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Georgiou MF, Sfakianaki E, Diaz-Kanelidis MN, Moshiree B. Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying. Diagnostics (Basel) 2024; 14:1240. [PMID: 38928655 PMCID: PMC11202747 DOI: 10.3390/diagnostics14121240] [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: 02/22/2024] [Revised: 06/01/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis. METHODS An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study. The model was trained and tested on a cohort of 1002 patients who underwent GES using a 70/30 stratified split ratio for training vs. testing. The ML software automated the generation of optimal predictive models by employing a combination of data preprocessing, appropriate feature selection, and predictive modeling analysis algorithms. RESULTS The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the predictive modeling performance. Several models were developed using different combinations of imaging time points as input features and methodologies to achieve optimal output. By using GE values at time points 0.5 h, 1 h, 1.5 h, 2 h, and 2.5 h as input predictors of the 4 h outcome, the analysis produced an AUC of 90.7% and a balanced accuracy (BA) of 80.0% on the test set. This performance was comparable to the training set results (AUC = 91.5%, BA = 84.7%) within the 95% confidence interval (CI), demonstrating a robust predictive capability. Through feature selection, it was discovered that the 2.5 h GE value alone was statistically significant enough to predict the 4 h outcome independently, with a slightly increased test set performance (AUC = 92.4%, BA = 83.3%), thus emphasizing its dominance as the primary predictor for delayed GE. ROC analysis was also performed for single time imaging points at 1 h and 2 h to assess their independent predictiveness of the 4 h outcome. Furthermore, the ML model was tested for its ability to predict "flipping" cases with normal GE at 1 h and 2 h that became abnormal with delayed GE at 4 h. CONCLUSIONS An AI/ML model was designed and trained for predicting delayed GE using a limited number of imaging time points in a 4 h GES clinical protocol. This study demonstrates the feasibility of employing ML for GES optimization in the detection of delayed GE and potentially shortening the protocol's time length without compromising diagnostic power.
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Affiliation(s)
- Michalis F. Georgiou
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA;
| | - Efrosyni Sfakianaki
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA;
| | | | - Baha Moshiree
- Atrium Health, Wake Forest University, Charlotte, NC 28204, USA
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Zhang L, Wong C, Li Y, Huang T, Wang J, Lin C. Artificial intelligence assisted diagnosis of early tc markers and its application. Discov Oncol 2024; 15:172. [PMID: 38761260 PMCID: PMC11102422 DOI: 10.1007/s12672-024-01017-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024] Open
Abstract
Thyroid cancer (TC) is a common endocrine malignancy with an increasing incidence worldwide. Early diagnosis is particularly important for TC patients, because it allows patients to receive treatment as early as possible. Artificial intelligence (AI) provides great advantages for complex healthcare systems by analyzing big data based on machine learning. Nowadays, AI is widely used in the early diagnosis of cancer such as TC. Ultrasound detection and fine needle aspiration biopsy are the main methods for early diagnosis of TC. AI has been widely used in the detection of malignancy in thyroid nodules by ultrasound images, cytopathology images and molecular markers. It shows great potential in auxiliary medical diagnosis. The latest clinical trial has shown that the performance of AI models matches with the diagnostic efficiency of experienced clinicians, and more efficient AI tools will be developed in the future. Therefore, in this review, we summarized the recent advances in the application of AI algorithms in assessing the risk of malignancy in thyroid nodules. The objective of this review was to provide a data base for the clinical use of AI-assisted diagnosis in TC, as well as to provide new ideas for the next generation of AI-assisted diagnosis in TC.
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Affiliation(s)
- Laney Zhang
- Yale School of Public Health, New Haven, CT, USA
| | - Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yungeng Li
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | | | - Jiawen Wang
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Chenghe Lin
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, Jilin, China.
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Ma KC, Mena E, Lindenberg L, Lay NS, Eclarinal P, Citrin DE, Pinto PA, Wood BJ, Dahut WL, Gulley JL, Madan RA, Choyke PL, Turkbey IB, Harmon SA. Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN. Oncotarget 2024; 15:288-300. [PMID: 38712741 DOI: 10.18632/oncotarget.28583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024] Open
Abstract
PURPOSE Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. METHODS A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. RESULTS Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). CONCLUSION The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.
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Affiliation(s)
- Kevin C Ma
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Esther Mena
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Liza Lindenberg
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nathan S Lay
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Phillip Eclarinal
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - James L Gulley
- Center for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter L Choyke
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ismail Baris Turkbey
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Mercolli L, Rominger A, Shi K. Towards quality management of artificial intelligence systems for medical applications. Z Med Phys 2024; 34:343-352. [PMID: 38413355 PMCID: PMC11156774 DOI: 10.1016/j.zemedi.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/29/2024]
Abstract
The use of artificial intelligence systems in clinical routine is still hampered by the necessity of a medical device certification and/or by the difficulty of implementing these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure robust model predictions and how to appraise the quality of a model's results on a regular basis. In this paper we discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment. We propose the methodology from AAPM Task Group 100 report No. 283 as a conceptual framework for developing risk-driven a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an example of a clinical workflow. Our analysis shows how the risk evaluation in this framework can accommodate artificial intelligence based systems independently of their robustness evaluation or the user's in-house expertise. In particular, we highlight how the degree of interpretability of a machine learning system can be systematically accounted for within the risk evaluation and in the development of a quality management system.
