill University, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada
| | - Maurice Anidjar
- Department of Urology, Jewish General Hospital, McGill University, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada
| | - Youri Kaitoukov
- Department of Radiology, Jewish General Hospital, McGill University, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada
| | - Errol Camlioglu
- Department of Radiology, Jewish General Hospital, McGill University, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada
| | - Bardia Moosavi
- Department of Radiology, Jewish General Hospital, McGill University, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, QC, H3T 1E2, Canada.
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Kumar S, Virarkar M, Vulasala SSR, Daoud T, Ozdemir S, Wieseler C, Vincety-Latorre F, Gopireddy DR, Bhosale P, Lall C. Magnetic Resonance Imaging Virtual Biopsy of Common Solid Renal Masses-A Pictorial Review. J Comput Assist Tomogr 2023; 47:186-198. [PMID: 36790908 DOI: 10.1097/rct.0000000000001424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
ABSTRACT The expanded application of radiologic imaging resulted in an increased incidence of renal masses in the recent decade. Clinically, it is difficult to determine the malignant potential of the renal masses, thus resulting in complex management. Image-guided biopsies are the ongoing standard of care to identify molecular variance but are limited by tumor accessibility and heterogeneity. With the evolving importance of individualized cancer therapies, radiomics has displayed promising results in the identification of tumoral mutation status on routine imaging. This article discusses how magnetic resonance imaging features can guide a radiologist toward identifying renal mass characteristics.
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Affiliation(s)
- Sindhu Kumar
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Mayur Virarkar
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Sai Swarupa R Vulasala
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Taher Daoud
- Division of Diagnostic Imaging, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Savas Ozdemir
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Carissa Wieseler
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | | | - Dheeraj R Gopireddy
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Priya Bhosale
- Division of Diagnostic Imaging, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Chandana Lall
- From the Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
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26
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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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27
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Dunn M, Linehan V, Clarke SE, Keough V, Nelson R, Costa AF. Diagnostic Performance and Interreader Agreement of the MRI Clear Cell Likelihood Score for Characterization of cT1a and cT1b Solid Renal Masses: An External Validation Study. AJR Am J Roentgenol 2022; 219:793-803. [PMID: 35642765 DOI: 10.2214/ajr.22.27378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND. The clear cell likelihood score (ccLS) has been proposed for the noninvasive differentiation of clear cell renal cell carcinoma (ccRCC) from other renal neoplasms on multiparametric MRI (mpMRI), though further external validation remains needed. OBJECTIVE. The purpose of our study was to evaluate the diagnostic performance and interreader agreement of the ccLS version 2.0 (v2.0) for characterizing solid renal masses as ccRCC. METHODS. This retrospective study included 102 patients (67 men, 35 women; mean age, 56.9 ± 12.8 [SD] years) who underwent mpMRI between January 2013 and February 2018, showing a total of 108 (≥ 25% enhancing tissue) solid renal masses measuring 7 cm or smaller (83 cT1a [≤ 4 cm] and 25 cT1b [> 4 cm and ≤ 7 cm]), all with a histologic diagnosis. Three abdominal radiologists independently reviewed the MRI examinations using ccLS v2.0. Median reader sensitivity, specificity, and accuracy were computed for predicting ccRCC by ccLS of 4 or greater, and individual reader AUCs were derived. The percentage of masses that were ccRCC was calculated, stratified by ccLS. Interobserver agreement was assessed by the Fleiss kappa statistic. RESULTS. The sample included 45 ccRCCs (34 cT1a, 11 cT1b), 30 papillary renal cell carcinomas (RCCs), 13 chromophobe RCCs, 14 oncocytomas, and six fat-poor angiomyolipomas. Median reader sensitivity, specificity, and accuracy for predicting ccRCC by ccLS of 4 or greater were 85%, 82%, and 83% among cT1a masses and 82%, 100%, and 92% among cT1b masses. The three readers' AUCs for predicting ccRCC by ccLS for cT1a masses were 0.90, 0.84, and 0.89 and for cT1b masses were 0.99, 0.97, and 0.92. Across readers, the percentage of masses that were ccRCC among cT1a masses was 0%, 0%, 20%, 68%, and 93% for ccLS of 1, 2, 3, 4, and 5, respectively; among cT1b masses, the percentage of masses that were ccRCC was 0%, 0%, 32%, 90%, and 100% for ccLS of 1, 2, 3, 4, and 5, respectively. Interobserver agreement among cT1a and cT1b masses for ccLS of 4 or greater was 0.82 and 0.83 and for ccLS of 1-5 overall was 0.65 and 0.62, respectively. CONCLUSION. This study provides external validation of the ccLS, finding overall high measures of diagnostic performance and interreader agreement. CLINICAL IMPACT. The ccLS provides a standardized approach to the noninvasive diagnosis of ccRCC by MRI.
