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Bodard S, Delavaud C, Dariane C, Boudhabhay I, Bensenouci NEI, Timsit MO, Correas JM, Verkarre V, Hélénon O. Low-grade oncocytic tumor of the kidney: imaging features of a novel tumor entity. Abdom Radiol (NY) 2024:10.1007/s00261-024-04487-2. [PMID: 39068611 DOI: 10.1007/s00261-024-04487-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 06/30/2024] [Accepted: 06/30/2024] [Indexed: 07/30/2024]
Abstract
PURPOSES Low-grade oncocytic tumor (LOT) is a rare renal tumor that has emerged from the spectrum of eosinophilic/oncocytic renal tumors and poses a diagnostic challenge due to its similarity to chromophobe renal cell carcinoma (CHRCC) and renal oncocytoma (RO). The imaging features of this novel tumor entity have not yet been clearly described. The purpose of this study was to describe the imaging features of LOT with radiologic-pathologic correlation. METHODS We conducted a retrospective observational study involving two expert centers. We identified 12 pathologically proven LOT with preoperative imaging available, including at least computed tomography (CT) or magnetic resonance imaging (MRI), from the past 12 years. Three experienced radiologists performed the imaging analysis independently. RESULTS All tumors presented well-defined borders. Nine of the 12 LOT exhibited an early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape. Three showed a homogeneous contrast enhancement. Macroscopic fat and calcifications were not observed in any of the tumors. CONCLUSION Early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape suggests a LOT diagnosis. Further analyses involving larger studies are needed to fully confirm these imaging characteristics. To date, a percutaneous biopsy should be performed before considering management.
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Affiliation(s)
- Sylvain Bodard
- Adult Department of Radiology, Service d'Imagerie Adulte, AP-HP-Centre, Hôpital Necker Enfants Malades, Université de Paris Cité, 149 Rue de Sèvres, 75015, Paris, France.
- Laboratoire d'Imagerie Biomédicale, Sorbonne Université, CNRS, INSERM, Paris, France.
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
| | - Christophe Delavaud
- Adult Department of Radiology, Service d'Imagerie Adulte, AP-HP-Centre, Hôpital Necker Enfants Malades, Université de Paris Cité, 149 Rue de Sèvres, 75015, Paris, France
| | - Charles Dariane
- Service d'Urologie, AP-HP-Centre, Hôpital Européen Georges Pompidou, Université de Paris Cité, 75015, Paris, France
| | - Idris Boudhabhay
- Service de Transplantation Rénale, AP-HP-Centre, Hôpital Necker Enfants Malades, Université de Paris Cité, 75015, Paris, France
| | - Nour El Imane Bensenouci
- Service d'Anatomie Pathologie, AP-HP-Centre, Hôpital Européen Georges Pompidou, Université de Paris Cité, 75015, Paris, France
| | - Marc-Olivier Timsit
- Service d'Urologie, AP-HP-Centre, Hôpital Européen Georges Pompidou, Université de Paris Cité, 75015, Paris, France
| | - Jean-Michel Correas
- Adult Department of Radiology, Service d'Imagerie Adulte, AP-HP-Centre, Hôpital Necker Enfants Malades, Université de Paris Cité, 149 Rue de Sèvres, 75015, Paris, France
- Laboratoire d'Imagerie Biomédicale, Sorbonne Université, CNRS, INSERM, Paris, France
| | - Virginie Verkarre
- Service d'Anatomie Pathologie, AP-HP-Centre, Hôpital Européen Georges Pompidou, Université de Paris Cité, 75015, Paris, France
- Equipe INSERM UMR 970 "Genetic and Metabolism of Rare Tumors" Equipe Labélisée Ligue Contre Le Cancer, PARCC, SIRIC CARPEM, Université de Paris-Cité, Paris, France
| | - Olivier Hélénon
- Adult Department of Radiology, Service d'Imagerie Adulte, AP-HP-Centre, Hôpital Necker Enfants Malades, Université de Paris Cité, 149 Rue de Sèvres, 75015, Paris, France
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Chai JL, Siegmund SE, Hirsch MS, Silverman SG. Low-grade oncocytic tumor: a review of radiologic and clinical features. Abdom Radiol (NY) 2024; 49:1940-1948. [PMID: 38372764 DOI: 10.1007/s00261-023-04167-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/09/2023] [Accepted: 12/16/2023] [Indexed: 02/20/2024]
Abstract
PURPOSE The 2022 World Health Organization classification of renal neoplasia expanded the spectrum of oncocytic neoplasms to encompass newly established and emerging entities; one of the latter is the low-grade oncocytic tumor (LOT). This study reports the radiologic appearance and clinical behavior of LOT. METHODS In this IRB-approved, HIPPA-compliant retrospective study, our institution's pathology database was searched for low-grade oncocytic tumors or neoplasms. Patient age, gender, and comorbidities were obtained from a review of electronic medical records, and imaging characteristics of the tumors were assessed through an imaging platform. RESULTS The pathology database search yielded 14 tumors in 14 patients. Four patients were excluded, as radiologic images were not available in three, and one did not fulfill diagnostic criteria after pathology re-review. The resulting cohort consisted of 10 tumors (median diameter 2.3 cm, range 0.7-5.1) in 10 patients (median age 68 years, range 53-91, six women). All tumors presented as a solitary, well-circumscribed, mass with solid components. All enhanced as much or almost as much as adjacent renal parenchyma; all but one enhanced heterogeneously. None had lymphadenopathy, venous invasion, or metastatic disease at presentation or at clinical follow-up (median, 22.2 months, range 3.4-71.6). Among five tumors undergoing active surveillance, mean increase in size was 0.4 cm/year at imaging follow-up (median 16.7 months, range 8.9-25.4). CONCLUSION LOT, a recently described pathologic entity in the kidney, can be considered in the differential diagnosis of an avidly and typically heterogeneously enhancing solid renal mass in an adult patient.
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Affiliation(s)
- Jessie L Chai
- Division of Abdominal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA.
| | | | - Michelle S Hirsch
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Stuart G Silverman
- Division of Abdominal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
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Uhlig A, Uhlig J, Leha A, Biggemann L, Bachanek S, Stöckle M, Reichert M, Lotz J, Zeuschner P, Maßmann A. Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting. Eur Radiol 2024:10.1007/s00330-024-10731-6. [PMID: 38634876 DOI: 10.1007/s00330-024-10731-6] [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: 12/10/2023] [Revised: 02/14/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). MATERIAL AND METHODS Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center. RESULTS The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify. CLINICAL RELEVANCE STATEMENT Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.
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Affiliation(s)
- Annemarie Uhlig
- Department of Urology, University Medical Center Goettingen, Goettingen, Germany.
| | - Johannes Uhlig
- Department of Clinical and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany
| | - Andreas Leha
- Department of Medical Statistics, University Medical Center Goettingen, Goettingen, Germany
| | - Lorenz Biggemann
- Department of Clinical and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany
| | - Sophie Bachanek
- Department of Clinical and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany
| | - Michael Stöckle
- Department of Urology and Pediatric Urology, Saarland University, Homburg, Germany
| | - Mathias Reichert
- Department of Urology, University Medical Center Goettingen, Goettingen, Germany
| | - Joachim Lotz
- Department of Cardiac Imaging, University Medical Center Goettingen, Goettingen, Germany
| | - Philip Zeuschner
- Department of Urology and Pediatric Urology, Saarland University, Homburg, Germany
| | - Alexander Maßmann
- Department of Radiology and Nuclear Medicine, Robert-Bosch-Clinic, Stuttgart, Germany
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Gao Y, Wang X, Zhao X, Zhu C, Li C, Li J, Wu X. Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma. BMC Cancer 2023; 23:953. [PMID: 37814228 PMCID: PMC10561466 DOI: 10.1186/s12885-023-11454-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. METHODS A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. RESULTS The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. CONCLUSION The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.