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Affiliation(s)
- Lorenzo Mercolli
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland.
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland
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Mansouri Z, Salimi Y, Akhavanallaf A, Shiri I, Teixeira EPA, Hou X, Beauregard JM, Rahmim A, Zaidi H. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [ 177Lu]Lu-DOTATATE radiopharmaceutical therapy. Eur J Nucl Med Mol Imaging 2024; 51:1516-1529. [PMID: 38267686 PMCID: PMC11043201 DOI: 10.1007/s00259-024-06618-9] [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: 11/13/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Eliluane Pirazzo Andrade Teixeira
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Xinchi Hou
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Jean-Mathieu Beauregard
- Cancer Research Centre and Department of Radiology and Nuclear Medicine, Université Laval, Quebec City, QC, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Slart RHJA, Bengel FM, Akincioglu C, Bourque JM, Chen W, Dweck MR, Hacker M, Malhotra S, Miller EJ, Pelletier-Galarneau M, Packard RRS, Schindler TH, Weinberg RL, Saraste A, Slomka PJ. Total-Body PET/CT Applications in Cardiovascular Diseases: A Perspective Document of the SNMMI Cardiovascular Council. J Nucl Med 2024:jnumed.123.266858. [PMID: 38388512 DOI: 10.2967/jnumed.123.266858] [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: 11/07/2023] [Accepted: 01/11/2024] [Indexed: 02/24/2024] Open
Abstract
Digital PET/CT systems with a long axial field of view have become available and are emerging as the current state of the art. These new camera systems provide wider anatomic coverage, leading to major increases in system sensitivity. Preliminary results have demonstrated improvements in image quality and quantification, as well as substantial advantages in tracer kinetic modeling from dynamic imaging. These systems also potentially allow for low-dose examinations and major reductions in acquisition time. Thereby, they hold great promise to improve PET-based interrogation of cardiac physiology and biology. Additionally, the whole-body coverage enables simultaneous assessment of multiple organs and the large vascular structures of the body, opening new opportunities for imaging systemic mechanisms, disorders, or treatments and their interactions with the cardiovascular system as a whole. The aim of this perspective document is to debate the potential applications, challenges, opportunities, and remaining challenges of applying PET/CT with a long axial field of view to the field of cardiovascular disease.
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Affiliation(s)
- Riemer H J A Slart
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands;
- Biomedical Photonic Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands
| | - Frank M Bengel
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Cigdem Akincioglu
- Division of Nuclear Medicine, Medical Imaging, Western University, London, Ontario, Canada
| | - Jamieson M Bourque
- Departments of Medicine (Cardiology) and Radiology, University of Virginia, Charlottesville, Virginia
| | - Wengen Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, Edinburgh Heart Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Edward J Miller
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, and Department of Internal Medicine, Yale University, New Haven, Connecticut
| | | | - René R S Packard
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Thomas H Schindler
- Mallinckrodt Institute of Radiology, Division of Nuclear Medicine, Cardiovascular Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Richard L Weinberg
- Division of Cardiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Antti Saraste
- Turku PET Centre and Heart Center, Turku University Hospital and University of Turku, Turku, Finland; and
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
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7
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Batarchuk V, Shepelytskyi Y, Grynko V, Kovacs AH, Hodgson A, Rodriguez K, Aldossary R, Talwar T, Hasselbrink C, Ruset IC, DeBoef B, Albert MS. Hyperpolarized Xenon-129 Chemical Exchange Saturation Transfer (HyperCEST) Molecular Imaging: Achievements and Future Challenges. Int J Mol Sci 2024; 25:1939. [PMID: 38339217 PMCID: PMC10856220 DOI: 10.3390/ijms25031939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 01/25/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024] Open
Abstract
Molecular magnetic resonance imaging (MRI) is an emerging field that is set to revolutionize our perspective of disease diagnosis, treatment efficacy monitoring, and precision medicine in full concordance with personalized medicine. A wide range of hyperpolarized (HP) 129Xe biosensors have been recently developed, demonstrating their potential applications in molecular settings, and achieving notable success within in vitro studies. The favorable nuclear magnetic resonance properties of 129Xe, coupled with its non-toxic nature, high solubility in biological tissues, and capacity to dissolve in blood and diffuse across membranes, highlight its superior role for applications in molecular MRI settings. The incorporation of reporters that combine signal enhancement from both hyperpolarized 129Xe and chemical exchange saturation transfer holds the potential to address the primary limitation of low sensitivity observed in conventional MRI. This review provides a summary of the various applications of HP 129Xe biosensors developed over the last decade, specifically highlighting their use in MRI. Moreover, this paper addresses the evolution of in vivo applications of HP 129Xe, discussing its potential transition into clinical settings.
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Affiliation(s)
- Viktoriia Batarchuk
- Chemistry Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; (V.B.)
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON P7B 6V4, Canada
| | - Yurii Shepelytskyi
- Chemistry Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; (V.B.)
