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Ali M, Benfante V, Di Raimondo D, Laudicella R, Tuttolomondo A, Comelli A. A Review of Advances in Molecular Imaging of Rheumatoid Arthritis: From In Vitro to Clinic Applications Using Radiolabeled Targeting Vectors with Technetium-99m. Life (Basel) 2024; 14:751. [PMID: 38929734 PMCID: PMC11204982 DOI: 10.3390/life14060751] [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: 04/22/2024] [Revised: 05/24/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in synovitis and pannus formation. Timely detection and effective management of RA are pivotal for mitigating inflammatory arthritis consequences, potentially influencing disease progression. Nuclear medicine using radiolabeled targeted vectors presents a promising avenue for RA diagnosis and response to treatment assessment. Radiopharmaceutical such as technetium-99m (99mTc), combined with single photon emission computed tomography (SPECT) combined with CT (SPECT/CT), introduces a more refined diagnostic approach, enhancing accuracy through precise anatomical localization, representing a notable advancement in hybrid molecular imaging for RA evaluation. This comprehensive review discusses existing research, encompassing in vitro, in vivo, and clinical studies to explore the application of 99mTc radiolabeled targeting vectors with SPECT imaging for RA diagnosis. The purpose of this review is to highlight the potential of this strategy to enhance patient outcomes by improving the early detection and management of RA.
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Affiliation(s)
- Muhammad Ali
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (M.A.); (A.C.)
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Viviana Benfante
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (M.A.); (A.C.)
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Domenico Di Raimondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Riccardo Laudicella
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, Messina University, 98124 Messina, Italy;
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (M.A.); (A.C.)
- NBFC—National Biodiversity Future Center, 90133 Palermo, Italy
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Ali M, Benfante V, Di Raimondo D, Salvaggio G, Tuttolomondo A, Comelli A. Recent Developments in Nanoparticle Formulations for Resveratrol Encapsulation as an Anticancer Agent. Pharmaceuticals (Basel) 2024; 17:126. [PMID: 38256959 PMCID: PMC10818631 DOI: 10.3390/ph17010126] [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: 12/19/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
Resveratrol is a polyphenolic compound that has gained considerable attention in the past decade due to its multifaceted therapeutic potential, including anti-inflammatory and anticancer properties. However, its anticancer efficacy is impeded by low water solubility, dose-limiting toxicity, low bioavailability, and rapid hepatic metabolism. To overcome these hurdles, various nanoparticles such as organic and inorganic nanoparticles, liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles, gold nanoparticles, zinc oxide nanoparticles, zeolitic imidazolate frameworks, carbon nanotubes, bioactive glass nanoparticles, and mesoporous nanoparticles were employed to deliver resveratrol, enhancing its water solubility, bioavailability, and efficacy against various types of cancer. Resveratrol-loaded nanoparticle or resveratrol-conjugated nanoparticle administration exhibits excellent anticancer potency compared to free resveratrol. This review highlights the latest developments in nanoparticle-based delivery systems for resveratrol, focusing on the potential to overcome limitations associated with the compound's bioavailability and therapeutic effectiveness.
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Affiliation(s)
- Muhammad Ali
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy;
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Viviana Benfante
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy;
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Domenico Di Raimondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Giuseppe Salvaggio
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy;
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy; (D.D.R.); (A.T.)
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy;
- National Biodiversity Future Center (NBFC), 90133 Palermo, Italy
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Scavuzzo A, Pasini G, Crescio E, Jimenez-Rios MA, Figueroa-Rodriguez P, Comelli A, Russo G, Vazquez IC, Araiza SM, Ortiz DG, Perez Montiel D, Lopez Saavedra A, Stefano A. Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection. J Imaging 2023; 9:213. [PMID: 37888320 PMCID: PMC10607637 DOI: 10.3390/jimaging9100213] [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: 08/30/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. AIM To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. METHODS Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models' performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. RESULT Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. CONCLUSIONS The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.
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Affiliation(s)
- Anna Scavuzzo
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Giovanni Pasini
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (G.P.); (G.R.)