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Affiliation(s)
- Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xia Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xiaoying Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai, 210000, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
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Sha Z, Song Y, Wu Y, Sha P, Ye C, Fan G, Gao S, Yu R. The value of texture analysis in peritumoral edema of differentiating diagnosis between glioblastoma and primary brain lymphoma. Br J Neurosurg 2023; 37:1074-1077. [PMID: 33307833 DOI: 10.1080/02688697.2020.1856783] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 11/24/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To evaluate the value of texture analysis of routine MRI image in peritumoral edema of differentiating diagnosis between glioblastoma (GBM) and primary brain lymphoma (PBL). METHODS The MRI imaging data of 22 patients with glioblastoma and 21 patients with PBL who were hospitalized in our hospital from January 2010 to October 2018 were selected. All the patients were pathologically diagnosed as glioblastoma or PBL, and MRI plain scan and enhanced examination were performed before operation. FireVoxel software was used to delineate the region of interest (ROI) on the most obvious level of peritumoral edema based on T1WI enhancement. Texture parameters were extracted and compared between glioblastoma and PBL. RESULTS In the glioblastoma group, the inhomogeneity, kurtosis and entropy texture parameters were statistically different from those in the PBL group. The entropy parameter area under the curve (AUC) (0.903) was significantly better than the kurtosis parameter AUC (0.859) and the inhomogeneity parameter AUC (0.729). When the entropy parameter Cut-off point = 3.883, the sensitivity, specificity and accuracy of glioblastoma and PBL were 85.7, 86.4 and 86.0%, respectively, by differential diagnosis. CONCLUSION Texture analysis of tumor peritumoral edema provided quantifiable information, which might be a new method for differentiating glioblastoma from PBL.
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Affiliation(s)
- Zhuang Sha
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yunnong Song
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Yihao Wu
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Pei Sha
- Department of Orthopedic Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Chengkun Ye
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Guangwei Fan
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shangfeng Gao
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Rutong Yu
- Institute of Nervous System Diseases, Xuzhou Medical University, Xuzhou, China
- Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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Klontzas ME, Koltsakis E, Kalarakis G, Trpkov K, Papathomas T, Sun N, Walch A, Karantanas AH, Tzortzakakis A. A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia. Sci Rep 2023; 13:12594. [PMID: 37537362 PMCID: PMC10400617 DOI: 10.1038/s41598-023-39809-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Heraklion, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Crete, Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Emmanouil Koltsakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Stockholm, Sweden
| | - Georgios Kalarakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Diagnostic Radiology, Karolinska University Hospital, Huddinge, Stockholm, Sweden
- University of Crete, School of Medicine, 71500, Heraklion, Greece
| | - Kiril Trpkov
- Department of Pathology and Laboratory Medicine, Alberta Precision Labs, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Department of Clinical Pathology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Na Sun
- Research Unit Analytical Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Heraklion, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Crete, Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden.
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Huddinge, C2:74, 14 186, Stockholm, Sweden.
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Anush A, Rohini G, Nicola S, WalaaEldin EM, Eranga U. Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images. J Med Imaging (Bellingham) 2023; 10:024501. [PMID: 36950139 PMCID: PMC10026851 DOI: 10.1117/1.jmi.10.2.024501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 02/22/2023] [Indexed: 03/24/2023] Open
Abstract
Purpose Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI). Approach In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation. Results The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively. Conclusions We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.
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Affiliation(s)
- Agarwal Anush
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| | - Gaikar Rohini
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
| | - Schieda Nicola
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | | | - Ukwatta Eranga
- University of Guelph, School of Engineering, Guelph, Ontario, Canada
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Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects. Int J Mol Sci 2023; 24:ijms24054615. [PMID: 36902045 PMCID: PMC10003020 DOI: 10.3390/ijms24054615] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.
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Dehghani Firouzabadi F, Gopal N, Homayounieh F, Anari PY, Li X, Ball MW, Jones EC, Samimi S, Turkbey E, Malayeri AA. CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis. Clin Imaging 2023; 94:9-17. [PMID: 36459898 PMCID: PMC9812928 DOI: 10.1016/j.clinimag.2022.11.007] [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: 10/04/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma. METHODS From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575). RESULTS After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically. CONCLUSIONS According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.
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Affiliation(s)
| | - Nikhil Gopal
- Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Fatemeh Homayounieh
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Pouria Yazdian Anari
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, USA
| | - Mark W Ball
- Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Safa Samimi
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Ashkan A Malayeri
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
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Qu J, Zhang Q, Song X, Jiang H, Ma H, Li W, Wang X. CT differentiation of the oncocytoma and renal cell carcinoma based on peripheral tumor parenchyma and central hypodense area characterisation. BMC Med Imaging 2023; 23:16. [PMID: 36707788 PMCID: PMC9881251 DOI: 10.1186/s12880-023-00972-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/18/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Although the central scar is an essential imaging characteristic of renal oncocytoma (RO), its utility in distinguishing RO from renal cell carcinoma (RCC) has not been well explored. The study aimed to evaluate whether the combination of CT characteristics of the peripheral tumor parenchyma (PTP) and central hypodense area (CHA) can differentiate typical RO with CHA from RCC. METHODS A total of 132 tumors on the initial dataset were retrospectively evaluated using four-phase CT. The excretory phases were performed more than 20 min after the contrast injection. In corticomedullary phase (CMP) images, all tumors had CHAs. These tumors were categorized into RO (n = 23), clear cell RCC (ccRCC) (n = 85), and non-ccRCC (n = 24) groups. The differences in these qualitative and quantitative CT features of CHA and PTP between ROs and ccRCCs/non-ccRCCs were statistically examined. Logistic regression filters the main factors for separating ROs from ccRCCs/non-ccRCCs. The prediction models omitting and incorporating CHA features were constructed and evaluated, respectively. The effectiveness of the prediction models including CHA characteristics was then confirmed through a validation dataset (8 ROs, 35 ccRCCs, and 10 non-ccRCCs). RESULTS The findings indicate that for differentiating ROs from ccRCCs and non-ccRCCs, prediction models with CHA characteristics surpassed models without CHA, with the corresponding areas under the curve (AUC) being 0.962 and 0.914 versus 0.952 and 0.839 respectively. In the prediction models that included CHA parameters, the relative enhancement ratio (RER) in CMP and enhancement inversion, as well as RER in nephrographic phase and enhancement inversion were the primary drivers for differentiating ROs from ccRCCs and non-ccRCCs, respectively. The prediction models with CHA characteristics had the comparable diagnostic ability on the validation dataset, with respective AUC values of 0.936 and 0.938 for differentiating ROs from ccRCCs and non-ccRCCs. CONCLUSION The prediction models with CHA characteristics can help better differentiate typical ROs from RCCs. When a mass with CHA is discovered, particularly if RO is suspected, EP images with longer delay scanning periods should be acquired to evaluate the enhancement inversion characteristics of CHA.
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Affiliation(s)
- Jianyi Qu
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Qianqian Zhang
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Xinhong Song
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Hong Jiang
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Heng Ma
- grid.410645.20000 0001 0455 0905Yuhuangding Hospital, Qingdao University School of Medicine, Shandong Yantai, China
| | - Wenhua Li
- grid.16821.3c0000 0004 0368 8293Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiaofei Wang
- grid.440653.00000 0000 9588 091XYantaishan Hospital, Binzhou Medical University, Shandong Yantai, China
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11
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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12
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Whole-Lesion CT Texture Analysis as a Quantitative Biomarker for the Identification of Homogeneous Renal Tumors. LIFE (BASEL, SWITZERLAND) 2022; 12:life12122148. [PMID: 36556513 PMCID: PMC9781849 DOI: 10.3390/life12122148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
Renal tumors are very common in the urinary system, and the preoperative differential diagnosis of homogeneous renal tumors remains a challenge. This study aimed to evaluate the feasibility of the whole-lesion CT texture analysis for the identification of homogeneous renal tumors including clear cell renal cell carcinoma (ccRCC), chromophobe RCC (chRCC), and renal oncocytoma (RO). This retrospective study was approved by our local IRB. Contrast-enhanced CT examination was performed in 128 patients and histopathologically confirmed ccRCC, chRCC, and RO. The one-way ANOVA test with Bonferroni corrections was used to compare the differences, and the receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic efficiency. The whole-lesion CT histogram analysis was used to demonstrate significant differences between ccRCC and chRCC in both arterial and venous phases, and the entropy demonstrated excellent performance in discriminating these two types of tumors (AUCs = 0.95, 0.91). The inhomogeneity of ccRCC was significantly higher than that of RO both in arterial and venous phases. The entropy of chRCC was significantly lower than that of RO, and the kurtosis and entropy yielded high sensitivity (91%) and moderate specificity (74%) in the arterial phase. The whole-lesion CT histogram analysis could be useful for the differential diagnosis of homogeneous ccRCC, chRCC, and RO.
