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Dong X, Chen G, Zhu Y, Ma B, Ban X, Wu N, Ming Y. Artificial intelligence in skeletal metastasis imaging. Comput Struct Biotechnol J 2024; 23:157-164. [PMID: 38144945 PMCID: PMC10749216 DOI: 10.1016/j.csbj.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 12/26/2023] Open
Abstract
In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.
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Affiliation(s)
- Xiying Dong
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021 Beijing, China
| | - Guilin Chen
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Graduate School of Peking Union Medical College, Beijing 100730, China
| | - Yuanpeng Zhu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Graduate School of Peking Union Medical College, Beijing 100730, China
| | - Boyuan Ma
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Xiaojuan Ban
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Nan Wu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Yue Ming
- Department of Nuclear Medicine (PET-CT Center), National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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2
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Boire A, Burke K, Cox TR, Guise T, Jamal-Hanjani M, Janowitz T, Kaplan R, Lee R, Swanton C, Vander Heiden MG, Sahai E. Why do patients with cancer die? Nat Rev Cancer 2024; 24:578-589. [PMID: 38898221 PMCID: PMC7616303 DOI: 10.1038/s41568-024-00708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/15/2024] [Indexed: 06/21/2024]
Abstract
Cancer is a major cause of global mortality, both in affluent countries and increasingly in developing nations. Many patients with cancer experience reduced life expectancy and have metastatic disease at the time of death. However, the more precise causes of mortality and patient deterioration before death remain poorly understood. This scarcity of information, particularly the lack of mechanistic insights, presents a challenge for the development of novel treatment strategies to improve the quality of, and potentially extend, life for patients with late-stage cancer. In addition, earlier deployment of existing strategies to prolong quality of life is highly desirable. In this Roadmap, we review the proximal causes of mortality in patients with cancer and discuss current knowledge about the interconnections between mechanisms that contribute to mortality, before finally proposing new and improved avenues for data collection, research and the development of treatment strategies that may improve quality of life for patients.
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Affiliation(s)
- Adrienne Boire
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katy Burke
- University College London Hospitals NHS Foundation Trust and Central and North West London NHS Foundation Trust Palliative Care Team, London, UK
| | - Thomas R Cox
- Cancer Ecosystems Program, The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia.
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, UNSW Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia.
| | - Theresa Guise
- Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mariam Jamal-Hanjani
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
- Cancer Research UK Lung Centre of Excellence, University College London Cancer Institute, London, UK
| | - Tobias Janowitz
- Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, NY, USA
- Northwell Health Cancer Institute, New York, NY, USA
| | - Rosandra Kaplan
- Paediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rebecca Lee
- Tumour Cell Biology Laboratory, The Francis Crick Institute, London, UK
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Charles Swanton
- Department of Oncology, University College London Hospitals, London, UK
- Cancer Research UK Lung Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Erik Sahai
- Tumour Cell Biology Laboratory, The Francis Crick Institute, London, UK.
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Volpe A, Lyashchenko SK, Ponomarev V. Nuclear-Based Labeling of Cellular Immunotherapies: A Simple Protocol for Preclinical Use. Mol Imaging Biol 2024; 26:555-568. [PMID: 38958882 DOI: 10.1007/s11307-024-01923-z] [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: 01/22/2024] [Revised: 05/10/2024] [Accepted: 05/18/2024] [Indexed: 07/04/2024]
Abstract
Labeling and tracking existing and emerging cell-based immunotherapies using nuclear imaging is widely used to guide the preclinical phases of development and testing of existing and new emerging off-the-shelf cell-based immunotherapies. In fact, advancing our knowledge about their mechanism of action and limitations could provide preclinical support and justification for moving towards clinical experimentation of newly generated products and expedite their approval by the Food and Drug Administration (FDA).Here we provide the reader with a ready to use protocol describing the labeling methodologies and practical procedures to render different candidate cell therapies in vivo traceable by nuclear-based imaging. The protocol includes sufficient practical details to aid researchers at all career stages and from different fields in familiarizing with the described concepts and incorporating them into their work.