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON P7B 6V4, Canada
| | - Vira Grynko
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON P7B 6V4, Canada
- Chemistry and Materials Science Program, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
| | - Antal Halen Kovacs
- Applied Life Science Program, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
| | - Aaron Hodgson
- Physics Program, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
| | - Karla Rodriguez
- Chemistry Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; (V.B.)
| | - Ruba Aldossary
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON P7B 6V4, Canada
| | - Tanu Talwar
- Chemistry Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; (V.B.)
| | - Carson Hasselbrink
- Chemistry & Biochemistry Department, California Polytechnic State University, San Luis Obispo, CA 93407-005, USA
| | | | - Brenton DeBoef
- Department of Chemistry, University of Rhode Island, Kingston, RI 02881, USA
| | - Mitchell S. Albert
- Chemistry Department, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; (V.B.)
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON P7B 6V4, Canada
- Faculty of Medical Sciences, Northern Ontario School of Medicine, Thunder Bay, ON P7B 5E1, Canada
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Zuckier LS, Boone SL. Is It Time to Retire PIOPED? J Nucl Med 2024; 65:13-15. [PMID: 37918867 DOI: 10.2967/jnumed.123.266186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/10/2023] [Indexed: 11/04/2023] Open
Affiliation(s)
- Lionel S Zuckier
- Division of Nuclear Medicine, Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York; and
| | - Sean Logan Boone
- Department of Radiology, St. Joseph's Hospital and Medical Center, Phoenix, Arizona
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Krishnamoorthy S, Surti S. Advances in Breast PET Instrumentation. PET Clin 2024; 19:37-47. [PMID: 37949606 DOI: 10.1016/j.cpet.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Dedicated breast PET scanners currently have a spatial resolution in the 1.5 to 2 mm range, and the ability to provide tomographic images and quantitative data. They are also commercially available from a few vendors. A review of past and recent advances in the development and performance of dedicated breast PET scanners is summarized.
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Affiliation(s)
- Srilalan Krishnamoorthy
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
| | - Suleman Surti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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10
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Zhang X, Zhong Y, Jin C, Hu D, Tian M, Zhang H. Medical image Generative Pre-Trained Transformer (MI-GPT): future direction for precision medicine. Eur J Nucl Med Mol Imaging 2024; 51:332-335. [PMID: 37803245 DOI: 10.1007/s00259-023-06450-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Affiliation(s)
- Xiaohui Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Daoyan Hu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China.
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, 201203, China.
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China.
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.
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11
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Panagiotidis E, Papachristou K, Makridou A, Zoglopitou LA, Paschali A, Kalathas T, Chatzimarkou M, Chatzipavlidou V. Review of artificial intelligence clinical applications in Nuclear Medicine. Nucl Med Commun 2024; 45:24-34. [PMID: 37901920 DOI: 10.1097/mnm.0000000000001786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer's disease and Parkinson's disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.
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Affiliation(s)
| | | | - Anna Makridou
- Medical Physics Department, Cancer Hospital of Thessaloniki 'Theagenio', Thessaloniki, Greece
| | | | - Anna Paschali
- Nuclear Medicine Department, Cancer Hospital of Thessaloniki 'Theagenio' and
| | - Theodoros Kalathas
- Nuclear Medicine Department, Cancer Hospital of Thessaloniki 'Theagenio' and
| | - Michael Chatzimarkou
- Medical Physics Department, Cancer Hospital of Thessaloniki 'Theagenio', Thessaloniki, Greece
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Glaudemans AWJM, Dierckx RAJO, Scheerder B, Niessen WJ, Pruim J, Dewi DEO, Borra RJH, Lammertsma AA, Tsoumpas C, Slart RHJA. The first international network symposium on artificial intelligence and informatics in nuclear medicine: "The bright future of nuclear medicine is illuminated by artificial intelligence". Eur J Nucl Med Mol Imaging 2024; 51:336-339. [PMID: 37962619 DOI: 10.1007/s00259-023-06507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Affiliation(s)
- Andor W J M Glaudemans
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Bart Scheerder
- Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Wiro J Niessen
- Board of Directors, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan Pruim
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Dyah E O Dewi
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Ronald J H Borra
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Adriaan A Lammertsma
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Charalampos Tsoumpas
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | - Riemer H J A Slart
- Department of Nuclear Medicine & Molecular Imaging (EB50), Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging group, University of Twente, Enschede, The Netherlands
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13
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Pitarch G, Rotstein Habarnau Y, Chirico R, Konowalik B, Pérez A, Valda A, Bastianello M. Exploring the applicability of a lesion segmentation method on [ 18F]fluorothymidine PET/CT images in diffuse large B-cell lymphoma. Eur J Hybrid Imaging 2023; 7:28. [PMID: 38143262 PMCID: PMC10749290 DOI: 10.1186/s41824-023-00184-3] [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: 08/16/2023] [Accepted: 10/31/2023] [Indexed: 12/26/2023] Open
Abstract
BACKGROUND AND PURPOSE The determination of the total metabolic tumour volume based on [18F]fluorothymidine ([18F]FLT) PET/CT images in diffuse large B-cell lymphoma has a potential clinical value for detecting early relapse in this type of heterogeneous lymphoproliferative tumours. Tumour segmentation is a key step in this process. For this purpose, our objective was to determine a segmentation threshold of [18F]FLT PET/CT images, based on a reference tissue uptake, on a cohort of patients with diffuse large B-cell lymphoma (DLBCL) that have been scanned at different stages of the treatment. METHODS We enrolled 23 adult patients with DLBCL confirmed in II-IV stages without nervous system compromise. All patients were scanned using [18F]FLT PET/CT at the time of diagnosis (baseline PET), interim PET (iPET), and at the end of treatment (fPET). The administered activity was 1.8-2.6 MBq/kg body weight, performed 60-70 min after injection and without use of contrast-enhanced CT. First, we assessed the [18F]FLT uptake stability in liver and bone marrow along the patient follow-up. For the lesion segmentation, three threshold values were assessed. RESULTS Both, liver, and bone marrow can be indistinctly taken as reference tissue. The SUV threshold for a voxel to be considered as belonging to a lesion is expressed in terms of a percentage relative to the patient's uptake in the reference tissue. Found thresholds were: for liver, 62%, 33%, 27%; and for bone marrow, 35%, 21% and 22%, for baseline, iPET and fPET stages, respectively. The relative threshold throughout the treatment has a decreasing tendency along the stages. CONCLUSION Based on the results obtained with [18F]FLT PET/CT during staging and follow-up in patients with DLBCL, reference values were obtained for each stage referring to liver and bone marrow uptake that could be used in clinical practice oncology.
