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García-Figueiras R, Oleaga L, Broncano J, Tardáguila G, Fernández-Pérez G, Vañó E, Santos-Armentia E, Méndez R, Luna A, Baleato-González S. What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future? J Imaging 2024; 10:154. [PMID: 39057725 PMCID: PMC11278514 DOI: 10.3390/jimaging10070154] [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: 05/19/2024] [Revised: 06/09/2024] [Accepted: 06/14/2024] [Indexed: 07/28/2024] Open
Abstract
Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual-energy imaging remains relatively untapped, possibly due to its intricate workflow and the intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is necessary for an appropriate clinical use. The aims of this paper are to review the physical and technical bases of DECT acquisition and analysis, to discuss the advantages and limitations of DECT in different clinical scenarios, to review the technical constraints in material labeling and quantification, and to evaluate the cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radiogenomics.
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Affiliation(s)
- Roberto García-Figueiras
- Department of Radiology, Hospital Clínico Universitario de Santiago, Choupana, 15706 Santiago de Compostela, Spain
| | - Laura Oleaga
- Department of Radiology, Hospital Clinic, C. de Villarroel, 170, 08036 Barcelona, Spain
| | | | - Gonzalo Tardáguila
- Department of Radiology, Hospital Ribera Povisa, Rúa de Salamanca, 5, Vigo, 36211 Pontevedra, Spain
| | | | - Eliseo Vañó
- Department of Radiology, Hospital Universitario Nuestra Señora, del Rosario, C. del Príncipe de Vergara, 53, 28006 Madrid, Spain
| | - Eloísa Santos-Armentia
- Department of Radiology, Hospital Ribera Povisa, Rúa de Salamanca, 5, Vigo, 36211 Pontevedra, Spain
| | - Ramiro Méndez
- Department of Radiology, Hospital Universitario Nuestra Señora, del Rosario, C. del Príncipe de Vergara, 53, 28006 Madrid, Spain
- Department of Radiology, Hospital Universitario Clínico San Carlos, Calle del Prof Martín Lagos, 28040 Madrid, Spain
| | | | - Sandra Baleato-González
- Department of Radiology, Hospital Clínico Universitario de Santiago, Choupana, 15706 Santiago de Compostela, Spain
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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Zama S, Fujioka T, Yamaga E, Kubota K, Mori M, Katsuta L, Yashima Y, Sato A, Kawauchi M, Higuchi S, Kawanishi M, Ishiba T, Oda G, Nakagawa T, Tateishi U. Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network. MEDICINA (KAUNAS, LITHUANIA) 2023; 60:14. [PMID: 38276048 PMCID: PMC10817540 DOI: 10.3390/medicina60010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/10/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND OBJECTIVES This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. MATERIALS AND METHODS We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. RESULTS The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. CONCLUSION The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.
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Affiliation(s)
- Shu Zama
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-koshigaya, Koshigaya 343-8555, Japan
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Yuka Yashima
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-koshigaya, Koshigaya 343-8555, Japan
| | - Arisa Sato
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Miho Kawauchi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Subaru Higuchi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Masaaki Kawanishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Toshiyuki Ishiba
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan
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Perrella A, Bagnacci G, Di Meglio N, Di Martino V, Mazzei MA. Thoracic Diseases: Technique and Applications of Dual-Energy CT. Diagnostics (Basel) 2023; 13:2440. [PMID: 37510184 PMCID: PMC10378112 DOI: 10.3390/diagnostics13142440] [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: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Dual-energy computed tomography (DECT) is one of the most promising technological innovations made in the field of imaging in recent years. Thanks to its ability to provide quantitative and reproducible data, and to improve radiologists' confidence, especially in the less experienced, its applications are increasing in number and variety. In thoracic diseases, DECT is able to provide well-known benefits, although many recent articles have sought to investigate new perspectives. This narrative review aims to provide the reader with an overview of the applications and advantages of DECT in thoracic diseases, focusing on the most recent innovations. The research process was conducted on the databases of Pubmed and Cochrane. The article is organized according to the anatomical district: the review will focus on pleural, lung parenchymal, breast, mediastinal, lymph nodes, vascular and skeletal applications of DECT. In conclusion, considering the new potential applications and the evidence reported in the latest papers, DECT is progressively entering the daily practice of radiologists, and by reading this simple narrative review, every radiologist will know the state of the art of DECT in thoracic diseases.
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Affiliation(s)
- Armando Perrella
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Giulio Bagnacci
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Nunzia Di Meglio
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Vito Di Martino
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
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Toshima F, Yoneda N, Terada K, Inoue D, Gabata T. DECT Numbers in Upper Abdominal Organs for Differential Diagnosis: A Feasibility Study. Tomography 2022; 8:2698-2708. [PMID: 36412684 PMCID: PMC9680450 DOI: 10.3390/tomography8060225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/01/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022] Open
Abstract
Evaluating the similarity between two entities such as primary and suspected metastatic lesions using quantitative dual-energy computed tomography (DECT) numbers may be useful. However, the criteria for the similarity between two entities based on DECT numbers remain unclear. We therefore considered the possibility that a similarity in DECT numbers within the same organ could provide suitable standards. Thus, we assumed that the variation in DECT numbers within a single organ is sufficiently minimal to be considered clinically equivalent. Therefore, the purpose of this preliminary study is to investigate the differences in DECT numbers within upper abdominal organs. This retrospective study included 30 patients with data from hepatic protocol DECT scans. DECT numbers of the following parameters were collected: (a, b) 70 and 40 keV CT values, (c) slope, (d) effective Z, and (e, f) iodine and water concentration. The agreement of DECT numbers obtained from two regions of interest in the same organ (liver, spleen, and kidney) were assessed using Bland-Altman analysis. The diagnostic ability of each DECT parameter to distinguish between the same or different organs was also assessed using receiver operating characteristic analysis. The 95% limits of agreement within the same organ exhibited the narrowest value range on delayed phase (DP) CT [(c) -11.2-8.3%, (d) -2.0-1.5%, (e) -11.3-8.4%, and (f) -0.59-0.62%]. The diagnostic ability was notably high when using differences in DECT numbers on portal venous (PVP) and DP images (the area under the curve of DP: 0.987-0.999 in (c)-(f)). Using the variability in DECT numbers in the same organ as a criterion for defining similarity may be helpful in making a differential diagnosis by comparing the DECT numbers of two entities.
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