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Affiliation(s)
- Marshall Dunn
- Department of Diagnostic Radiology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, 1276 S Park St, Victoria Bldg, Rm 307, Halifax, NS B3H 2Y9, Canada
| | - Victoria Linehan
- Department of Diagnostic Radiology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, 1276 S Park St, Victoria Bldg, Rm 307, Halifax, NS B3H 2Y9, Canada
| | - Sharon E Clarke
- Department of Diagnostic Radiology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, 1276 S Park St, Victoria Bldg, Rm 307, Halifax, NS B3H 2Y9, Canada
| | - Valerie Keough
- Department of Diagnostic Radiology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, 1276 S Park St, Victoria Bldg, Rm 307, Halifax, NS B3H 2Y9, Canada
| | - Ralph Nelson
- Department of Diagnostic Radiology, McGill University Health Centre, Montreal General Hospital Site, Montreal, QC, Canada
| | - Andreu F Costa
- Department of Diagnostic Radiology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, 1276 S Park St, Victoria Bldg, Rm 307, Halifax, NS B3H 2Y9, Canada
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Editorial Comment: Toward a CT Equivalent of the MRI Clear Cell Likelihood Score. AJR Am J Roentgenol 2022; 219:824. [PMID: 35766536 DOI: 10.2214/ajr.22.28118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Development of a Multiparametric Renal CT Algorithm for Diagnosis of Clear Cell Renal Cell Carcinoma Among Small (≤ 4 cm) Solid Renal Masses. AJR Am J Roentgenol 2022; 219:814-823. [PMID: 35766532 DOI: 10.2214/ajr.22.27971] [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: 12/13/2022]
Abstract
BACKGROUND. The MRI clear cell likelihood score predicts the likelihood that a renal mass is clear cell renal cell carcinoma (ccRCC). A CT-based algorithm has not yet been established. OBJECTIVE. The purpose of our study was to develop and evaluate a CT-based algorithm for diagnosing ccRCC among small (≤ 4 cm) solid renal masses. METHODS. This retrospective study included 148 patients (73 men, 75 women; mean age, 58 ± 12 [SD] years) with 148 small (≤ 4 cm) solid (> 25% enhancing tissue) renal masses that underwent renal mass CT (unenhanced, corticomedullary, and nephrographic phases) before resection between January 2016 and December 2019. Two radiologists independently evaluated CT examinations and recorded calcification, mass attenuation in all phases, mass-to-cortex corticomedullary attenuation ratio, and heterogeneity score (score on a 5-point Likert scale, assessed in corticomedullary phase). Features associated with ccRCC were identified by multivariable logistic regression analysis and then used to create a five-tiered CT score for diagnosing ccRCC. RESULTS. The masses comprised 53% (78/148) ccRCC and 47% (70/148) other histologic diagnoses. The mass-to-cortex corticomedullary attenuation ratio was higher for ccRCC than for other diagnoses (reader 1: 0.84 ± 0.68 vs 0.68 ± 0.65, p = .02; reader 2: 0.75 ± 0.29 vs 0.59 ± 0.25, p = .02). The heterogeneity score was higher for ccRCC than other diagnoses (reader 1: 4.0 ± 1.1 vs 1.5 ± 1.6, p < .001; reader 2: 4.4 ± 0.9 vs 3.3 ± 1.5, p < .001). Other features showed no difference. A five-tiered diagnostic algorithm including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score had interobserver agreement of 0.71 (weighted κ) and achieved an AUC for diagnosing ccRCC of 0.75 (95% CI, 0.68-0.82) for reader 1 and 0.72 (95% CI, 0.66-0.82) for reader 2. A CT score of 4 or greater achieved sensitivity, specificity, and PPV of 71% (95% CI, 59-80%), 79% (95% CI, 67-87%), and 79% (95% CI, 67-87%) for reader 1 and 42% (95% CI, 31-54%), 81% (95% CI, 70-90%), and 72% (95% CI, 56-84%) for reader 2. A CT score of 2 or less had NPV of 85% (95% CI, 69-95%) for reader 1 and 88% (95% CI, 69-97%) for reader 2. CONCLUSION. A five-tiered renal CT algorithm, including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score, had substantial interobserver agreement, moderate AUC and PPV, and high NPV for diagnosing ccRCC. CLINICAL IMPACT. The CT algorithm, if validated, may represent a useful clinical tool for diagnosing ccRCC.
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Tian J, Teng F, Xu H, Zhang D, Chi Y, Zhang H. Systematic review and meta-analysis of multiparametric MRI clear cell likelihood scores for classification of small renal masses. Front Oncol 2022; 12:1004502. [DOI: 10.3389/fonc.2022.1004502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/11/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo systematically assess the multiparametric MRI clear cell likelihood score (ccLS) algorithm for the classification of small renal masses (SRM).MethodsWe conducted an electronic literature search on Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify relevant articles from 2017 up to June 30, 2022. We included studies reporting the diagnostic performance of the ccLS for characterization of solid SRM. The bivariate model and hierarchical summary receiver operating characteristic (HSROC) model were used to pool sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR). The quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.ResultsA total of 6 studies with 825 renal masses (785 patients) were included in the current meta-analysis. The pooled sensitivity and specificity for cT1a renal masses were 0.80 (95% CI 0.75–0.85) and 0.74 (95% CI 0.65–0.81) at the threshold of ccLS ≥4, the pooled LR+, LR−, and DOR were 3.04 (95% CI 2.34-3.95), 0.27 (95% CI 0.22–0.33), and 11.4 (95% CI 8.2-15.9), respectively. The area under the HSROC curve was 0.84 (95% CI 0.81–0.87). For all cT1 renal masses, the pooled sensitivity and specificity were 0.80 (95% CI 0.74–0.85) and 0.76 (95% CI 0.67–0.83).ConclusionsThe ccLS had moderate to high accuracy for identifying ccRCC from other RCC subtypes and with a moderate inter-reader agreement. However, its diagnostic performance remain needs multi-center, large cohort studies to validate in the future.
