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Riley BA, Stevens JB, Li X, Yang Z, Wang C, Mowery YM, Brizel DM, Yin FF, Lafata KJ. Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma. J Med Imaging (Bellingham) 2024; 11:024007. [PMID: 38549835 PMCID: PMC10966359 DOI: 10.1117/1.jmi.11.2.024007] [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: 09/06/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. Approach A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N = 69 ; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model. Results FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p = 0.0158 and p = 0.0180 , respectively; log-rank test). Conclusions Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.
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Affiliation(s)
- Breylon A. Riley
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Jack B. Stevens
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Xiang Li
- Duke University Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
| | - Zhenyu Yang
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Chunhao Wang
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Yvonne M. Mowery
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- University of Pittsburgh, UPMC Hillman Cancer Center, Department of Radiation Oncology, Pittsburgh, North Carolina, United States
| | - David M. Brizel
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University School of Medicine, Department of Head and Neck Surgery and Communication Sciences, Durham, North Carolina, United States
| | - Fang-Fang Yin
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Kyle J. Lafata
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
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Owens TC, Anton N, Attia MF. CT and X-ray contrast agents: Current clinical challenges and the future of contrast. Acta Biomater 2023; 171:19-36. [PMID: 37739244 DOI: 10.1016/j.actbio.2023.09.027] [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: 07/29/2023] [Revised: 09/05/2023] [Accepted: 09/17/2023] [Indexed: 09/24/2023]
Abstract
Computed tomography (CT) is a powerful and widely used imaging technique in modern medicine. However, it often requires the use of contrast agents to visualize structures with similar radiographic density. Unfortunately, current clinical contrast agents (CAs) for CT have remained largely unchanged for decades and come with several significant drawbacks, including serious nephrotoxicity and short circulation half-lives. The next generation of CT radiocontrast agents should strive to be long-circulating, non-toxic, and non-immunogenic. Nanoparticle contrast agents have shown promise in recent years and are likely to comprise the majority of next-generation CT contrast agents. This review highlights the fundamental mechanism and background of X-ray and contrast agents. It also focuses on the challenges associated with current clinical contrast agents and provides a brief overview of potential future agents that are based on various materials such as lipids, polymers, dendrimers, metallic, and non-metallic inorganic nanoparticles (NPs). STATEMENT OF SIGNIFICANCE: We realized a need for clarification on a number of concerns related to the use of iodinated contrast material as debates regarding the safety of these agents with patients with kidney disease, shellfish allergies, and thyroid dysfunction remain ongoing in medical practice. This review was partially inspired by debates witnessed in medical practice regarding outdated misconceptions of contrast material that warrant clarification in translational and clinical arenas. Given that conversation around currently available agents is at somewhat of a high water mark, and nanoparticle research has now reached an unprecedented number of readers, we find that this review is timely and unique in the context of recent discussions in the field.
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Affiliation(s)
- Tyler C Owens
- Center for Nanotechnology in Drug Delivery and Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Nicolas Anton
- Université de Strasbourg, INSERM, Regenerative Nanomedicine UMR 1260, Centre de Recherche en Biomédecine de Strasbourg (CRBS), F-67000 Strasbourg, France
| | - Mohamed F Attia
- Center for Nanotechnology in Drug Delivery and Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599, USA.
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Cong W, Wang G. Fluorescence molecular tomography for quantum yield and lifetime. APPLIED OPTICS 2023; 62:5926-5931. [PMID: 37706945 DOI: 10.1364/ao.495129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 07/09/2023] [Indexed: 09/15/2023]
Abstract
Fluorescence molecular tomography (FMT) is a promising modality for noninvasive imaging of internal fluorescence agents in biological tissues, especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data with photon-counting microcomputed tomography (PCMCT) images to estimate the quantum yield and lifetime of fluorescent markers in a mouse model. By incorporating PCMCT images, a permissible region of interest of fluorescence yield and lifetime can be roughly estimated as prior knowledge, reducing the number of unknown variables in the inverse problem and improving the image reconstruction stability. Our numerical experiments demonstrate the accuracy and stability of the proposed reconstruction method in the presence of data noise, achieving a reconstruction error of 0.02 ns for the fluorescence lifetime and an average relative error of 18% for quantum yield reconstruction.