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Affiliation(s)
- Germán Pitarch
- Sección de Imágenes Moleculares y Terapia Metabólica, Hospital Universitario CEMIC, Ciudad Autónoma de Buenos Aires, Argentina
| | - Yamila Rotstein Habarnau
- Centro Universitario de Imágenes Médicas, Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Roxana Chirico
- Sección de Imágenes Moleculares y Terapia Metabólica, Hospital Universitario CEMIC, Ciudad Autónoma de Buenos Aires, Argentina
| | - Brenda Konowalik
- Sección de Imágenes Moleculares y Terapia Metabólica, Hospital Universitario CEMIC, Ciudad Autónoma de Buenos Aires, Argentina
| | - Amalia Pérez
- Centro Universitario de Imágenes Médicas, Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Alejandro Valda
- Centro Universitario de Imágenes Médicas, Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina.
| | - María Bastianello
- Sección de Imágenes Moleculares y Terapia Metabólica, Hospital Universitario CEMIC, Ciudad Autónoma de Buenos Aires, Argentina
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Jia Y, Li Z, Akhavanallaf A, Fessler JA, Dewaraja YK. 90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework. EJNMMI Phys 2023; 10:82. [PMID: 38091168 PMCID: PMC10719178 DOI: 10.1186/s40658-023-00598-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
PURPOSE 90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy-efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods. METHODS Our unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections and CT, and the output is the absorbed dose-rate map. For training and testing under realistic conditions, we generated a series of virtual patient phantom activity/density maps from post-therapy images of patients treated with 90Y-RE at our clinic. To train the scatter estimation network, we use the scatter projections for phantoms generated from MC simulation as the ground truth (GT). To train the dosimetry network, we use MC dose-rate maps generated directly from the activity/density maps of phantoms as the GT (Phantom + MC Dose). We compared performance of our framework (SPECT w/CNN SC + DblurDoseNet) and MC dosimetry (SPECT w/CNN SC + MC Dose) using normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) relative to GT. RESULTS When testing on virtual patient phantoms, our CNN predicted scatter projections had NRMSE of 4.0% ± 0.7% on average. For the SPECT reconstruction with CNN SC, we observed a significant improvement on NRMSE (9.2% ± 1.7%), compared to reconstructions with no SC (149.5% ± 31.2%). In terms of virtual patient dose-rate estimation, SPECT w/CNN SC + DblurDoseNet had a NMAE of 8.6% ± 5.7% and 5.4% ± 4.8% in lesions and healthy livers, respectively; compared to 24.0% ± 6.1% and 17.7% ± 2.1% for SPECT w/CNN SC + MC Dose. In patient dose-rate maps, though no GT was available, we observed sharper lesion boundaries and increased lesion-to-background ratios with our framework. For a typical patient data set, the trained networks took ~ 1 s to generate the scatter estimate and ~ 20 s to generate the dose-rate map (matrix size: 512 × 512 × 194) on a single GPU (NVIDIA V100). CONCLUSION Our deep learning framework, trained using true activity/density maps, has the potential to outperform non-learning voxel dosimetry methods such as MC that are dependent on SPECT image quality. Across comprehensive testing and evaluations on multiple targeted lesions and healthy livers in virtual patients, our proposed deep learning framework demonstrated higher (66% on average in terms of NMAE) estimation accuracy than the current "gold-standard" MC method. The enhanced computing speed with our framework without sacrificing accuracy is highly relevant for clinical dosimetry following 90Y-RE.