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13
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The value of CT features and demographic data in the differential diagnosis of type 2 papillary renal cell carcinoma from fat-poor angiomyolipoma and oncocytoma. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3838-3846. [PMID: 36085376 DOI: 10.1007/s00261-022-03644-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 07/30/2022] [Accepted: 08/01/2022] [Indexed: 01/18/2023]
Abstract
PURPOSES To determine the CT features and demographic data predictive of type 2 papillary renal cell carcinoma (PRCC) that can help distinguish this neoplasm from fat-poor angiomyolipoma (fpAML) and oncocytoma. METHODS Fifty-four patients with type 2 PRCC, 48 with fpAML, and 47 with oncocytoma in the kidney from multiple centers were retrospectively reviewed. The demographic data and CT features of type 2 PRCC were analyzed and compared with those of fpAML and oncocytoma by univariate analysis and multiple logistic regression analysis to determine the predictive factors for differential diagnosis. Then, receiver operating characteristic (ROC) curve analysis was performed to further assess the logistic regression model and set the threshold level values of the numerical parameters. RESULTS Older age (≥ 46.5 years), unenhanced lesion-to-renal cortex attenuation (RLRCA) < 1.21, corticomedullary ratio of lesion to renal cortex net enhancement (RLRCNE) < 0.32, and size ≥ 30.1 mm were independent predictors for distinguishing type 2 PRCC from fpAML (OR 14.155, 8.332, and 57.745, respectively, P < 0.05 for all). The area under the curve (AUC) of the multiple logistic regression model in the ROC curve analysis was 0.970. In the combined evaluation, the four independent predictors had a sensitivity and specificity of 0.896 and 0.889, respectively. A corticomedullary RLRCNE < 0.61, irregular shape, and male sex were independent predictors for the differential diagnosis of type 2 PRCC from oncocytoma (OR 15.714, 12.158, and 6.175, respectively, P < 0.05 for all). In the combined evaluation, the three independent predictors had a sensitivity and specificity of 0.889 and 0.979, respectively. The AUC of the multiple logistic regression model in the ROC curve analysis was 0.964. CONCLUSION The combined application of CT features and demographic data had good ability in distinguishing type 2 PRCC from fpAML and oncocytoma, respectively.
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14
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Renal oncocytoma: a challenging diagnosis. Curr Opin Oncol 2022; 34:243-252. [DOI: 10.1097/cco.0000000000000829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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15
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Small Renal Masses without Gross Fat: What Is the Role of Contrast-Enhanced MDCT? Diagnostics (Basel) 2022; 12:diagnostics12020553. [PMID: 35204643 PMCID: PMC8871355 DOI: 10.3390/diagnostics12020553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/17/2022] Open
Abstract
Increased detection of small renal masses (SRMs) has encouraged research for non-invasive diagnostic tools capable of adequately differentiating malignant vs. benign SRMs and the type of the tumour. Multi-detector computed tomography (MDCT) has been suggested as an alternative to intervention, therefore, it is important to determine both the capabilities and limitations of MDCT for SRM evaluation. In our study, two abdominal radiologists retrospectively blindly assessed MDCT scan images of 98 patients with incidentally detected lipid-poor SRMs that did not present as definitely aggressive lesions on CT. Radiological conclusions were compared to histopathological findings of materials obtained during surgery that were assumed as the gold standard. The probability (odds ratio (OR)) in regression analyses, sensitivity (SE), and specificity (SP) of predetermined SRM characteristics were calculated. Correct differentiation between malignant vs. benign SRMs was detected in 70.4% of cases, with more accurate identification of malignant (73%) in comparison to benign (65.7%) lesions. The radiological conclusions of SRM type matched histopathological findings in 56.1%. Central scarring (OR 10.6, p = 0.001), diameter of lesion (OR 2.4, p = 0.003), and homogeneous accumulation of contrast medium (OR 3.4, p = 0.03) significantly influenced the accuracy of malignant diagnosis. SE and SP of these parameters varied from 20.6% to 91.3% and 22.9% to 74.3%, respectively. In conclusion, MDCT is able to correctly differentiate malignant versus uncharacteristic benign SRMs in more than 2/3 of cases. However, frequency of the correct histopathological SRM type MDCT identification remains low.
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16
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Lyske J, Mathew RP, Hutchinson C, Patel V, Low G. Multimodality imaging review of focal renal lesions. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-020-00391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Focal lesions of the kidney comprise a spectrum of entities that can be broadly classified as malignant tumors, benign tumors, and non-neoplastic lesions. Malignant tumors include renal cell carcinoma subtypes, urothelial carcinoma, lymphoma, post-transplant lymphoproliferative disease, metastases to the kidney, and rare malignant lesions. Benign tumors include angiomyolipoma (fat-rich and fat-poor) and oncocytoma. Non-neoplastic lesions include infective, inflammatory, and vascular entities. Anatomical variants can also mimic focal masses.
Main body of the abstract
A range of imaging modalities are available to facilitate characterization; ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), magnetic resonance (MR) imaging, and positron emission tomography (PET), each with their own strengths and limitations. Renal lesions are being detected with increasing frequency due to escalating imaging volumes. Accurate diagnosis is central to guiding clinical management and determining prognosis. Certain lesions require intervention, whereas others may be managed conservatively or deemed clinically insignificant. Challenging cases often benefit from a multimodality imaging approach combining the morphology, enhancement and metabolic features.
Short conclusion
Knowledge of the relevant clinical details and key imaging features is crucial for accurate characterization and differentiation of renal lesions.
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17
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Li X, Ma Q, Tao C, Liu J, Nie P, Dong C. A CT-based radiomics nomogram for differentiation of small masses (< 4 cm) of renal oncocytoma from clear cell renal cell carcinoma. Abdom Radiol (NY) 2021; 46:5240-5249. [PMID: 34268628 DOI: 10.1007/s00261-021-03213-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Renal oncocytoma (RO) is the most commonly resected benign renal tumor because of misdiagnosis as renal cell carcinoma. This misdiagnosis is generally owing to overlapping imaging features. This study describes the building of a radiomics nomogram based on clinical data and radiomics signature for the preoperative differentiation of RO from clear cell renal cell carcinoma (ccRCC) on tri-phasic contrast-enhanced CT. METHODS A total of 122 patients (85 in training set and 37 in external validation set) with ROs (n = 46) or ccRCCs (n = 76) were enrolled. Patient characteristics and tri-phasic contrast-enhanced CT imaging features were evaluated to build a clinical factors model. A radiomics signature was constructed by extracting radiomics features from tri-phasic contrast-enhanced CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to a multivariate logistic regression analysis. The diagnostic performance of the above three models was evaluated in training and validation sets. RESULTS Central stellate area and perirenal fascia thickening were selected to build the clinical factors model. Eleven radiomics features were combined to construct the radiomics signature. The AUCs of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.960 and 0.898 in the training and validation sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the validation set indicated an overall net benefit over the clinical factors model. CONCLUSION Our radiomics nomogram can effectively predict the preoperative diagnosis of ROs and may therefore be of assistance in sparing unnecessary surgery and tailoring precise therapy. The ROC curves of the clinical model, the radiomics signature and the radiomics nomogram for the validation set. RO = Renal oncocytoma; ccRCC = Clear cell renal cell carcinoma.
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Affiliation(s)
- Xiaoli Li
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qianli Ma
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Cheng Tao
- Department of Research Management and International Cooperation, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jinling Liu
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Pei Nie
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China.
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18
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Jaggi A, Mastrodicasa D, Charville GW, Jeffrey RB, Napel S, Patel B. Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma. J Med Imaging (Bellingham) 2021; 8:054501. [PMID: 34514033 DOI: 10.1117/1.jmi.8.5.054501] [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: 01/08/2021] [Accepted: 08/05/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). Approach: In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, 63 ± 12 years ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. From each RB round, we computed radiomics features and compared the performance of a random forest and AdaBoost binary classifier trained from the features. To control for overfitting, we performed 10 rounds of 70% to 30% train-test splits with feature-selection, cross-validation, and hyperparameter-optimization on each split. We evaluated the performance with test ROC AUC. We tested models on data from the other RB round and compared with the same round testing with the DeLong test. We clustered important features for each round and measured a bootstrapped adjusted Rand index agreement. Results: Our best classifiers achieved an average AUC of 0.71 ± 0.024 . We found no evidence of an effect for RB round ( p = 1 ). We also found no evidence for a decrease in model performance when tested on the other RB round ( p = 0.85 ). Feature clustering produced seven clusters in each RB round with high agreement ( Rand index = 0.981 ± 0.002 , p < 0.00001 ). Conclusions: A consistent radiomic signature can be derived from RBs and could help distinguish oncocytoma and chromophobe RCC.