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Monti S, Truppa ME, Albanese S, Mancini M. Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review. J Pers Med 2023; 13:1204. [PMID: 37623455 PMCID: PMC10455673 DOI: 10.3390/jpm13081204] [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: 06/09/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by exploring the relationship between imaging characteristics, genomic information, and outcomes that qualitative interpretations may have overlooked, offering valuable insights for personalized medicine. Preclinical research allows for a controlled environment where various aspects of a pathology can be replicated in animal models, providing radiomic and radiogenomic approaches with the unique opportunity to investigate the causal connection between imaging and molecular factors. The aim of this review is to present the current state of the art in the application of radiomics and radiogenomics on murine models. This review will provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings.
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Affiliation(s)
| | | | - Sandra Albanese
- National Research Council, Institute of Biostructures and Bioimaging, 80145 Naples, Italy; (S.M.); (M.E.T.); (M.M.)
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Zhang H. The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings. Tomography 2023; 9:931-941. [PMID: 37218936 DOI: 10.3390/tomography9030076] [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: 12/13/2022] [Revised: 01/31/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Abstract
Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members.
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Affiliation(s)
- Huiming Zhang
- Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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Kurz FT, Schlemmer HP. Imaging in translational cancer research. Cancer Biol Med 2022; 19:j.issn.2095-3941.2022.0677. [PMID: 36476372 PMCID: PMC9724222 DOI: 10.20892/j.issn.2095-3941.2022.0677] [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] [Indexed: 12/12/2022] Open
Abstract
This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.
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Affiliation(s)
- Felix T. Kurz
- Department of Radiology, German Cancer Research Center, Heidelberg 69120, Germany,Correspondence to: Felix T. Kurz and Heinz-Peter Schlemmer, E-mail: and
| | - Heinz-Peter Schlemmer
- Department of Radiology, German Cancer Research Center, Heidelberg 69120, Germany,Correspondence to: Felix T. Kurz and Heinz-Peter Schlemmer, E-mail: and
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Li Y, Younis MH, Wang H, Zhang J, Cai W, Ni D. Spectral computed tomography with inorganic nanomaterials: State-of-the-art. Adv Drug Deliv Rev 2022; 189:114524. [PMID: 36058350 PMCID: PMC9664656 DOI: 10.1016/j.addr.2022.114524] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/09/2022] [Accepted: 08/27/2022] [Indexed: 01/24/2023]
Abstract
Recently, spectral computed tomography (CT) technology has received great interest in the field of radiology. Spectral CT imaging utilizes the distinct, energy-dependent X-ray absorption properties of substances in order to provide additional imaging information. Dual-energy CT and multi-energy CT (Spectral CT) are capable of constructing monochromatic energy images, material separation images, energy spectrum curves, constructing effective atomic number maps, and more. However, poor contrast, due to neighboring X-ray attenuation of organs and tissues, is still a challenge to spectral CT. Hence, contrast agents (CAs) are applied for better differentiation of a given region of interest (ROI). Currently, many different kinds of inorganic nanoparticulate CAs for spectral CT have been developed due to the limitations of clinical iodine (I)-based contrast media, leading to the conclusion that inorganic nanomedicine applied to spectral CT will be a powerful collaboration both in basic research and in clinics. In this review, the underlying principles and types of spectral CT techniques are discussed, and some evolving clinical diagnosis applications of spectral CT techniques are introduced. In particular, recent developments in inorganic CAs used for spectral CT are summarized. Finally, the challenges and future developments of inorganic nanomedicine in spectral CT are briefly discussed.
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Affiliation(s)
- Yuhan Li
- School of Medicine, Shanghai University, No. 99 Shangda Rd, Shanghai 200444, PR China
| | - Muhsin H Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, WI 53705, United States
| | - Han Wang
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Rd, Shanghai 200025, PR China
| | - Jian Zhang
- School of Medicine, Shanghai University, No. 99 Shangda Rd, Shanghai 200444, PR China; Shanghai Universal Medical Imaging Diagnostic Center, Bldg 8, No. 406 Guilin Rd, Shanghai 200233, PR China.
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, WI 53705, United States.
| | - Dalong Ni
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Rd, Shanghai 200025, PR China.
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