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Albano D, Mallardi C, Afat S, Agnollitto PM, Caruso D, Cannella R, Carriero S, Chupetlovska K, Clauser P, D'Angelo T, De Santis D, Dioguardi Burgio M, Dumic-Cule I, Fanni SC, Fusco S, Gatti M, Gitto S, Jankovic S, Karagechev T, Klontzas ME, Koltsakis E, Leithner D, Matišić V, Muscogiuri G, Penkova R, Polici M, Serpi F, Sofia C, Snoj Z, Akinci D'Antonoli T, Vernuccio F, Vieira J, Vieira AC, Wielema M, Zerunian M, Messina C. How young radiologists use contrast media and manage adverse reactions: an international survey. Insights Imaging 2024; 15:92. [PMID: 38530547 DOI: 10.1186/s13244-024-01658-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/25/2024] [Indexed: 03/28/2024] Open
Abstract
OBJECTIVES To collect real-world data about the knowledge and self-perception of young radiologists concerning the use of contrast media (CM) and the management of adverse drug reactions (ADR). METHODS A survey (29 questions) was distributed to residents and board-certified radiologists younger than 40 years to investigate the current international situation in young radiology community regarding CM and ADRs. Descriptive statistics analysis was performed. RESULTS Out of 454 respondents from 48 countries (mean age: 31.7 ± 4 years, range 25-39), 271 (59.7%) were radiology residents and 183 (40.3%) were board-certified radiologists. The majority (349, 76.5%) felt they were adequately informed regarding the use of CM. However, only 141 (31.1%) received specific training on the use of CM and 82 (18.1%) about management ADR during their residency. Although 266 (58.6%) knew safety protocols for handling ADR, 69.6% (316) lacked confidence in their ability to manage CM-induced ADRs and 95.8% (435) expressed a desire to enhance their understanding of CM use and handling of CM-induced ADRs. Nearly 300 respondents (297; 65.4%) were aware of the benefits of contrast-enhanced ultrasound, but 249 (54.8%) of participants did not perform it. The preferred CM injection strategy in CT parenchymal examination and CT angiography examination was based on patient's lean body weight in 318 (70.0%) and 160 (35.2%), a predeterminate fixed amount in 79 (17.4%) and 116 (25.6%), iodine delivery rate in 26 (5.7%) and 122 (26.9%), and scan time in 31 (6.8%) and 56 (12.3%), respectively. CONCLUSION Training in CM use and management ADR should be implemented in the training of radiology residents. CRITICAL RELEVANCE STATEMENT We highlight the need for improvement in the education of young radiologists regarding contrast media; more attention from residency programs and scientific societies should be focused on training about contrast media use and the management of adverse drug reactions. KEY POINTS • This survey investigated training of young radiologists about use of contrast media and management adverse reactions. • Most young radiologists claimed they did not receive dedicated training. • An extreme heterogeneity of responses was observed about contrast media indications/contraindications and injection strategy.
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Affiliation(s)
- Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Dipartimento Di Scienze Biomediche, Chirurgiche Ed Odontoiatriche, Università Degli Studi Di Milano, Milan, Italy.