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Affiliation(s)
- Yixuan Jia
- Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA.
| | - Zongyu Li
- Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA
| | | | - Jeffrey A Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, 4125 EECS Bldg., 1301 Beal Ave., Ann Arbor, MI, 48109, USA
| | - Yuni K Dewaraja
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
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15
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Léost F, Barbet J, Beyler M, Chérel M, Delpon G, Garcion E, Lacerda S, Lepareur N, Rbah-Vidal L, Vaugier L, Visvikis D. ["New Modalities in Cancer Imaging and Therapy" XVth edition of the workshop organized by the network "Tumor Targeting, Imaging, Radiotherapies" of the Cancéropôle Grand-Ouest, 5-8 October 2022, France]. Bull Cancer 2023; 110:1322-1331. [PMID: 37880044 DOI: 10.1016/j.bulcan.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/16/2023] [Accepted: 08/13/2023] [Indexed: 10/27/2023]
Abstract
The fifteenth edition of the international workshop organized by the "Tumour Targeting and Radiotherapies network" of the Cancéropôle Grand-Ouest focused on the latest advances in internal and external radiotherapy from different disciplinary angles: chemistry, biology, physics, and medicine. The workshop covered several deliberately diverse topics: the role of artificial intelligence, new tools for imaging and external radiotherapy, theranostic aspects, molecules and contrast agents, vectors for innovative combined therapies, and the use of alpha particles in therapy.
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Affiliation(s)
- Françoise Léost
- Cancéropôle Grand-Ouest, IRS-UN, 8, quai Moncousu, 44007 Nantes cedex 1, France.
| | | | - Maryline Beyler
- Université de Brest, UMR CNRS-UBO 6521 CEMCA, 6, avenue V.-Le-Gorgeu, 29200 Brest, France
| | - Michel Chérel
- Nantes Université, Inserm, CNRS, Université d'Angers, CRCI(2)NA, Nantes, France
| | - Grégory Delpon
- Institut de cancérologie de l'Ouest, département de physique médicale, boulevard Jacques-Monod, 44800 Saint-Herblain, France; Laboratoire SUBATECH, UMR 6457 CNRS-IN2P3, IMT Atlantique, 4, rue Alfred-Kastler, 44307 Nantes cedex 3, France
| | - Emmanuel Garcion
- Université d'Angers, Inserm, CNRS, Nantes Université, CRCI(2)NA, Angers, France
| | - Sara Lacerda
- Université d'Orléans, centre de biophysique moléculaire, CNRS UPR 4301, rue Charles-Sadron, 45071 Orléans cedex 2, France
| | - Nicolas Lepareur
- Université de Rennes, Inrae, Inserm, CLCC Eugène-Marquis, institut nutrition, métabolismes et cancer (NUMECAN), UMR 1317, Rennes, France
| | - Latifa Rbah-Vidal
- Nantes Université, Inserm, CNRS, Université d'Angers, CRCI(2)NA, Nantes, France
| | - Loïg Vaugier
- Institut de cancérologie de l'Ouest, département de physique médicale, boulevard Jacques-Monod, 44800 Saint-Herblain, France; Laboratoire SUBATECH, UMR 6457 CNRS-IN2P3, IMT Atlantique, 4, rue Alfred-Kastler, 44307 Nantes cedex 3, France
| | - Dimitris Visvikis
- Inserm, LaTIM, UMR 1101, IBSAM, UBO, UBL, 22, rue Camille-Desmoulins, 29238 Brest, France
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Kerkhof PLM, Tona F. Sex differences in diagnostic modalities of atherosclerosis in the macrocirculation. Atherosclerosis 2023; 384:117275. [PMID: 37783644 DOI: 10.1016/j.atherosclerosis.2023.117275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/30/2023] [Accepted: 09/01/2023] [Indexed: 10/04/2023]
Abstract
Asymptomatic atherosclerosis begins early in life and may progress in a sex-specific manner to become the major cause of cardiovascular morbidity and death. As diagnostic tools to evaluate atherosclerosis in the macrocirculation, we discuss imaging methods (in terms of computed tomography, positron emission tomography, intravascular ultrasound, magnetic resonance imaging, and optical coherence tomography), along with derived scores (Agatston, Gensini, Leaman, Syntax), and also hemodynamic indices of vascular stiffness (including flow-mediated dilation, shear stress, pulse pressure, augmentation index, arterial distensibility), assessment of plaque properties (composition, erosion, rupture), stenosis measures such as fractional flow reserve. Moreover, biomarkers including matrix metalloproteinases, vascular endothelial growth factors and miRNAs, as well as the impact of machine learning support, are described. Special attention is given to age-related aspects and sex-specific characteristics, along with clinical implications. Knowledge gaps are identified and directions for future research formulated.
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Affiliation(s)
- Peter L M Kerkhof
- Dept. Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Location VUmc, Amsterdam, the Netherlands.