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
| | - Elisabetta Crescio
- Science Department, Tecnológico de Monterrey, Mexico City 14080, Mexico;
| | - Miguel Angel Jimenez-Rios
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Pavel Figueroa-Rodriguez
- Department of Biomedical Engineering, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy;
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (G.P.); (G.R.)
| | - Ivan Calvo Vazquez
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Sebastian Muruato Araiza
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - David Gomez Ortiz
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Delia Perez Montiel
- Department of Pathology, Instituto Nacional de Cancerología, Mexico City 14080, Mexico
| | - Alejandro Lopez Saavedra
- Advanced Microscopy Applications Unit (ADMiRA), Instituto Nacional de Cancerología, Mexico City 14080, Mexico
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (G.P.); (G.R.)
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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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Lo Casto A, Spartivento G, Benfante V, Di Raimondo R, Ali M, Di Raimondo D, Tuttolomondo A, Stefano A, Yezzi A, Comelli A. Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs. Life (Basel) 2023; 13:1441. [PMID: 37511816 PMCID: PMC10381483 DOI: 10.3390/life13071441] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/30/2023] Open
Abstract
The purpose of this investigation was to evaluate the diagnostic performance of two convolutional neural networks (CNNs), namely ResNet-152 and VGG-19, in analyzing, on panoramic images, the rapport that exists between the lower third molar (MM3) and the mandibular canal (MC), and to compare this performance with that of an inexperienced observer (a sixth year dental student). Utilizing the k-fold cross-validation technique, 142 MM3 images, cropped from 83 panoramic images, were split into 80% as training and validation data and 20% as test data. They were subsequently labeled by an experienced radiologist as the gold standard. In order to compare the diagnostic capabilities of CNN algorithms and the inexperienced observer, the diagnostic accuracy, sensitivity, specificity, and positive predictive value (PPV) were determined. ResNet-152 achieved a mean sensitivity, specificity, PPV, and accuracy, of 84.09%, 94.11%, 92.11%, and 88.86%, respectively. VGG-19 achieved 71.82%, 93.33%, 92.26%, and 85.28% regarding the aforementioned characteristics. The dental student's diagnostic performance was respectively 69.60%, 53.00%, 64.85%, and 62.53%. This work demonstrated the potential use of deep CNN architecture for the identification and evaluation of the contact between MM3 and MC in panoramic pictures. In addition, CNNs could be a useful tool to assist inexperienced observers in more accurately identifying contact relationships between MM3 and MC on panoramic images.
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Affiliation(s)
- Antonio Lo Casto
- Section of Radiological Sciences, Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy
| | - Giacomo Spartivento
- Section of Radiological Sciences, Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy
| | - Viviana Benfante
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Riccardo Di Raimondo
- Postgraduate Section of Periodontology, Faculty of Odontology, University Complutense, 28040 Madrid, Spain
- Postgraduate Section of Oral Surgery, Periodontology and Implant, University Sur Mississippi, Spain Istitutions, 28040 Madrid, Spain
| | - Muhammad Ali
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
| | - Domenico Di Raimondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Anthony Yezzi
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
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Cammarata FP, Torrisi F, Vicario N, Bravatà V, Stefano A, Salvatorelli L, D'Aprile S, Giustetto P, Forte GI, Minafra L, Calvaruso M, Richiusa S, Cirrone GAP, Petringa G, Broggi G, Cosentino S, Scopelliti F, Magro G, Porro D, Libra M, Ippolito M, Russo G, Parenti R, Cuttone G. Proton boron capture therapy (PBCT) induces cell death and mitophagy in a heterotopic glioblastoma model. Commun Biol 2023; 6:388. [PMID: 37031346 PMCID: PMC10082834 DOI: 10.1038/s42003-023-04770-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/28/2023] [Indexed: 04/10/2023] Open
Abstract
Despite aggressive therapeutic regimens, glioblastoma (GBM) represents a deadly brain tumor with significant aggressiveness, radioresistance and chemoresistance, leading to dismal prognosis. Hypoxic microenvironment, which characterizes GBM, is associated with reduced therapeutic effectiveness. Moreover, current irradiation approaches are limited by uncertain tumor delineation and severe side effects that comprehensively lead to unsuccessful treatment and to a worsening of the quality of life of GBM patients. Proton beam offers the opportunity of reduced side effects and a depth-dose profile, which, unfortunately, are coupled with low relative biological effectiveness (RBE). The use of radiosensitizing agents, such as boron-containing molecules, enhances proton RBE and increases the effectiveness on proton beam-hit targets. We report a first preclinical evaluation of proton boron capture therapy (PBCT) in a preclinical model of GBM analyzed via μ-positron emission tomography/computed tomography (μPET-CT) assisted live imaging, finding a significant increased therapeutic effectiveness of PBCT versus proton coupled with an increased cell death and mitophagy. Our work supports PBCT and radiosensitizing agents as a scalable strategy to treat GBM exploiting ballistic advances of proton beam and increasing therapeutic effectiveness and quality of life in GBM patients.