| | - Carmen Mallardi
- Scuola Di Specializzazione in Radiodiagnostica, Università Degli Studi Di Milano, Milan, Italy
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Paulo Moraes Agnollitto
- Ribeirão Preto Medical School, Radiology Division of the Department of Medical Imaging, Hematology and Clinical Oncology, University of São Paulo, São Paulo, Ribeirão Preto, Brazil
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza - University of Rome, Rome, Italy
| | - Roberto Cannella
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Serena Carriero
- Department of Radiology and Interventional Radiology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Kalina Chupetlovska
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Tommaso D'Angelo
- Diagnostic and Inverventional Radiology Unit, Department of Biomedical Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza - University of Rome, Rome, Italy
| | - Marco Dioguardi Burgio
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université Paris Cité, INSERM, Centre de Recherche Sur L'inflammation, 75018, Paris, France
| | - Ivo Dumic-Cule
- Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Kispaticeva 12, 10000, Zagreb, Croatia
- University North, 104 Brigade 3, 42000, Varazdin, Croatia
| | | | - Stefano Fusco
- Department of Biomedical Sciences for Health, Università Degli Studi Di Milano, Milan, Italy
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, Università Degli Studi Di Milano, Milan, Italy
| | - Sonja Jankovic
- Center for Radiology, University Clinical Center Nis, Nis, Republic of Serbia
| | | | - Michail E Klontzas
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
| | - Emmanouil Koltsakis
- Department of Radiology, Karolinska University Hospital of Stockholm, Stockholm, Sweden
| | - Doris Leithner
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Vid Matišić
- St. Catherine Specialty Hospital, 10000, Zagreb, Croatia
| | | | - Ralitsa Penkova
- Radiology Department, Acibadem City Clinic Tokuda Hospital, 51B Nikola Y. Vaptsarov Blvd, Sofia, 1407, Bulgaria
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza - University of Rome, Rome, Italy
- PhD School in Traslational Medicine and Oncology, Department of Medical Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, "Sapienza" University of Rome, Rome, Italy
| | - Francesca Serpi
- Department of Biomedical Sciences for Health, Università Degli Studi Di Milano, Milan, Italy
| | - Carmelo Sofia
- Diagnostic and Inverventional Radiology Unit, Department of Biomedical Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | - Ziga Snoj
- Radiology Institute, University Medical Centre Ljubljana, Zaloška 7, Ljubljana, Slovenia
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Federica Vernuccio
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - João Vieira
- Radiology, Hospital Divino Espírito Santo, Ponta Delgada, Portugal
| | - Ana Catarina Vieira
- Radiology Department, Hospital CUF Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Mirjam Wielema
- Department of Radiology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
| | - Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza - University of Rome, Rome, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, Università Degli Studi Di Milano, Milan, Italy
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Vrettos K, Koltsakis E, Zibis AH, Karantanas AH, Klontzas ME. Generative adversarial networks for spine imaging: A critical review of current applications. Eur J Radiol 2024; 171:111313. [PMID: 38237518 DOI: 10.1016/j.ejrad.2024.111313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/18/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024]
Abstract
PURPOSE In recent years, the field of medical imaging has witnessed remarkable advancements, with innovative technologies which revolutionized the visualization and analysis of the human spine. Among the groundbreaking developments in medical imaging, Generative Adversarial Networks (GANs) have emerged as a transformative tool, offering unprecedented possibilities in enhancing spinal imaging techniques and diagnostic outcomes. This review paper aims to provide a comprehensive overview of the use of GANs in spinal imaging, and to emphasize their potential to improve the diagnosis and treatment of spine-related disorders. A specific review focusing on Generative Adversarial Networks (GANs) in the context of medical spine imaging is needed to provide a comprehensive and specialized analysis of the unique challenges, applications, and advancements within this specific domain, which might not be fully addressed in broader reviews covering GANs in general medical imaging. Such a review can offer insights into the tailored solutions and innovations that GANs bring to the field of spinal medical imaging. METHODS An extensive literature search from 2017 until July 2023, was conducted using the most important search engines and identified studies that used GANs in spinal imaging. RESULTS The implementations include generating fat suppressed T2-weighted (fsT2W) images from T1 and T2-weighted sequences, to reduce scan time. The generated images had a significantly better image quality than true fsT2W images and could improve diagnostic accuracy for certain pathologies. GANs were also utilized in generating virtual thin-slice images of intervertebral spaces, creating digital twins of human vertebrae, and predicting fracture response. Lastly, they could be applied to convert CT to MRI images, with the potential to generate near-MR images from CT without MRI. CONCLUSIONS GANs have promising applications in personalized medicine, image augmentation, and improved diagnostic accuracy. However, limitations such as small databases and misalignment in CT-MRI pairs, must be considered.
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Affiliation(s)
- Konstantinos Vrettos
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
| | - Emmanouil Koltsakis
- Department of Radiology, Karolinska University Hospital, Solna, Stockholm, Sweden
| | - Aristeidis H Zibis
- Department of Anatomy, Medical School, University of Thessaly, Larissa, Greece
| | - Apostolos H Karantanas
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece; Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
| | - Michail E Klontzas
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece; Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece.