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Affiliation(s)
- Akshay Jaggi
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Domenico Mastrodicasa
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Gregory W Charville
- Stanford University School of Medicine, Department of Pathology, Stanford, California, United States
| | - R Brooke Jeffrey
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Sandy Napel
- Stanford University School of Medicine, Department of Radiology, Stanford, California, United States
| | - Bhavik Patel
- Mayo Clinic Arizona, Department of Radiology, Phoenix, Arizona, United States.,Arizona State University, Ira A. Fulton School of Engineering, Phoenix, Arizona, United States
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Hines JJ, Eacobacci K, Goyal R. The Incidental Renal Mass- Update on Characterization and Management. Radiol Clin North Am 2021; 59:631-646. [PMID: 34053610 DOI: 10.1016/j.rcl.2021.03.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Renal masses are commonly encountered on cross-sectional imaging examinations performed for nonrenal indications. Although most can be dismissed as benign cysts, a subset will be either indeterminate or suspicious; in many cases, imaging cannot be used to reliably differentiate between benign and malignant masses. On-going research in defining characteristics of common renal masses on advanced imaging shows promise in offering solutions to this issue. A recent update of the Bosniak classification (used to categorize cystic renal masses) was proposed with the goals of decreasing imaging follow-up in likely benign cystic masses, and therefore avoiding unnecessary surgical resection of such masses.
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Affiliation(s)
- John J Hines
- Department of Radiology, Huntington Hospital, Northwell Health, 270 Park Avenue, Huntington, NY 11743, USA.
| | - Katherine Eacobacci
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Boulevard, Hempstead, NY 11549, USA
| | - Riya Goyal
- Department of Radiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Boulevard, Hempstead, NY 11549, USA
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20
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Tsikopoulos I, Papadopoulos DI, Charitopoulos K, Gkekas C. 'Hybrid' oncocytoma: collecting duct (Bellini) carcinoma-the peril from this extremely rare intratumoural coexistence. BMJ Case Rep 2021; 14:e241091. [PMID: 33906887 PMCID: PMC8076927 DOI: 10.1136/bcr-2020-241091] [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] [Accepted: 03/03/2021] [Indexed: 11/03/2022] Open
Abstract
We presented an extremely rare entity of 'hybrid' oncocytoma and collecting duct (Bellini) carcinoma. The intratumoural coexistence of benign and malignant cells may lead to false diagnosis and suboptimal treatment of an aggressive tumour. Diagnosis may be challenging if only based on imaging modalities. Even the established value of targeted renal biopsy may be questioned in such scarce cases. Consequently, active surveillance for small renal tumours shall not considered a widely safe management.
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Affiliation(s)
- Ioannis Tsikopoulos
- Urology Department, 424 General Military Training Hospital, Thessaloniki, Greece
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21
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Yamamoto T, Gulanbar A, Hayashi K, Kohno A, Komai Y, Yonese J, Matsueda K, Inamura K. Is hypervascular papillary renal cell carcinoma present? Abdom Radiol (NY) 2021; 46:1687-1693. [PMID: 33047228 DOI: 10.1007/s00261-020-02809-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 09/21/2020] [Accepted: 09/30/2020] [Indexed: 11/25/2022]
Abstract
PURPOSE We aimed to investigate atypical papillary renal cell carcinoma (PRCC) presenting with early contrast enhancement and late washout and to investigate the correlation between the CT attenuation value of the corticomedullary phase (CMP) of contrast-enhanced CT in PRCCs and the endothelial cell counts of these tumors. METHODS Twenty-two patients with pathologically confirmed PRCC were enrolled in this study. PRCCs were categorized into 18 typical PRCCs and 4 atypical PRCCs. The CT attenuation value of the lesion in the CMP was measured in the maximal section of the tumor using the region of interest. Microvessel density (MVD) was evaluated as a histopathologic parameter using tissue specimens immunohistochemically stained with an anti-ERG antibody. The CT attenuation value and MVD were compared between atypical and typical PRCCs using the Mann-Whitney U test, where p < 0.05 was considered significant. The correlations between CT attenuation value and MVD were evaluated in all PRCCs using single linear regression analysis. RESULTS The mean CT attenuation value and the MVD were significantly higher in atypical than in typical PRCCs. Correlation analyses revealed a weak positive correlation between the CT attenuation value and MVD. CONCLUSIONS We confirmed several cases of atypical PRCC that present with early contrast enhancement, such as clear cell renal cell carcinoma. In addition, a positive correlation was found between the CT attenuation value in the CMP of PRCCs and the vascular endothelial cell count.
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Affiliation(s)
- Tatsuya Yamamoto
- Department of Diagnostic Radiology, The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
| | - Amori Gulanbar
- Division of Pathology, The Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Kuniyoshi Hayashi
- Division of Biostatistics and Bioinformatics, Graduate School of Public Health, St. Luke's International University, 3-6-2 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Atsushi Kohno
- Department of Diagnostic Radiology, The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Yoshinobu Komai
- Department of Urology, The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Junji Yonese
- Department of Urology, The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Kiyoshi Matsueda
- Department of Diagnostic Radiology, The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Kentaro Inamura
- Division of Pathology, The Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
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Erdim C, Yardimci AH, Bektas CT, Kocak B, Koca SB, Demir H, Kilickesmez O. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis. Acad Radiol 2020; 27:1422-1429. [PMID: 32014404 DOI: 10.1016/j.acra.2019.12.015] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/09/2019] [Accepted: 12/16/2019] [Indexed: 02/06/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis. MATERIALS AND METHODS Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest. RESULTS The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively. CONCLUSION ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.
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Affiliation(s)
- Cagri Erdim
- Department of Radiology, Sultangazi Haseki Training and Research Hospital, Sultangazi, Istanbul, Turkey
| | - Aytul Hande Yardimci
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey
| | - Ceyda Turan Bektas
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey
| | - Burak Kocak
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey.
| | - Sevim Baykal Koca
- Department of Pathology, Istanbul Training and Research Hospital, Samatya, Istanbul, Turkey
| | - Hale Demir
- Department of Pathology, Amasya University School of Medicine, Amasya, Turkey
| | - Ozgur Kilickesmez
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, Istanbul 34098, Turkey
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Nguyen K, Schieda N, James N, McInnes MDF, Wu M, Thornhill RE. Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images. Eur Radiol 2020; 31:1676-1686. [PMID: 32914197 DOI: 10.1007/s00330-020-07233-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/14/2020] [Accepted: 08/27/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT. MATERIALS AND METHODS A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared. RESULTS Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05). CONCLUSION Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses. KEY POINTS • Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.
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Affiliation(s)
- Kathleen Nguyen
- 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.
| | - Nick James
- Software Solutions, The Ottawa Hospital, Ottawa, Canada
| | - Matthew D F McInnes
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Mark Wu
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Rebecca E Thornhill
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
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Schieda N, Nguyen K, Thornhill RE, McInnes MDF, Wu M, James N. Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT. Abdom Radiol (NY) 2020; 45:2786-2796. [PMID: 32627049 DOI: 10.1007/s00261-020-02632-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 06/14/2020] [Accepted: 06/23/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT. MATERIALS AND METHODS With IRB approval, we retrospectively identified 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe and 61 benign tumors; 49 oncocytomas and 12 fat-poor angiomyolipomas) with renal protocol CT between 2012 and 2017. Tumors were independently segmented by two blinded radiologists. Twenty-five 2-dimensional TA features were extracted from each phase. Diagnostic accuracy for 1) RCC versus benign tumor and 2) cc-RCC versus other tumor was assessed using XGBoost. RESULTS ML of texture analysis features on different phases achieved mean area under the ROC curve (AUC [SD]), sensitivity/specificity for 1) RCC vs benign = 0.70(0.19), 96%/32% on CM-CECT and 0.71(0.14), 83%/58% on NG-CECT and; 2) cc-RCC vs other = 0.77(0.12), 49%/90% on CM-CECT and 0.71(0.16), 22%/94% on NG-CECT. There was no difference in AUC comparing CECT to NCCT (p = 0.058-0.54) and no improvement when combining data across all three phases compared single-phase assessment (p = 0.39-0.68) for either outcome. AUCs decreased when ML models were trained with one phase and tested on a different phase for both outcomes (RCC;p = 0.045-0.106, cc-RCC; < 0.001). CONCLUSION Accuracy of machine learning classification of renal masses using texture analysis features did not depend on phase; however, models trained using one phase performed worse when tested on another phase particularly when associating NCCT and CECT. These findings have implications for large registries which use varying CT protocols to study renal masses.