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Affiliation(s)
- Alessia Volpe
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Serge K Lyashchenko
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Vladimir Ponomarev
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
- Molecular Pharmacology and Chemistry Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
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Zarei M, Wallsten E, Grefve J, Söderkvist K, Gunnlaugsson A, Sandgren K, Jonsson J, Keeratijarut Lindberg A, Nilsson E, Bergh A, Zackrisson B, Moreau M, Thellenberg Karlsson C, Olsson LE, Widmark A, Riklund K, Blomqvist L, Berg Loegager V, Axelsson J, Strandberg SN, Nyholm T. Accuracy of gross tumour volume delineation with [68Ga]-PSMA-PET compared to histopathology for high-risk prostate cancer. Acta Oncol 2024; 63:503-510. [PMID: 38912830 DOI: 10.2340/1651-226x.2024.39041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 04/24/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND The delineation of intraprostatic lesions is vital for correct delivery of focal radiotherapy boost in patients with prostate cancer (PC). Errors in the delineation could translate into reduced tumour control and potentially increase the side effects. The purpose of this study is to compare PET-based delineation methods with histopathology. MATERIALS AND METHODS The study population consisted of 15 patients with confirmed high-risk PC intended for prostatectomy. [68Ga]-PSMA-PET/MR was performed prior to surgery. Prostate lesions identified in histopathology were transferred to the in vivo [68Ga]-PSMA-PET/MR coordinate system. Four radiation oncologists manually delineated intraprostatic lesions based on PET data. Various semi-automatic segmentation methods were employed, including absolute and relative thresholds, adaptive threshold, and multi-level Otsu threshold. RESULTS The gross tumour volumes (GTVs) delineated by the oncologists showed a moderate level of interobserver agreement with Dice similarity coefficient (DSC) of 0.68. In comparison with histopathology, manual delineations exhibited the highest median DSC and the lowest false discovery rate (FDR) among all approaches. Among semi-automatic approaches, GTVs generated using standardized uptake value (SUV) thresholds above 4 (SUV > 4) demonstrated the highest median DSC (0.41), with 0.51 median lesion coverage ratio, FDR of 0.66 and the 95th percentile of the Hausdorff distance (HD95%) of 8.22 mm. INTERPRETATION Manual delineations showed a moderate level of interobserver agreement. Compared to histopathology, manual delineations and SUV > 4 exhibited the highest DSC and the lowest HD95% values. The methods that resulted in a high lesion coverage were associated with a large overestimation of the size of the lesions.
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Affiliation(s)
- Maryam Zarei
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden.
| | - Elin Wallsten
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Josefine Grefve
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Karin Söderkvist
- Department of Diagnostics and Intervention, Oncology, Umeå University, Umeå, Sweden
| | - Adalsteinn Gunnlaugsson
- Skane University Hospital, Department of Hematology, Oncology and Radiation Physics, Lund, Sweden
| | - Kristina Sandgren
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Joakim Jonsson
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Angsana Keeratijarut Lindberg
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Erik Nilsson
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Björn Zackrisson
- Department of Diagnostics and Intervention, Oncology, Umeå University, Umeå, Sweden
| | - Mathieu Moreau
- Skane University Hospital, Department of Hematology, Oncology and Radiation Physics, Lund, Sweden
| | | | - Lars E Olsson
- Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Anders Widmark
- Department of Diagnostics and Intervention, Oncology, Umeå University, Umeå, Sweden
| | - Katrine Riklund
- Department of Diagnostics and Intervention, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Lennart Blomqvist
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden
| | - Vibeke Berg Loegager
- Department of Radiology, Copenhagen University Hospital in Herlev, Herlev, Denmark
| | - Jan Axelsson
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
| | - Sara N Strandberg
- Department of Diagnostics and Intervention, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Tufve Nyholm
- Department of Diagnostics and Intervention, Biomedical engineering and Radiation Physics, Umeå University, Umeå, Sweden
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Mo X, Zhang Z, Song J, Wang Y, Yu Z. Self-assembly of peptides in living cells for disease theranostics. J Mater Chem B 2024; 12:4289-4306. [PMID: 38595070 DOI: 10.1039/d4tb00365a] [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: 04/11/2024]
Abstract
The past few decades have witnessed substantial progress in biomedical materials for addressing health concerns and improving disease therapeutic and diagnostic efficacy. Conventional biomedical materials are typically created through an ex vivo approach and are usually utilized under physiological environments via transfer from preparative media. This transfer potentially gives rise to challenges for the efficient preservation of the bioactivity and implementation of theranostic goals on site. To overcome these issues, the in situ synthesis of biomedical materials on site has attracted great attention in the past few years. Peptides, which exhibit remarkable biocompability and reliable noncovalent interactions, can be tailored via tunable assembly to precisely create biomedical materials. In this review, we summarize the progress in the self-assembly of peptides in living cells for disease diagnosis and therapy. After a brief introduction to the basic design principles of peptide assembly systems in living cells, the applications of peptide assemblies for bioimaging and disease treatment are highlighted. The challenges in the field of peptide self-assembly in living cells and the prospects for novel peptide assembly systems towards next-generation biomaterials are also discussed, which will hopefully help elucidate the great potential of peptide assembly in living cells for future healthcare applications.