| | - Francesco Tona
- Dept. Cardiac, Thoracic and Vascular Sciences, University of Padova, Italy
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17
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Yang C, Ko K, Lin P. Reducing scan time in 177 Lu planar scintigraphy using convolutional neural network: A Monte Carlo simulation study. J Appl Clin Med Phys 2023; 24:e14056. [PMID: 37261890 PMCID: PMC10562044 DOI: 10.1002/acm2.14056] [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: 03/04/2023] [Revised: 05/15/2022] [Accepted: 05/16/2023] [Indexed: 06/02/2023] Open
Abstract
PURPOSE The aim of this study was to reduce scan time in 177 Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate personalized dosimetry for 177 Lu-based peptide receptor radionuclide therapy. METHODS The CNN model used in this work was based on DenseNet, and the training and testing datasets were generated from Monte Carlo simulation. The CNN input images (IMGinput ) consisted of 177 Lu planar scintigraphy that contained 10-90% of the total photon counts, while the corresponding full-count images (IMG100% ) were used as the CNN label images. Two-sample t-test was conducted to compare the difference in pixel intensities within region of interest between IMG100% and CNN output images (IMGoutput ). RESULTS No difference was found in IMGoutput for rods with diameters ranging from 13 to 33 mm in the Derenzo phantom with a target-to-background ratio of 20:1, while statistically significant differences were found in IMGoutput for the 10-mm diameter rods when IMGinput containing 10% to 60% of the total photon counts were denoised. Statistically significant differences were found in IMGoutput for both right and left kidneys in the NCAT phantom when IMGinput containing 10% of the total photon counts were denoised. No statistically significant differences were found in IMGoutput for any other source organs in the NCAT phantom. CONCLUSION Our results showed that the proposed method can reduce scan time by up to 70% for objects larger than 13 mm, making it a useful tool for personalized dosimetry in 177 Lu-based peptide receptor radionuclide therapy in clinical practice.
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Affiliation(s)
- Ching‐Ching Yang
- Department of Medical Imaging and Radiological SciencesKaohsiung Medical UniversityKaohsiungTaiwan
- Department of Medical ResearchKaohsiung Medical University Chung‐Ho Memorial HospitalKaohsiungTaiwan
| | - Kuan‐Yin Ko
- Department of Nuclear MedicineNational Taiwan University Cancer CenterTaipeiTaiwan
- Graduate Institute of Clinical MedicineCollege of MedicineNational Taiwan UniversityTaipeiTaiwan
| | - Pei‐Yao Lin
- Department of Nuclear MedicineNational Taiwan University Cancer CenterTaipeiTaiwan
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Wang YB, He X, Song X, Li M, Zhu D, Zhang F, Chen Q, Lu Y, Wang Y. The radiomic biomarker in non-small cell lung cancer: 18F-FDG PET/CT characterisation of programmed death-ligand 1 status. Clin Radiol 2023; 78:e732-e740. [PMID: 37419772 DOI: 10.1016/j.crad.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/25/2023] [Accepted: 06/01/2023] [Indexed: 07/09/2023]
Abstract
AIM To present an integrated 2-[18F]-fluoro-2-deoxy-d-glucose (18F-FDG) positron-emission tomography (PET)/computed tomography (CT) radiomic characterisation of programmed death-ligand 1 (PD-L1) status in non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS In this retrospective study, 18F-FDG PET/CT images and clinical data of 394 eligible patients were divided into training (n=275) and test sets (n=119). Next, the corresponding nodule of interest was segmented manually on the axial CT images by radiologists. After which, the spatial position matching method was used to match the image positions of CT and PET, and radiomic features of the CT and PET images were extracted. Radiomic models were built using five different machine-learning classifiers and the performance of the radiomic models were further evaluated. Finally, a radiomic signature was established to predict the PD-L1 status in patients with NSCLC using the features in the best performing radiomic model. RESULTS The radiomic model based on the PET intranodular region determined using the logistic regression classifier preformed best, yielding an area under the receiver operating characteristics curve (AUC) of 0.813 (95% CI: 0.812, 0.821) on the test set. The clinical features did not improve the test set AUC (0.806, 95% CI: 0.801, 0.810). The final radiomic signature for PD-L1 status was consisted of three PET radiomic features. CONCLUSION This study showed that an 18F-FDG PET/CT-based radiomic signature could be used as a non-invasive biomarker to discriminate PD-L1-positive from PD-L1-negative in patients with NSCLC.
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Affiliation(s)
- Y B Wang
- Department of Nuclear Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - X He
- Department of Nuclear Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - X Song
- Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - M Li
- Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - D Zhu
- Department of Pathology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - F Zhang
- Department of Nuclear Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - Q Chen
- Department of Nuclear Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China
| | - Y Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Y Wang
- Department of Nuclear Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, Guangdong Province, China.
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Bitterman DS, Gensheimer MF, Jaffray D, Pryma DA, Jiang SB, Morin O, Ginart JB, Upadhaya T, Vallis KA, Buatti JM, Deasy J, Hsiao HT, Chung C, Fuller CD, Greenspan E, Cloyd-Warwick K, Courdy S, Mao A, Barnholtz-Sloan J, Topaloglu U, Hands I, Maurer I, Terry M, Curran WJ, Le QT, Nadaf S, Kibbe W. Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology. JCO Clin Cancer Inform 2023; 7:e2300136. [PMID: 38055914 PMCID: PMC10703125 DOI: 10.1200/cci.23.00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 10/16/2023] [Indexed: 12/08/2023] Open
Abstract
In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.