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Affiliation(s)
- Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada.
| | - Kathleen Nguyen
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Rebecca E Thornhill
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Matthew D F McInnes
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Mark Wu
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Nick James
- Software Solutions, The Ottawa Hospital, Ottawa, Canada
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Razik A, Goyal A, Sharma R, Kandasamy D, Seth A, Das P, Ganeshan B. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma. Br J Radiol 2020; 93:20200569. [PMID: 32667833 DOI: 10.1259/bjr.20200569] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma. METHODS After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence. RESULTS Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000. CONCLUSIONS MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma. ADVANCES IN KNOWLEDGE There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.
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Affiliation(s)
- Abdul Razik
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Ankur Goyal
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Raju Sharma
- Departments of Radiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | | | - Amlesh Seth
- Departments of Urology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Prasenjit Das
- Departments of Pathology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospital NHS Trust, London, United Kingdom
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Paño B, Soler A, Goldman DA, Salvador R, Buñesch L, Sebastià C, Nicolau C. Usefulness of multidetector computed tomography to differentiate between renal cell carcinoma and oncocytoma. A model validation. Br J Radiol 2020; 93:20200064. [PMID: 32706993 DOI: 10.1259/bjr.20200064] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVE The purpose of this study is to validate a multivariable predictive model previously developed to differentiate between renal cell carcinoma (RCC) and oncocytoma using CT parameters. METHODS AND MATERIALS We included 100 renal lesions with final diagnosis of RCC or oncocytoma studied before surgery with 4-phase multidetector CT (MDCT). We evaluated the characteristics of the tumors and the enhancement patterns at baseline, arterial, nephrographic and excretory MDCT phases. RESULTS Histopathologically 15 tumors were oncocytomas and 85 RCCs. RCCs were significantly larger (median 4.4 cm vs 2.8 cm, p = 0.006). There were significant differences in nodule attenuation in the excretory phase compared to baseline (median: 31 vs 42, p = 0.015), with RCCs having lower values. Heterogeneous enhancement patterns were also more frequent in RCCs (85.9% vs 60%, p = 0.027).Multivariable analysis showed that the independent predictors of malignancy were the enhancement pattern, with oncocytomas being more homogeneous in the nephrographic phase [Odds Ratio (OR) 0.16 (95% CI 0.03 to 0.75, p = 0.02)], nodule enhancement in the excretory phase compared to baseline, with RCCs showing lower enhancement [OR 0.96 (95% CI 0.93 to 0.99, p = 0.005)], and a size > 4 cm, with RCCs being larger [OR 5.89 (95% CI 1.10 to 31.58), p = 0.038]. CONCLUSION The multivariable predictive model previously developed which combines different MDCT parameters, including lesion size > 4 cm, lesion enhancement in the excretory phase compared to baseline and enhancement heterogeneity, can be successfully applied to distinguish RCC from oncocytoma. ADVANCES IN KNOWLEDGE This study confirms that multiparametric assessment using MDCT (including parameters such as size, homogeneity and enhancement differences between the excretory and the baseline phases) can help distinguish between RCCs and oncocytomas. While it is true that this multiparametric predictive model may not always correctly classify renal tumors such as RCC or oncocytoma, it can be used to determine which patients would benefit from pre-surgical biopsy to confirm that the tumor is in fact an oncocytoma, and thereby avoid unnecessary surgical treatments.
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Affiliation(s)
- Blanca Paño
- Department of Radiology, Hospital Clínic de Barcelona. 170, Villarroel street, 08036 , Barcelona, Spain
| | - Alexandre Soler
- Department of Radiology, Hospital Clínic de Barcelona. 170, Villarroel street, 08036 , Barcelona, Spain
| | - Debra A Goldman
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Rafael Salvador
- Department of Radiology, Hospital Clínic de Barcelona. 170, Villarroel street, 08036 , Barcelona, Spain
| | - Laura Buñesch
- Department of Radiology, Hospital Clínic de Barcelona. 170, Villarroel street, 08036 , Barcelona, Spain
| | - Carmen Sebastià
- Department of Radiology, Hospital Clínic de Barcelona. 170, Villarroel street, 08036 , Barcelona, Spain
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27
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Jacobsen MC, Thrower SL. Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement. Med Phys 2020; 47:3752-3771. [PMID: 32453879 PMCID: PMC8495770 DOI: 10.1002/mp.14241] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/21/2022] Open
Abstract
Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual-energy CT (DECT) and photon-counting detector CT (PCD-CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD-CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi-energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image-guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.
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Affiliation(s)
- Megan C. Jacobsen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sara L. Thrower
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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28
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Fatemeh Z, Nicola S, Satheesh K, Eranga U. Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images. Med Phys 2020; 47:4032-4044. [PMID: 32329074 DOI: 10.1002/mp.14193] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/06/2020] [Accepted: 04/15/2020] [Indexed: 01/07/2023] Open
Abstract
PURPOSE Detection and accurate localization of renal masses (RM) are important steps toward future potential classification of benign vs malignant RM. A fully automated algorithm for detection and localization of RM may eliminate the observer variability in the clinical workflow. METHOD In this paper, we describe a fully automated methodology for accurate detection and segmentation of RM from contrast-enhanced computed tomography (CECT) images. We first determine the boundaries of the kidneys on the CECT images utilizing a convolutional neural network-based method to be used as a region of interest to search for RM. We then employ a homogenous U-Net-based ensemble learning model to identify and delineate RM. We used an institutional dataset comprised of CECT images in 315 patients to train and evaluate the proposed method. We compared results of our method to those of three-dimensional (3D) U-Net for RM localization and further evaluated our algorithm using the kidney tumor segmentation (KiTS19) challenge dataset. RESULTS The developed algorithm reported a Dice similarity coefficient (DSC) of 95.79% ± 5.16% and 96.25 ± 3.37 (mean ± standard deviation) for segmentation accuracy of kidney boundary from 125 and 60 test images from institutional and KiTS19 datasets, respectively. Using our method, RM were detected in 125 and 52 test cases, which corresponds to 100% and 86.67% sensitivity at patient level in institutional and KiTS19 test images. Our ensemble method for RM localization yielded a mean DSC of 88.65% ± 7.31% and 87.91% ± 6.82% on the institutional and KiTS19 test datasets, respectively. The mean DSC for RM delineation from CECT institutional test images using 3D U-Net was 85.95% ± 1.46%. CONCLUSION We describe a method for automated localization of RM using CECT images. Our results are important in terms of clinical perspective as fully automated detection of RM is a fundamental step for further diagnosis of cystic vs solid RM and eventually benign vs malignant solid RM, that has not been reported previously.
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Affiliation(s)
- Zabihollahy Fatemeh
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - Schieda Nicola
- Department of Radiology, University of Ottawa, Ottawa, ON, Canada
| | - Krishna Satheesh
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Ukwatta Eranga
- School of Engineering, University of Guelph, Guelph, ON, Canada
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Abstract
Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.
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Affiliation(s)
- Meghan G Lubner
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792, USA.
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30
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Abdessater M, Kanbar A, Comperat E, Dupont-Athenor A, Alechinsky L, Mouton M, Sebe P. Renal Oncocytoma: An Algorithm for Diagnosis and Management. Urology 2020; 143:173-180. [PMID: 32512107 DOI: 10.1016/j.urology.2020.05.047] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 04/23/2020] [Accepted: 05/16/2020] [Indexed: 12/18/2022]
Abstract
Renal oncocytoma is an uncommon tumor that exhibits numerous features which are characteristic but not necessarily unique. Percutaneous biopsy is a safe method of diagnosis. However, differentiation from other tumor subtypes often requires sophisticated analysis and is not universally feasible. This is why, surgical management can be considered as a first-line treatment or after surveillance. Potential triggers for change in management are: tumor size >3 cm, stage progression, kinetics of size progression (>5 mm/y), and clinical change in patient or tumor factors. Long-term follow-up data are lacking and greater centralization should be considered to reach adequate management.