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Shetty AS, Fraum TJ, Ludwig DR, Hoegger MJ, Zulfiqar M, Ballard DH, Strnad BS, Rajput MZ, Itani M, Salari R, Lanier MH, Mellnick VM. Body MRI: Imaging Protocols, Techniques, and Lessons Learned. Radiographics 2022; 42:2054-2074. [DOI: 10.1148/rg.220025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Anup S. Shetty
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Tyler J. Fraum
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Daniel R. Ludwig
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mark J. Hoegger
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Maria Zulfiqar
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - David H. Ballard
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Benjamin S. Strnad
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mohamed Z. Rajput
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Malak Itani
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Reza Salari
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Michael H. Lanier
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Vincent M. Mellnick
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
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Acquired cystic disease subtype renal cell carcinoma (ACD-RCC): prevalence and imaging features at a single institution. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:2858-2866. [PMID: 35674787 DOI: 10.1007/s00261-022-03566-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE Acquired cystic kidney disease (ACKD) is commonly seen in patients with end-stage renal disease (ESRD), and patients with ACKD have an increased risk of renal cell carcinoma (RCC). Acquired cystic disease-associated RCC (ACD-RCC) was incorporated into the 2016 World Health Organization Classification. This study aims to describe the imaging features of ACD-RCC, which are not well reported previously. METHODS Retrospective review of patients with ACKD who underwent total nephrectomy for concern of a renal mass between 2016 and 2021 yielded 122 nephrectomies in 107 patients. Pathology reports were searched for type and subtype of mass. In ACD-RCC subtypes, imaging studies were evaluated for modality and contrast enhancement (CE). Imaging findings assessed included cystic/solid nature, unenhanced CT (NECT) attenuation, enhancement characteristics [non-enhancing (< 10 HU difference), equivocal (10-20 HU), enhancing (> 20 HU)], subjective MRI enhancement, T1 and T2 signal intensity, restricted diffusion, ultrasound (US) echogenicity, and subjective CEUS enhancement. RESULTS 148 masses were identified, 122 (82%) of which were malignant and 26 (18%) benign. The three most common tumors were clear cell RCC (n = 47), papillary RCC (n = 35), and ACD-RCC (n = 21). Of the 21 cases of ACD-RCC, 16 had preoperative imaging: CT (15: 6 NECT only, 2 CECT only, 7 combined NECT and CECT), MRI (4), CEUS (5). Ten of these tumors were solid/mostly solid and 6 mixed cystic/solid. On NECT, the average attenuation was 35 HU (range 13-52). Of those with multiphasic CTs, 1 was non-enhancing, 3 were equivocal, and 3 enhanced. All 3 masses imaged with CE-MRI showed enhancement. All 4 tumors evaluated by MRI demonstrated T2 hypointensity and restricted diffusion. All five masses enhanced on CEUS. CONCLUSION ACD-RCC subtype was the third most common renal neoplasm in ACKD patients. Our findings found that no single imaging feature is pathognomonic for ACD-RCC. However, ACD-RCCs are typically solid masses with most demonstrating equivocal or mild enhancement on CT. T2 hypointensity and restricted diffusion were the most common MRI features.
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Roussel E, Capitanio U, Kutikov A, Oosterwijk E, Pedrosa I, Rowe SP, Gorin MA. Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review. Eur Urol 2022; 81:476-488. [PMID: 35216855 PMCID: PMC9844544 DOI: 10.1016/j.eururo.2022.01.040] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/08/2022] [Accepted: 01/21/2022] [Indexed: 01/19/2023]
Abstract
CONTEXT The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management. OBJECTIVE To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization. EVIDENCE ACQUISITION The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr. EVIDENCE SYNTHESIS Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice. CONCLUSIONS A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis. PATIENT SUMMARY Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.
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Affiliation(s)
- Eduard Roussel
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Umberto Capitanio
- Department of Urology, University Vita-Salute, San Raffaele Scientific Institute, Milan, Italy; Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alexander Kutikov
- Division of Urology, Department of Surgery, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
| | - Egbert Oosterwijk
- Department of Urology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences (RIMLS), Nijmegen, The Netherlands
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Advanced Imaging Research Center. University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael A Gorin
- Urology Associates and UPMC Western Maryland, Cumberland, MD, USA; Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Mileto A, Potretzke TA. Standardized Evaluation of Small Renal Masses Using the MRI Clear Cell Likelihood Score. Radiology 2022; 303:600-602. [PMID: 35289666 DOI: 10.1148/radiol.220054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Achille Mileto
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
| | - Theodora A Potretzke
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905
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