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Affiliation(s)
- Danielle S. Bitterman
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - Michael F. Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - David Jaffray
- Department of Radiation Physics, M.D. Anderson Cancer Center, Houston, TX
| | - Daniel A. Pryma
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Steve B. Jiang
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Olivier Morin
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Jorge Barrios Ginart
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Taman Upadhaya
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Katherine A. Vallis
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
| | - John M. Buatti
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Joseph Deasy
- Department of Radiation Oncology, University of Iowa Carver College of Medicine, Iowa City, IA
| | - H. Timothy Hsiao
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Caroline Chung
- Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA
| | - Clifton D. Fuller
- Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA
| | - Emily Greenspan
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
| | - Kristy Cloyd-Warwick
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD
| | | | | | - Jill Barnholtz-Sloan
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
- Center for Informatics, Digital Vertical, City of Hope National Comprehensive Cancer Center, Los Angeles, CA
| | - Umit Topaloglu
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
| | - Isaac Hands
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
- Cancer Research Informatics Shared Resource Facility, University of Kentucky Markey Cancer Center, Lexington, NY
| | | | | | | | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - Sorena Nadaf
- Department of Radiation Oncology, Emory University, Atlanta, GA
| | - Warren Kibbe
- Cancer Center Informatics Society, Los Angeles, CA
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20
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Šedienė S, Kulakienė I, Urbonavičius BG, Korobeinikova E, Rudžianskas V, Povilonis PA, Jaselskė E, Adlienė D, Juozaitytė E. Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1173. [PMID: 37374377 DOI: 10.3390/medicina59061173] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/25/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: To our knowledge, this is the first study that investigated the prognostic value of radiomics features extracted from not only staging 18F-fluorodeoxyglucose positron emission tomography (FDG PET/CT) images, but also post-induction chemotherapy (ICT) PET/CT images. This study aimed to construct a training model based on radiomics features obtained from PET/CT in a cohort of patients with locally advanced head and neck squamous cell carcinoma treated with ICT, to predict locoregional recurrence, development of distant metastases, and the overall survival, and to extract the most significant radiomics features, which were included in the final model. Materials and Methods: This retrospective study analyzed data of 55 patients. All patients underwent PET/CT at the initial staging and after ICT. Along the classical set of 13 parameters, the original 52 parameters were extracted from each PET/CT study and an additional 52 parameters were generated as a difference between radiomics parameters before and after the ICT. Five machine learning algorithms were tested. Results: The Random Forest algorithm demonstrated the best performance (R2 0.963-0.998) in the majority of datasets. The strongest correlation in the classical dataset was between the time to disease progression and time to death (r = 0.89). Another strong correlation (r ≥ 0.8) was between higher-order texture indices GLRLM_GLNU, GLRLM_SZLGE, and GLRLM_ZLNU and standard PET parameters MTV, TLG, and SUVmax. Patients with a higher numerical expression of GLCM_ContrastVariance, extracted from the delta dataset, had a longer survival and longer time until progression (p = 0.001). Good correlations were observed between Discretized_SUVstd or Discretized_SUVSkewness and time until progression (p = 0.007). Conclusions: Radiomics features extracted from the delta dataset produced the most robust data. Most of the parameters had a positive impact on the prediction of the overall survival and the time until progression. The strongest single parameter was GLCM_ContrastVariance. Discretized_SUVstd or Discretized_SUVSkewness demonstrated a strong correlation with the time until progression.
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Affiliation(s)
- Severina Šedienė
- Department of Radiology of Lithuanian, University of Health Sciences, Eivenių g. 2, LT-50161 Kaunas, Lithuania
| | - Ilona Kulakienė
- Department of Radiology of Lithuanian, University of Health Sciences, Eivenių g. 2, LT-50161 Kaunas, Lithuania
| | - Benas Gabrielis Urbonavičius
- Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentu g. 50, LT-51368 Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute of Lithuanian, University of Health Sciences, Eiveniu g. 2, LT-50161 Kaunas, Lithuania
| | - Viktoras Rudžianskas
- Oncology Institute of Lithuanian, University of Health Sciences, Eiveniu g. 2, LT-50161 Kaunas, Lithuania
| | - Paulius Algirdas Povilonis
- Medical Academy of Lithuania, University of Health Sciences, A. Mickeviciaus g. 9, LT-44307 Kaunas, Lithuania
| | - Evelina Jaselskė
- Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentu g. 50, LT-51368 Kaunas, Lithuania
| | - Diana Adlienė
- Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentu g. 50, LT-51368 Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute of Lithuanian, University of Health Sciences, Eiveniu g. 2, LT-50161 Kaunas, Lithuania
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21
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Carter LM, Krebs S, Marquis H, Ramos JCO, Olguin EA, Mason EO, Bolch WE, Zanzonico PB, Kesner AL. Dosimetric variability across a library of computational tumor phantoms. J Nucl Med 2023; 64:782-790. [PMID: 37074039 PMCID: PMC10152122 DOI: 10.2967/jnumed.122.264916] [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: 09/13/2022] [Revised: 11/29/2022] [Indexed: 12/13/2022] Open
Abstract
In radiopharmaceutical therapy, dosimetry-based treatment planning and response evaluation require accurate estimates of tumor-absorbed dose. Tumor dose estimates are routinely derived using simplistic spherical models, despite the well-established influence of tumor geometry on the dosimetry. Moreover, the degree of disease invasiveness correlates with departure from ideal geometry; malignant lesions often possess lobular, spiculated, or otherwise irregular margins in contrast to the commonly regular or smooth contours characteristic of benign lesions. To assess the effects of tumor shape, size, and margin contour on absorbed dose, an array of tumor geometries was modeled using computer-aided design software, and the models were used to calculate absorbed dose per unit of time-integrated activity (i.e., S values) for several clinically applied therapeutic radionuclides (90Y, 131I, 177Lu, 211At, 225Ac, 213Bi, and 223Ra). Methods: Three-dimensional tumor models of several different shape classifications were generated using Blender software. Ovoid shapes were generated using axial scaling. Lobulated, spiculated, and irregular contours were generated using noise-based mesh deformation. The meshes were rigidly scaled to different volumes, and S values were then computed using PARaDIM software. Radiomic features were extracted for each shape, and the impact on S values was examined. Finally, the systematic error present in dose calculations that model complex tumor shapes versus equivalent-mass spheres was estimated. Results: The dependence of tumor S values on shape was largest for extreme departures from spherical geometry and for long-range emissions (e.g., 90Y β-emissions). S values for spheres agreed reasonably well with lobulated, spiculated, or irregular contours if the surface perturbation was small. For marked deviations from spherical shape and small volumes, the systematic error of the equivalent-sphere approximation increased to 30%–75% depending on radionuclide. The errors were largest for shapes with many long spicules and for spherical shells with a thickness less than or comparable to the particle range in tissue. Conclusion: Variability in tumor S values as a function of tumor shape and margin contour was observed, suggesting use of contour-matched phantoms to improve the accuracy of tumor dosimetry in organ-level dosimetry paradigms. Implementing a library of tumor phantoms in organ-level dosimetry software may facilitate optimization strategies for personalized radionuclide therapies.