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Klontzas ME, Kalarakis G, Koltsakis E, Papathomas T, Karantanas AH, Tzortzakakis A. Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset. Insights Imaging 2024; 15:26. [PMID: 38270726 PMCID: PMC10811309 DOI: 10.1186/s13244-023-01601-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 12/17/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVES To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset. METHODS A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN. RESULTS Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma. CONCLUSION Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors. CRITICAL RELEVANCE STATEMENT Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors. KEY POINTS • Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion, Greece
| | - Georgios Kalarakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Emmanouil Koltsakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Department of Clinical Pathology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, 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, 14 186, Huddinge, Stockholm, Sweden.
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Triantafyllou M, Klontzas ME, Koltsakis E, Papakosta V, Spanakis K, Karantanas AH. Radiomics for the Detection of Active Sacroiliitis Using MR Imaging. Diagnostics (Basel) 2023; 13:2587. [PMID: 37568950 PMCID: PMC10416894 DOI: 10.3390/diagnostics13152587] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.
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Affiliation(s)
- Matthaios Triantafyllou
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece; (M.T.); (M.E.K.); (E.K.); (V.P.); (K.S.)
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71500 Heraklion, Greece
| | - Michail E. Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece; (M.T.); (M.E.K.); (E.K.); (V.P.); (K.S.)
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71500 Heraklion, Greece
| | - Emmanouil Koltsakis
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece; (M.T.); (M.E.K.); (E.K.); (V.P.); (K.S.)
- Department of Radiology, Karolinska University Hospital, 17164 Stockholm, Sweden
| | - Vasiliki Papakosta
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece; (M.T.); (M.E.K.); (E.K.); (V.P.); (K.S.)
| | - Konstantinos Spanakis
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece; (M.T.); (M.E.K.); (E.K.); (V.P.); (K.S.)
| | - Apostolos H. Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece; (M.T.); (M.E.K.); (E.K.); (V.P.); (K.S.)
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71500 Heraklion, Greece
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Klontzas ME, Koltsakis E, Kalarakis G, Trpkov K, Papathomas T, Karantanas AH, Tzortzakakis A. Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors. Cancers (Basel) 2023; 15:3553. [PMID: 37509214 PMCID: PMC10377512 DOI: 10.3390/cancers15143553] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The increasing evidence of oncocytic renal tumors positive in 99mTc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of 99mTc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of 99mTc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of 99mTc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and 99mTc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that 99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with 99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of 99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of 99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion 71110, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion 70013, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion 71110, Greece
| | - Emmanouil Koltsakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm 17177, Sweden
| | - Georgios Kalarakis
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm 17177, Sweden
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm 14152, Sweden
| | - Kiril Trpkov
- Alberta Precision Labs, Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB T2L 2K5, Canada
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK
- Department of Clinical Pathology, Vestre Viken Hospital Trust, Drammen 3004, Norway
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion 71110, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion 70013, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Heraklion 71110, Greece
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm 14152, Sweden
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Huddinge, Stockholm 14186, Sweden
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Klontzas ME, Triantafyllou M, Leventis D, Koltsakis E, Kalarakis G, Tzortzakakis A, Karantanas AH. Radiomics Analysis for Multiple Myeloma: A Systematic Review with Radiomics Quality Scoring. Diagnostics (Basel) 2023; 13:2021. [PMID: 37370916 DOI: 10.3390/diagnostics13122021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Multiple myeloma (MM) is one of the most common hematological malignancies affecting the bone marrow. Radiomics analysis has been employed in the literature in an attempt to evaluate the bone marrow of MM patients. This manuscript aimed to systematically review radiomics research on MM while employing a radiomics quality score (RQS) to accurately assess research quality in the field. A systematic search was performed on Web of Science, PubMed, and Scopus. The selected manuscripts were evaluated (data extraction and RQS scoring) by three independent readers (R1, R2, and R3) with experience in radiomics analysis. A total of 23 studies with 2682 patients were included, and the median RQS was 10 for R1 (IQR 5.5-12) and R3 (IQR 8.3-12) and 11 (IQR 7.5-12.5) for R2. RQS was not significantly correlated with any of the assessed bibliometric data (impact factor, quartile, year of publication, and imaging modality) (p > 0.05). Our results demonstrated the low quality of published radiomics research in MM, similarly to other fields of radiomics research, highlighting the need to tighten publication standards.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71003 Heraklion, Greece