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Affiliation(s)
- Francesco Paolo Cammarata
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
- National Institute for Nuclear Physics, Laboratori Nazionali del Sud, INFN-LNS, Catania, Italy
| | - Filippo Torrisi
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Nunzio Vicario
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
- Molecular Preclinical and Translational Imaging Research Center - IMPRonTe, University of Catania, Catania, Italy
| | - Valentina Bravatà
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | - Lucia Salvatorelli
- Department G.F. Ingrassia, Azienda Ospedaliero-Universitaria "Policlinico-Vittorio Emanuele" Anatomic Pathology, University of Catania, Catania, Italy
| | - Simona D'Aprile
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Pierangela Giustetto
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Giusi Irma Forte
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | - Luigi Minafra
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | - Marco Calvaruso
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | - Selene Richiusa
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | | | - Giada Petringa
- National Institute for Nuclear Physics, Laboratori Nazionali del Sud, INFN-LNS, Catania, Italy
| | - Giuseppe Broggi
- Department G.F. Ingrassia, Azienda Ospedaliero-Universitaria "Policlinico-Vittorio Emanuele" Anatomic Pathology, University of Catania, Catania, Italy
| | | | - Fabrizio Scopelliti
- Radiopharmacy Laboratory Nuclear Medicine Department, Cannizzaro Hospital, Catania, Italy
| | - Gaetano Magro
- Department G.F. Ingrassia, Azienda Ospedaliero-Universitaria "Policlinico-Vittorio Emanuele" Anatomic Pathology, University of Catania, Catania, Italy
| | - Danilo Porro
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy
| | - Massimo Libra
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Massimo Ippolito
- Nuclear Medicine Department, Cannizzaro Hospital, Catania, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council, IBFM-CNR, Cefalù, Italy.
- National Institute for Nuclear Physics, Laboratori Nazionali del Sud, INFN-LNS, Catania, Italy.
| | - Rosalba Parenti
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
- Molecular Preclinical and Translational Imaging Research Center - IMPRonTe, University of Catania, Catania, Italy.
| | - Giacomo Cuttone
- National Institute for Nuclear Physics, Laboratori Nazionali del Sud, INFN-LNS, Catania, Italy
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Benfante V, Stefano A, Ali M, Laudicella R, Arancio W, Cucchiara A, Caruso F, Cammarata FP, Coronnello C, Russo G, Miele M, Vieni A, Tuttolomondo A, Yezzi A, Comelli A. An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics. Diagnostics (Basel) 2023; 13:diagnostics13071210. [PMID: 37046428 PMCID: PMC10093267 DOI: 10.3390/diagnostics13071210] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/11/2023] [Accepted: 03/17/2023] [Indexed: 04/14/2023] Open
Abstract
Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on 64Cu-, 68Ga-, 125I-, and 99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.