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Affiliation(s)
- Lukas M. Carter
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Simone Krebs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Harry Marquis
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Juan C. Ocampo Ramos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Edmond A. Olguin
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard University, Boston, Massachusetts
| | - Emilia O. Mason
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami, Miami, Florida; and
| | - Wesley E. Bolch
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Pat B. Zanzonico
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Adam L. Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
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22
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Tian L, Dong T, Hu S, Zhao C, Yu G, Hu H, Yang W. Radiomic and clinical nomogram for cognitive impairment prediction in Wilson's disease. Front Neurol 2023; 14:1131968. [PMID: 37188313 PMCID: PMC10177658 DOI: 10.3389/fneur.2023.1131968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
Objective To investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson's disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment. Methods Overall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD. Results The area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment. Conclusion The nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients.
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Affiliation(s)
- Liwei Tian
- Graduate School, Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Ting Dong
- Department of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
- Key Laboratory of Xin’An Medicine, Ministry of Education, Hefei, Anhui, China
- *Correspondence: Ting Dong,
| | - Sheng Hu
- Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Chenling Zhao
- Graduate School, Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Guofang Yu
- Graduate School, Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Huibing Hu
- Qimen People's Hospital, Huangshan, Anhui, China
| | - Wenming Yang
- Department of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
- Key Laboratory of Xin’An Medicine, Ministry of Education, Hefei, Anhui, China
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23
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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24
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van Sluis J, Borra R, Tsoumpas C, van Snick JH, Roya M, ten Hove D, Brouwers AH, Lammertsma AA, Noordzij W, Dierckx RA, Slart RH, Glaudemans AW. Extending the clinical capabilities of short- and long-lived positron-emitting radionuclides through high sensitivity PET/CT. Cancer Imaging 2022; 22:69. [PMID: 36527149 PMCID: PMC9755796 DOI: 10.1186/s40644-022-00507-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
This review describes the main benefits of using long axial field of view (LAFOV) PET in clinical applications. As LAFOV PET is the latest development in PET instrumentation, many studies are ongoing that explore the potentials of these systems, which are characterized by ultra-high sensitivity. This review not only provides an overview of the published clinical applications using LAFOV PET so far, but also provides insight in clinical applications that are currently under investigation. Apart from the straightforward reduction in acquisition times or administered amount of radiotracer, LAFOV PET also allows for other clinical applications that to date were mostly limited to research, e.g., dual tracer imaging, whole body dynamic PET imaging, omission of CT in serial PET acquisition for repeat imaging, and studying molecular interactions between organ systems. It is expected that this generation of PET systems will significantly advance the field of nuclear medicine and molecular imaging.
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Affiliation(s)
- Joyce van Sluis
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Ronald Borra
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Charalampos Tsoumpas
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Johannes H. van Snick
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Mostafa Roya
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Dik ten Hove
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Adrienne H. Brouwers
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Adriaan A. Lammertsma
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Walter Noordzij
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Rudi A.J.O. Dierckx
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Riemer H.J.A. Slart
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
| | - Andor W.J.M. Glaudemans
- grid.4494.d0000 0000 9558 4598Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, the Netherlands
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25
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Hustinx R, Pruim J, Lassmann M, Visvikis D. An EANM position paper on the application of artificial intelligence in nuclear medicine. Eur J Nucl Med Mol Imaging 2022; 50:61-66. [PMID: 36006443 DOI: 10.1007/s00259-022-05947-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 08/16/2022] [Indexed: 11/04/2022]
Abstract
Artificial intelligence (AI) is coming into the field of nuclear medicine, and it is likely here to stay. As a society, EANM can and must play a central role in the use of AI in nuclear medicine. In this position paper, the EANM explains the preconditions for the implementation of AI in NM and takes position.
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Affiliation(s)
- Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège & GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
| | - Jan Pruim
- Medical Imaging Center, Dept. of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Michael Lassmann
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
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