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Affiliation(s)
- Maher Abdessater
- Department of Urology and Renal Transplantation, APHP - Pitié Salpêtrière University Hospital, Paris, France; Department of Urology, Hospital Group Diaconesses Croix Saint-Simon, Paris, France.
| | - Anthony Kanbar
- Department of Urology, Hospital Group Diaconesses Croix Saint-Simon, Paris, France
| | - Eva Comperat
- Department of Pathology, APHP - Tenon Hospital, Paris, France
| | | | - Louise Alechinsky
- Department of Urology and Renal Transplantation, APHP - Pitié Salpêtrière University Hospital, Paris, France; Department of Urology, Hospital Group Diaconesses Croix Saint-Simon, Paris, France
| | - Martin Mouton
- Department of Urology, Hospital Group Diaconesses Croix Saint-Simon, Paris, France
| | - Philippe Sebe
- Department of Urology, Hospital Group Diaconesses Croix Saint-Simon, Paris, France
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31
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Jordan AR, Wang J, Yates TJ, Hasanali SL, Lokeshwar SD, Morera DS, Shamaladevi N, Li CS, Klaassen Z, Terris MK, Thangaraju M, Singh AB, Soloway MS, Lokeshwar VB. Molecular targeting of renal cell carcinoma by an oral combination. Oncogenesis 2020; 9:52. [PMID: 32427869 PMCID: PMC7237463 DOI: 10.1038/s41389-020-0233-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/17/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023] Open
Abstract
The 5-year survival rate of patients with metastatic renal cell carcinoma (mRCC) is <12% due to treatment failure. Therapeutic strategies that overcome resistance to modestly effective drugs for mRCC, such as sorafenib (SF), could improve outcome in mRCC patients. SF is terminally biotransformed by UDP-glucuronosyltransferase-1A9 (A9) mediated glucuronidation, which inactivates SF. In a clinical-cohort and the TCGA-dataset, A9 transcript and/or protein levels were highly elevated in RCC specimens and predicted metastasis and overall-survival. This suggested that elevated A9 levels even in primary tumors of patients who eventually develop mRCC could be a mechanism for SF failure. 4-methylumbelliferone (MU), a choleretic and antispasmodic drug, downregulated A9 and inhibited SF-glucuronidation in RCC cells. Low-dose SF and MU combinations inhibited growth, motility, invasion and downregulated an invasive signature in RCC cells, patient-derived tumor explants and/or endothelial-RCC cell co-cultures; however, both agents individually were ineffective. A9 overexpression made RCC cells resistant to the combination, while its downregulation sensitized them to SF treatment alone. The combination inhibited kidney tumor growth, angiogenesis and distant metastasis, with no detectable toxicity; A9-overexpressing tumors were resistant to treatment. With effective primary tumor control and abrogation of metastasis in preclinical models, the low-dose SF and MU combinations could be an effective treatment option for mRCC patients. Broadly, our study highlights how targeting specific mechanisms that cause the failure of “old” modestly effective FDA-approved drugs could improve treatment response with minimal alteration in toxicity profile.
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Affiliation(s)
- Andre R Jordan
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA.,Sheila and David Fuente Graduate Program in Cancer Biology, University of Miami-Miller School of Medicine, Miami, 1600 NW 10th Avenue, Miami, FL, 33136, USA
| | - Jiaojiao Wang
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | - Travis J Yates
- Sheila and David Fuente Graduate Program in Cancer Biology, University of Miami-Miller School of Medicine, Miami, 1600 NW 10th Avenue, Miami, FL, 33136, USA.,Travis Yates: QualTek Molecular Laboratories, King of Prussia, PA, USA
| | - Sarrah L Hasanali
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | - Soum D Lokeshwar
- Honors Program in Medical Education, University of Miami-Miller School of Medicine, Miami, 1600 NW 10th Avenue, Miami, FL, 33136, USA
| | - Daley S Morera
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | | | - Charles S Li
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | - Zachary Klaassen
- Department of Surgery, Division of Urology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | - Martha K Terris
- Department of Surgery, Division of Urology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | - Muthusamy Thangaraju
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA
| | - Amar B Singh
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Vinata B Lokeshwar
- Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA, 30912, USA.
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32
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Gentili F, Bronico I, Maestroni U, Ziglioli F, Silini EM, Buti S, de Filippo M. Small renal masses (≤ 4 cm): differentiation of oncocytoma from renal clear cell carcinoma using ratio of lesion to cortex attenuation and aorta-lesion attenuation difference (ALAD) on contrast-enhanced CT. Radiol Med 2020; 125:1280-1287. [PMID: 32385827 DOI: 10.1007/s11547-020-01199-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/13/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE We investigate the use of ratio of lesion to cortex (L/C) attenuation and aorta-lesion attenuation difference (ALAD) on multiphase contrast-enhanced CT to help distinguish oncocytoma from clear cell RCC in small renal masses (diameter < 4 cm). METHODS We retrospectively identified 76 patients that undergo CT before surgery for a suspicious small renal mass between January 2014 and December 2018 with pathological diagnosis of 21 oncocytomas (ROs), 25 clear cell RCCs, 7 chromophobe RCCs, 7 papillary RCCs, 7 multilocular cystic RCCs, 7 angiomyolipomas and 2 leiomyomas. CT attenuation values were obtained for the tumor, the normal renal cortex and the aorta, placing a circular region of interest (ROI) in the same slice by two radiologists, independently. RESULTS In the corticomedullary phase, ROs showed isodense enhancement to the renal cortex (ratio L/C 0.92 ± 0.12), while clear cell RCCs appeared hypodense to the renal cortex (ratio L/C 0.69 ± 0.20; p < 0.01) with an accuracy of 80% for diagnosing RO. In nephrographic phase, the ratio L/C attenuation was lower than the corticomedullary phase in ROs (0.78 ± 0.11) showing an early washout pattern, while the ratio L/C was similar to the corticomedullary phase in clear cell RCCs (0.69 ± 0.13; p = 0.025, with an accuracy of 65% for diagnosing RO). The ratio L/C attenuation showed considerable overlap between ROs and clear cell RCCs in the excretory phase (p = 0.27). Mean ALAD values in the nephrographic phase were 21.95 ± 16.24 for ROs and 36.96 ± 30.53 for clear cell RCCs (p = 0.049). CONCLUSION The ratio L/C attenuation in corticomedullary phase may be useful to differentiate RO from clear cell RCC.
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Affiliation(s)
- Francesco Gentili
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy.
| | - Ilaria Bronico
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
| | - Umberto Maestroni
- Department of Urology, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
| | - Francesco Ziglioli
- Department of Urology, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
| | - Enrico Maria Silini
- Department of Biomedical, Biotechnological and Translational Sciences, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
| | - Sebastiano Buti
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
- Department of Medical Oncology, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
| | - Massimo de Filippo
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
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Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion. Eur Radiol 2020; 30:5183-5190. [PMID: 32350661 DOI: 10.1007/s00330-020-06787-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 02/20/2020] [Accepted: 03/02/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images. METHODS This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms. RESULTS For differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively. CONCLUSIONS A semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT. KEY POINTS • Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images. • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).
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Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning. AJR Am J Roentgenol 2020; 214:605-612. [PMID: 31913072 DOI: 10.2214/ajr.19.22074] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE. This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. MATERIALS AND METHODS. This retrospective study included 1807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 patients between 2012 and 2016. Masses were classified as malignant (n = 136) or benign (n = 32). The dataset was randomly divided into five subsets: four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images). The Inception-v3 architecture convolutional neural network (CNN) model was used. The AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Multivariate logistic regression analysis was also performed. RESULTS. Malignant and benign lesions showed no significant difference of size. The AUC value of corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022). The highest accuracy (88%) was achieved in corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, sex, and lesion size. CONCLUSION. A deep learning method with a CNN allowed acceptable differentiation of small (≤ 4 cm) solid renal masses in dynamic CT images, especially in the corticomedullary image model.