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Affiliation(s)
- Viviana Benfante
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Muhammad Ali
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
| | | | - Walter Arancio
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Antonino Cucchiara
- Department of Diagnostic and Therapeutic Services, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy
| | - Fabio Caruso
- Department of Diagnostic and Therapeutic Services, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy
| | - Francesco Paolo Cammarata
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Claudia Coronnello
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
- National Biodiversity Future Center (NBFC), 90133 Palermo, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
- National Biodiversity Future Center (NBFC), 90133 Palermo, Italy
| | - Monica Miele
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Alessandra Vieni
- Department of Diagnostic and Therapeutic Services, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy
| | - Anthony Yezzi
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
- National Biodiversity Future Center (NBFC), 90133 Palermo, Italy
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Cheng X, Yang D, Saha D, Sun X, Shao Y. Integrated Small Animal PET/CT/RT with Onboard PET/CT Image Guidance for Preclinical Radiation Oncology Research. Tomography 2023; 9:567-578. [PMID: 36961005 PMCID: PMC10037572 DOI: 10.3390/tomography9020046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/22/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
We have integrated a compact and lightweight PET with an existing CT image-guided small animal irradiator to enable practical onboard PET/CT image-guided preclinical radiation therapy (RT) research. The PET with a stationary and full-ring detectors has ~1.1 mm uniform spatial resolution over its imaging field-of-view of 8.0 cm diameter and 3.5 cm axial length and was mechanically installed inside the irradiator in a tandem configuration with CT and radiation unit. A common animal bed was used for acquiring sequential dual functional and anatomical images with independent PET and CT control and acquisition systems. The reconstructed dual images were co-registered based on standard multi-modality image calibration and registration processes. Phantom studies were conducted to evaluate the integrated system and dual imaging performance. The measured mean PET/CT image registration error was ~0.3 mm. With one-bed and three-bed acquisitions, initial tumor focused and whole-body [18F]FDG animal images were acquired to test the capability of onboard PET/CT image guidance for preclinical RT research. Overall, the results have shown that integrated PET/CT/RT can provide advantageous and practical onboard PET/CT image to significantly enhance the accuracy of tumor delineation and radiation targeting that should enhance the existing and enable new and potentially breakthrough preclinical RT research and applications.
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Affiliation(s)
- Xinyi Cheng
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Dongxu Yang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Debabrata Saha
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xiankai Sun
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yiping Shao
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022; 12:16712. [PMID: 36202934 PMCID: PMC9537186 DOI: 10.1038/s41598-022-20703-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.
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Image Processing and Analysis for Preclinical and Clinical Applications. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Preclinical and clinical imaging aims to characterize and measure biological processes and diseases in animals [...]
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Tumakov D, Kayumov Z, Zhumaniezov A, Chikrin D, Galimyanov D. Elimination of Defects in Mammograms Caused by a Malfunction of the Device Matrix. J Imaging 2022; 8:jimaging8050128. [PMID: 35621892 PMCID: PMC9143204 DOI: 10.3390/jimaging8050128] [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: 02/28/2022] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 11/22/2022] Open
Abstract
Today, the processing and analysis of mammograms is quite an important field of medical image processing. Small defects in images can lead to false conclusions. This is especially true when the distortion occurs due to minor malfunctions in the equipment. In the present work, an algorithm for eliminating a defect is proposed, which includes a change in intensity on a mammogram and deteriorations in the contrast of individual areas. The algorithm consists of three stages. The first is the defect identification stage. The second involves improvement and equalization of the contrasts of different parts of the image outside the defect. The third involves restoration of the defect area via a combination of interpolation and an artificial neural network. The mammogram obtained as a result of applying the algorithm shows significantly better image quality and does not contain distortions caused by changes in brightness of the pixels. The resulting images are evaluated using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Naturalness Image Quality Evaluator (NIQE) metrics. In total, 98 radiomics features are extracted from the original and obtained images, and conclusions are drawn about the minimum changes in features between the original image and the image obtained by the proposed algorithm.
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Affiliation(s)
- Dmitrii Tumakov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
- Correspondence:
| | - Zufar Kayumov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
| | - Alisher Zhumaniezov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
| | - Dmitry Chikrin
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
| | - Diaz Galimyanov
- Medical Unit, Department of Radiology, Kazan Federal University, 420008 Kazan, Russia;
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