| | | | - Dimitrios Leventis
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
| | - Emmanouil Koltsakis
- Department of Radiology, Karolinska University Hospital, 14152 Stockholm, Sweden
| | - Georgios Kalarakis
- Department of Radiology, Karolinska University Hospital, 14152 Stockholm, Sweden
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14152 Stockholm, Sweden
| | - Antonios Tzortzakakis
- Division of Radiology, Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14152 Stockholm, Sweden
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, 14186 Huddinge, Stockholm, Sweden
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71003 Heraklion, Greece
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Tzortzakakis A, Kalarakis G, Huang B, Terezaki E, Koltsakis E, Kechagias A, Tsekrekos A, Rouvelas I. Role of Radiology in the Preoperative Detection of Arterial Calcification and Celiac Trunk Stenosis and Its Association with Anastomotic Leakage Post Esophagectomy, an Up-to-Date Review of the Literature. Cancers (Basel) 2022; 14:cancers14041016. [PMID: 35205764 PMCID: PMC8870074 DOI: 10.3390/cancers14041016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/10/2022] [Accepted: 02/15/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Esophageal cancer is the sixth deadliest among all cancers worldwide. Multimodal treatment, including surgical resection of the esophagus, offers the potential for cure even in advanced cases, but esophagectomy is still associated with serious complications. Among these, anastomotic leakage has the most significant clinical impact, both in terms of prognosis and health-related quality of life. Identifying patients at a high risk for leakage is of great importance in order to modify their treatment and, if possible, avoid this complication. This review aims to study the current literature regarding the role of radiology in detecting potential risk factors associated with anastomotic leakage. The measurement of calcium plaques on the aorta, as well as the detection of narrowing of the celiac trunk and its branches, can be easily assessed by preoperative computed tomography, and can be used to individualize perioperative patient management to effectively reduce the rate of leakage. Abstract Surgical resection of the esophagus remains a critical component of the multimodal treatment of esophageal cancer. Anastomotic leakage (AL) is the most significant complication following esophagectomy, in terms of clinical implications. Identifying risk factors for AL is important for modifying patient management and improving surgical outcomes. This review aims to examine the role of radiological risk factors for AL after esophagectomy, and in particular, arterial calcification and celiac trunk stenosis. Eligible publications prior to 25 August 2021 were retrieved from Medline and Google Scholar using a predefined search algorithm. A total of 68 publications were identified, of which 9 original studies remained for in-depth analysis. The majority of these studies found correlations between calcifications in the aorta, celiac trunk, and right post-celiac arteries and AL following esophagectomy. Some studies suggest celiac trunk stenosis as a more appropriate surrogate. Our up-to-date review highlights the need for automated quantification of aortic calcifications, as well as the degree of celiac trunk stenosis in preoperative computed tomography in patients undergoing esophagectomy, to obtain robust and reproducible measurements that can be used for a definite correlation.
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Affiliation(s)
- Antonios Tzortzakakis
- Department of Clinical Science, Division of Radiology, Intervention and Technology (CLINTEC), Karolinska Institutet, 141 86 Stockholm, Sweden; (A.T.); (G.K.)
- Medical Radiation Physics and Nuclear Medicine, Functional Unit of Nuclear Medicine, Karolinska University Hospital, 141 86 Huddinge, Sweden
| | - Georgios Kalarakis
- Department of Clinical Science, Division of Radiology, Intervention and Technology (CLINTEC), Karolinska Institutet, 141 86 Stockholm, Sweden; (A.T.); (G.K.)
- Department of Radiology, Karolinska University Hospital, Huddinge, 141 86 Stockholm, Sweden
| | - Biying Huang
- Department of Upper Abdominal Surgery, Karolinska University Hospital Huddinge, 141 86 Stockholm, Sweden; (B.H.); (A.T.)
- Department of General Surgery, Södertälje Hospital, 152 86 Södertälje, Sweden
| | - Eleni Terezaki
- Department of Emergency Medicine, Karolinska University Hospital, 171 64 Stockholm, Sweden;
| | - Emmanouil Koltsakis
- Department of Radiology, Karolinska University Hospital, Solna, 171 64 Stockholm, Sweden;
| | - Aristotelis Kechagias
- Department of Digestive Surgery, Kanta-Häme Central Hospital, 13530 Hämeenlinna, Finland;
| | - Andrianos Tsekrekos
- Department of Upper Abdominal Surgery, Karolinska University Hospital Huddinge, 141 86 Stockholm, Sweden; (B.H.); (A.T.)