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Udare A, Walker D, Krishna S, Chatelain R, McInnes MD, Flood TA, Schieda N. Characterization of clear cell renal cell carcinoma and other renal tumors: evaluation of dual-energy CT using material-specific iodine and fat imaging. Eur Radiol 2019; 30:2091-2102. [PMID: 31858204 DOI: 10.1007/s00330-019-06590-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/02/2019] [Accepted: 11/12/2019] [Indexed: 12/16/2022]
Abstract
OBJECTIVE This study aimed to assess material-specific iodine and fat images for diagnosis of clear cell renal cell carcinoma (cc-RCC) compared to papillary RCC (p-RCC) and other renal masses. MATERIALS AND METHODS With IRB approval, we identified histologically confirmed solid renal masses that underwent rapid-kVp-switch DECT between 2016 and 2018: 25 cc-RCC (7 low grade versus 18 high grade), 11 p-RCC, and 6 other tumors (2 clear cell papillary RCC, 2 chromophobe RCC, 1 oncocytoma, 1 renal angiomyomatous tumor). A blinded radiologist measured iodine and fat concentration on material-specific iodine-water and fat-water basis pair images. Comparisons were performed between groups using univariate analysis and diagnostic accuracy calculated by ROC. RESULTS Iodine concentration was higher in cc-RCC (6.14 ± 1.79 mg/mL) compared to p-RCC (1.40 ± 0.54 mg/mL, p < 0.001), but not compared to other tumors (5.0 ± 2.2 mg/mL, p = 0.370). Intratumoral fat was seen in 36.0% (9/25) cc-RCC (309.6 ± 234.3 mg/mL [71.1-762.3 ng/mL]), 9.1% (1/11) papillary RCC (97.11 mg/mL), and no other tumors (p = 0.036). Iodine concentration ≥ 3.99 mg/mL achieved AUC and sensitivity/specificity of 0.88 (CI 0.76-1.00) and 92.31%/82.40% to diagnose cc-RCC. To diagnose p-RCC, iodine concentration ≤ 2.5 mg/mL achieved AUC and sensitivity/specificity of 0.99 (0.98-1.00) and 100%/100%. The presence of intratumoral fat had AUC 0.64 (CI 0.53-0.75) and sensitivity/specificity of 34.6%/93.8% to diagnose cc-RCC. A logistic regression model combining iodine concentration and presence of fat increased AUC to 0.91 (CI 0.81-1.0) with sensitivity/specificity of 80.8%/93.8% to diagnose cc-RCC. CONCLUSION Iodine concentration values are highly accurate to differentiate clear cell RCC from papillary RCC; however, they overlap with other tumors. Fat-specific images may improve differentiation of clear cell RCC from other avidly enhancing tumors. KEY POINTS • Clear cell renal cell carcinoma (RCC) has significantly higher iodine concentration than papillary RCC, but there is an overlap in values comparing clear cell RCC to other renal tumors. • Iodine concentration ≤ 2.5 mg/mL is highly accurate to differentiate papillary RCC from clear cell RCC and other renal tumors. • The presence of microscopic fat on material-specific fat images was specific for clear cell RCC, helping to differentiate clear cell RCC from other avidly enhancing renal tumors.
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Affiliation(s)
- Amar Udare
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada
| | - Daniel Walker
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada
| | - Satheesh Krishna
- Joint Department of Medical Imaging, Toronto General Hospital, The University of Toronto, Toronto, Canada
| | - Robert Chatelain
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada
| | - Matthew Df McInnes
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada
| | - Trevor A Flood
- Department of Anatomical Pathology, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada.
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Deng Y, Soule E, Cui E, Samuel A, Shah S, Lall C, Sundaram C, Sandrasegaran K. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol 2019; 75:108-115. [PMID: 31668402 DOI: 10.1016/j.crad.2019.09.131] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 09/12/2019] [Indexed: 12/22/2022]
Abstract
AIM To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters. MATERIALS AND METHODS A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy. RESULTS Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC. CONCLUSIONS Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.
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Affiliation(s)
- Y Deng
- Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - E Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - E Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - A Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - S Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - C Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - C Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - K Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Mayo Clinic, Phoenix, AZ, USA.
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Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies. AJR Am J Roentgenol 2019; 214:129-136. [PMID: 31613661 DOI: 10.2214/ajr.19.21709] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE. The purpose of this study was to systematically review the radiomics literature on renal mass characterization in terms of reproducibility and validation strategies. MATERIALS AND METHODS. With use of PubMed and Google Scholar, a systematic literature search was performed to identify original research papers assessing the value of radiomics in characterization of renal masses. The data items were extracted on the basis of three main categories: baseline study characteristics, radiomic feature reproducibility strategies, and statistical model validation strategies. RESULTS. After screening and application of the eligibility criteria, a total of 41 papers were included in the study. Almost one-half of the papers (19 [46%]) presented at least one reproducibility analysis. Segmentation variability (18 [44%]) was the main theme of the analyses, outnumbering image acquisition or processing (3 [7%]). No single paper considered slice selection bias. The most commonly used statistical tool for analysis was intraclass correlation coefficient (14 of 19 [74%]), with no consensus on the threshold or cutoff values. Approximately one-half of the papers (22 [54%]) used at least one validation method, with a predominance of internal validation techniques (20 [49%]). The most frequently used internal validation technique was k-fold cross-validation (12 [29%]). Independent or external validation was used in only three papers (7%). CONCLUSION. Workflow characteristics described in the radiomics literature about renal mass characterization are heterogeneous. To bring radiomics from a mere research area to clinical use, the field needs many more papers that consider the reproducibility of radiomic features and include independent or external validation in their workflow.
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Deng Y, Soule E, Samuel A, Shah S, Cui E, Asare-Sawiri M, Sundaram C, Lall C, Sandrasegaran K. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 2019; 29:6922-6929. [PMID: 31127316 DOI: 10.1007/s00330-019-06260-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/01/2019] [Accepted: 04/30/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade. METHODS A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis. RESULTS A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful. CONCLUSION Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.
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Affiliation(s)
- Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Erik Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Aster Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sakhi Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Enming Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Oncology, Hope Regional Cancer Center, Panama, FL, USA
| | - Chandru Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Kumaresan Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.
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Abstract
OBJECTIVE. Renal masses comprise a heterogeneous group of pathologic conditions, including benign and indolent diseases and aggressive malignancies, complicating management. In this article, we explore the emerging role of imaging to provide a comprehensive noninvasive characterization of a renal mass-so-called "virtual biopsy"-and its potential use in the management of patients with renal tumors. CONCLUSION. Percutaneous renal mass biopsy (RMB) remains a valuable method to provide a presurgical histopathologic diagnosis of renal masses, but it is an invasive procedure and is not always feasible. Accumulating data support the use of imaging features to predict histopathology of renal masses. Imaging may help address some of the inherent limitations of RMB, and in certain settings, a multimodal clinical approach may allow decreasing the need for RMB.
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Hoang UN, Mojdeh Mirmomen S, Meirelles O, Yao J, Merino M, Metwalli A, Marston Linehan W, Malayeri AA. Assessment of multiphasic contrast-enhanced MR textures in differentiating small renal mass subtypes. Abdom Radiol (NY) 2018; 43:3400-3409. [PMID: 29858935 PMCID: PMC8080867 DOI: 10.1007/s00261-018-1625-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE This study seeks to evaluate the use of quantitative texture parameters extracted from multiphasic contrast-enhanced magnetic resonance (MR) imaging in differentiating between benign and malignant masses (oncocytoma vs. clear cell and papillary RCC) and between common subtypes of renal cell carcinoma (clear cell vs. papillary RCC) in small renal masses (< 4 cm). METHOD One-hundred and forty-two renal lesions (90 clear cell and 22 papillary RCCs; 30 oncocytomas) were identified in a cohort of 41 patients (18 men, 23 women: mean age, 52.8 ± 14.4 years) who underwent preoperative multiphasic contrast-enhanced MR with four phases (unenhanced, arterial, venous, and delayed) between 2015 and 2016. In this study, texture features were extracted from entire cross-sectional tumoral region in three consecutive slices containing the largest cross-sectional area from each of the four phases. The change in imaging feature between precontrast imaging and each postcontrast phase was calculated. Data dimension reduction and feature selection were performed by conducting (1) pairwise Wilcoxon rank test followed by modified false discovery rate adjustment, and (2) Lasso regression. Multivariate modeling incorporating the selected features was performed using random forest classification method. RESULTS Histogram imaging features were informative variables in differentiating between benign and malignant masses, while textures imaging features were of added value in differentiating between subtypes of RCCs. Papillary RCCs were distinguished from clear cell RCCs (sensitivity 65.5%, specificity 88%, and accuracy 77.9%), oncocytomas from clear cell RCCs (sensitivity 67.3%, specificity 88.9%, and accuracy 79.3%), and oncocytomas from papillary and clear cell RCCs (sensitivity 64.7%, specificity 85.9%, and accuracy 77.9%). CONCLUSIONS A combination of histogram and texture imaging features on multiphasic MR can help differentiate histologic cell types in common small renal masses (< 4 cm).
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Affiliation(s)
- Uyen N Hoang
- Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA.
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, 20892, USA.
- , 10 Center Dr, Bethesda, MD, 20814, USA.
| | - S Mojdeh Mirmomen
- Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Osorio Meirelles
- Neuroepidemiology Section, National Institute of Aging, Bethesda, MD, 20892, USA
| | - Jianhua Yao
- Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Maria Merino
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Adam Metwalli
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, 20892, USA
| | - W Marston Linehan
- Neuroepidemiology Section, National Institute of Aging, Bethesda, MD, 20892, USA
| | - Ashkan A Malayeri
- Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, 20892, USA
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Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping. AJR Am J Roentgenol 2018; 211:W288-W296. [PMID: 30240299 DOI: 10.2214/ajr.18.19551] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses. MATERIALS AND METHODS In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC. RESULTS Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses. CONCLUSION The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.
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Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2018; 29:1067-1073. [PMID: 30116961 DOI: 10.1007/s00330-018-5662-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/15/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVES We investigated the value of CT texture analysis (CTTA) in predicting prognosis of unresectable pancreatic cancer. METHODS Sixty patients with unresectable pancreatic cancers at presentation were enrolled for post-processing with CTTA using commercially available software (TexRAD Ltd, Cambridge, UK). The largest cross-section of the tumour on axial CT was chosen to draw a region-of-interest. CTTA parameters (mean value of positive pixels (MPP), kurtosis, entropy, skewness), arterial and venous invasion, metastatic disease and tumour size were correlated with overall and progression-free survivals. RESULTS The median overall and progression-free survivals of cohort were 13.3 and 7.8 months, respectively. On multivariate Cox proportional hazard regression analysis, presence of metastatic disease at presentation had the highest association with overall survival (p = 0.003-0.05) and progression-free survival (p < 0.001 to p = 0.004). MPP at medium spatial filter was significantly associated with poor overall survival (p = 0.04). On Kaplan-Meier survival analysis of CTTA parameters at medium spatial filter, MPP of more than 31.625 and kurtosis of more than 0.565 had significantly worse overall survival (p = 0.036 and 0.028, respectively). CONCLUSIONS CTTA features were significantly associated with overall survival in pancreas cancer, particularly in patients with non-metastatic, locally advanced disease. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis can determine prognosis in patients with unresectable pancreas cancer. • The best predictors of poor prognosis were high kurtosis and MPP.
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Affiliation(s)
- Kumar Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.
| | - Yuning Lin
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Department of Medical Imaging, Fuzhou General Hospital, Fuzhou, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA.,Hope Radiation Cancer, Panama City, FL, USA
| | - Tai Taiyini
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
| | - Mark Tann
- Department of Radiology, Indiana University School of Medicine, 550 N. University Blvd., UH 0279, Indianapolis, IN, 46202, USA
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Varghese BA, Chen F, Hwang DH, Cen SY, Gill IS, Duddalwar VA. Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT. Br J Radiol 2018; 91:20170789. [PMID: 29888982 DOI: 10.1259/bjr.20170789] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics. METHODS In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation. RESULTS A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve >0.7, p < 0.05) within these three groups of comparisons. CONCLUSION In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhanced CT data.
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Affiliation(s)
- Bino A Varghese
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Frank Chen
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Darryl H Hwang
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Steven Y Cen
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA
| | - Inderbir S Gill
- 2 Institute of Urology, University of Southern California , Los Angeles, CA , USA
| | - Vinay A Duddalwar
- 1 Department of Radiology, University of Southern California , Los Angeles, CA , USA.,2 Institute of Urology, University of Southern California , Los Angeles, CA , USA
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Khene Z, Bensalah K, Largent A, Shariat S, Verhoest G, Peyronnet B, Acosta O, DeCrevoisier R, Mathieu R. Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat. World J Urol 2018; 36:1635-1642. [DOI: 10.1007/s00345-018-2292-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 04/05/2018] [Indexed: 01/29/2023] Open
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Abstract
PURPOSE OF REVIEW Renal cell carcinoma is a heterogeneous disease with a spectrum of subtypes and clinical behavior. Quantitative and qualitative imaging biomarkers are sought to correlate with genetic and histologic features and complement pathologic analysis. RECENT FINDINGS Texture analysis, radiogenomics, and modality-specific advancements have yielded an array of renal cell carcinoma imaging biomarkers in the research domain. Although many techniques are promising, standardization and validation of these procedures are needed prior to implementation into clinical practice. SUMMARY We introduce novel imaging techniques and analytic methods which have been shown to contribute to characterization of renal cell carcinoma and its subtypes, aggressiveness, and responsiveness to therapy, including associated advantages and limitations.
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Are growth patterns on MRI in small (< 4 cm) solid renal masses useful for predicting benign histology? Eur Radiol 2018; 28:3115-3124. [PMID: 29492598 DOI: 10.1007/s00330-018-5324-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 01/02/2018] [Accepted: 01/10/2018] [Indexed: 12/21/2022]
Abstract
PURPOSE To evaluate previously described growth patterns in < 4 cm solid renal masses. MATERIALS AND METHODS With IRB approval, 63 renal cell carcinomas (RCC; clear cell n = 22, papillary n = 28, chromophobe n = 13) and 36 benign masses [minimal-fat (mf) angiomyolipoma (AML) n = 13, oncocytoma n = 23) from a single institution were independently evaluated by two blinded radiologists (R1/R2) using T2-weighted MRI for (1) the angular interface sign (AIS), (2) bubble-over sign (BOS), (3) percentage (%) exophytic growth and (4) long-to-short axis ratio. Comparisons were performed using ANOVA, chi-square and multi-variate regression. RESULTS AIS was present in 11.1% (7/63) -9.5% (6/63) R1/R2 RCC compared to 13.9% (5/36) -19.4% (7/36) R1/R2 benign masses (p = 0.68 and 0.16). BOS was present in 11.1% (7/63) -3.2% (2/63) R1/R2 RCC compared to 16.7% (6/36) -8.3% (3/36) R1/R2 benign masses (p = 0.432 and 0.261). Agreement was moderate (K = 0.50 and 0.55). mf-AML [66 ± 32% (range 0-100%)] and oncocytoma [53 ± 26% (0-90%)] had larger % exophytic growth compared to RCC [32 ± 23% (0-80%)] (p < 0.001). No RCC had 90-100% exophytic growth, present in 38.5% (5/13) mf-AMLs and 17.4% (4/23) oncocytomas. The long-to-short axis did not differ between groups (p = 0.053). CONCLUSIONS Benign masses show greater % exophytic growth whereas other growth patterns are not useful. Future studies evaluating % exophytic growth using multi-variate MR analysis in renal masses are required. KEY POINTS • Greater exophytic growth is associated with benignity among solid renal masses. • Only minimal fat AMLs and oncocytomas had 90-100% exophytic growth. • The angular interface sign was not useful to differentiate benign masses from RCC. • The bubble-over sign was not useful to differentiate benign masses from RCC. • Subjective analysis of growth patterns had fair-to-moderate agreement.
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Sasaguri K, Takahashi N. CT and MR imaging for solid renal mass characterization. Eur J Radiol 2017; 99:40-54. [PMID: 29362150 DOI: 10.1016/j.ejrad.2017.12.008] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 12/04/2017] [Accepted: 12/09/2017] [Indexed: 12/15/2022]
Abstract
As our understanding has expanded that relatively large fraction of incidentally discovered renal masses, especially in small size, are benign or indolent even if malignant, there is growing acceptance of more conservative management including active surveillance for small renal masses. As for advanced renal cell carcinomas (RCCs), nonsurgical and subtype specific treatment options such as immunotherapy and targeted therapy is developing. On these backgrounds, renal mass characterization including differentiation of benign from malignant tumors, RCC subtyping and prediction of RCC aggressiveness is receiving much attention and a variety of imaging techniques and analytic methods are being investigated. In addition to conventional imaging techniques, integration of texture analysis, functional imaging (i.e. diffusion weighted and perfusion imaging) and multivariate diagnostic methods including machine learning have provided promising results for these purposes in research fields, although standardization and external, multi-institutional validations are needed.
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Affiliation(s)
- Kohei Sasaguri
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan.
| | - Naoki Takahashi
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States.
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Abstract
Oncocytoma is a well-defined benign renal tumor, with classic gross and histologic features, including a tan or mahogany-colored mass with central scar, microscopic nested architecture, bland cytology, and round, regular nuclei with prominent central nucleoli. As a result of variations in this classic appearance, difficulty in standardizing diagnostic criteria, and entities that mimic oncocytoma, such as eosinophilic variant chromophobe renal cell carcinoma and succinate dehydrogenase-deficient renal cell carcinoma, pathologic diagnosis remains a challenge. This review addresses the current state of pathologic diagnosis of oncocytoma, with emphasis on modern diagnostic markers, areas of controversy, and emerging techniques for less invasive diagnosis, including renal mass biopsy and advanced imaging.
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Noda Y, Goshima S, Koyasu H, Shigeyama S, Miyoshi T, Kawada H, Kawai N, Matsuo M. Renovascular CT: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction. Clin Radiol 2017; 72:901.e13-901.e19. [DOI: 10.1016/j.crad.2017.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 05/30/2017] [Accepted: 06/06/2017] [Indexed: 10/19/2022]
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