- Department of Clinical Science, Division of Surgery, Intervention and Technology (CLINTEC), Karolinska Institutet, 141 86 Stockholm, Sweden
| | - Ioannis Rouvelas
- Department of Upper Abdominal Surgery, Karolinska University Hospital Huddinge, 141 86 Stockholm, Sweden; (B.H.); (A.T.)
- Department of Clinical Science, Division of Surgery, Intervention and Technology (CLINTEC), Karolinska Institutet, 141 86 Stockholm, Sweden
- Correspondence: ; Tel.: +46-70-797-68-14
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Abstract
Morton’s neuroma is a painful lesion of the interdigital nerve, usually at the third intermetatarsal space, associated with fibrotic changes in the nerve, microvascular degeneration, and deregulation of sympathetic innervation. Patients usually present with burning or sharp metatarsalgia at the dorsal or plantar aspect of the foot. The management of Morton’s neuroma starts with conservative measures, usually with limited efficacy, including orthotics and anti-inflammatory medication. When conservative treatment fails, a series of minimally invasive ultrasound-guided procedures can be employed as second-line treatments prior to surgery. Such procedures include infiltration of the area with a corticosteroid and local anesthetic, chemical neurolysis with alcohol or radiofrequency thermal neurolysis. Ultrasound aids in the accurate diagnosis of Morton’s neuroma and guides the aforementioned treatment, so that significant and potentially long-lasting pain reduction can be achieved. In cases of initial treatment failure, the procedure can be repeated, usually leading to the complete remission of symptoms. Current data shows that minimally invasive treatments can significantly reduce the need for subsequent surgery in patients with persistent Morton’s neuroma unresponsive to conservative measures. The purpose of this review is to present current data on the application of ultrasound for the diagnosis and treatment of Morton’s neuroma, with emphasis on the outcomes of ultrasound-guided treatments.
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Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.,Advanced Hybrid Imaging Systems, Institute of Computer Science, FORTH, Crete, Greece.,Department of Radiology, School of Medicine, University of Crete, Greece
| | - Emmanouil Koltsakis
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
| | - George A Kakkos
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.,Advanced Hybrid Imaging Systems, Institute of Computer Science, FORTH, Crete, Greece.,Department of Radiology, School of Medicine, University of Crete, Greece
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Kakkos GA, Klontzas ME, Koltsakis E, Karantanas AH. US-guided high-volume injection for Achilles tendinopathy. J Ultrason 2021; 21:e127-e133. [PMID: 34258037 PMCID: PMC8264817 DOI: 10.15557/jou.2021.0021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/16/2021] [Indexed: 11/22/2022] Open
Abstract
Achilles tendinopathy is a common overuse condition affecting the adult population. The incidence is on the rise because of greater participation of people in recreational or competitive sporting activities. Chronic Achilles tendinopathy occurs most commonly in the tendon’s mid-portion, and it is challenging to manage, leading to significant patient morbidity. Despite conservative management many patients still require surgical intervention. The mechanism underlying pain is not entirely understood; however, high-resolution color Doppler ultrasound has shown that neovascularisation could be involved. Minimally-invasive treatments for chronic Achilles tendinopathy may prevent the need for surgery when conservative methods have failed. Ultrasound provides an option to guide therapeutic interventions accurately, so that treatment is delivered to the desired site of pathology. High-volume image-guided injection is a relatively new technique where a high volume of liquid is injected between the anterior aspect of the Achilles tendon and the Kager’s fat pad, used to strip away the neovascularity and disrupt the nerve ingrowth seen in chronic cases of Achilles tendinopathy. High-volume image-guided injection has shown promising results in terms of reducing pain and improving function in patients where conservative measures have failed. This review aims to describe the fundamental technical factors, and investigate the efficacy of high-volume image-guided injection with reference to the available literature.
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Affiliation(s)
- George A Kakkos
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.,Advanced Hybrid Imaging Systems, Institute of Computer Science, FORTH, Crete, Greece.,Department of Radiology, School of Medicine, University of Crete, Greece
| | - Emmanouil Koltsakis
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.,Advanced Hybrid Imaging Systems, Institute of Computer Science, FORTH, Crete, Greece.,Department of Radiology, School of Medicine, University of Crete, Greece
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