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Affiliation(s)
- Xiaowei Mo
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Zeyu Zhang
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Jinyan Song
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Yushi Wang
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Zhilin Yu
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
- Haihe Laboratory of Synthetic Biology, 21 West 15th Avenue, Tianjin 300308, China
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Scoditti E, Sabatini S, Carli F, Gastaldelli A. Hepatic glucose metabolism in the steatotic liver. Nat Rev Gastroenterol Hepatol 2024; 21:319-334. [PMID: 38308003 DOI: 10.1038/s41575-023-00888-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/13/2023] [Indexed: 02/04/2024]
Abstract
The liver is central in regulating glucose homeostasis, being the major contributor to endogenous glucose production and the greatest reserve of glucose as glycogen. It is both a target and regulator of the action of glucoregulatory hormones. Hepatic metabolic functions are altered in and contribute to the highly prevalent steatotic liver disease (SLD), including metabolic dysfunction-associated SLD (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). In this Review, we describe the dysregulation of hepatic glucose metabolism in MASLD and MASH and associated metabolic comorbidities, and how advances in techniques and models for the assessment of hepatic glucose fluxes in vivo have led to the identification of the mechanisms related to the alterations in glucose metabolism in MASLD and comorbidities. These fluxes can ultimately increase hepatic glucose production concomitantly with fat accumulation and alterations in the secretion and action of glucoregulatory hormones. No pharmacological treatment has yet been approved for MASLD or MASH, but some antihyperglycaemic drugs approved for treating type 2 diabetes have shown positive effects on hepatic glucose metabolism and hepatosteatosis. A deep understanding of how MASLD affects glucose metabolic fluxes and glucoregulatory hormones might assist in the early identification of at-risk individuals and the use or development of targeted therapies.
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Affiliation(s)
- Egeria Scoditti
- Institute of Clinical Physiology, National Research Council, Lecce, Italy
| | - Silvia Sabatini
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Fabrizia Carli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Amalia Gastaldelli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy.
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7
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Philip MM, Watts J, Moeini SNM, Musheb M, McKiddie F, Welch A, Nath M. Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images. Phys Med Biol 2024; 69:095005. [PMID: 38530298 DOI: 10.1088/1361-6560/ad37ea] [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: 11/07/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.
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Affiliation(s)
- Mahima Merin Philip
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| | - Jessica Watts
- National Health Service Grampian, Aberdeen AB15 6RE, United Kingdom
| | | | - Mohammed Musheb
- National Health Service Highland, Inverness IV2 3BW, United Kingdom
| | - Fergus McKiddie
- National Health Service Grampian, Aberdeen AB15 6RE, United Kingdom
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| | - Mintu Nath
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
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8
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Marks LB, Das SK, Tepper JE. What You See Ain't Necessarily What You Got. Int J Radiat Oncol Biol Phys 2024; 118:1164-1166. [PMID: 38492967 DOI: 10.1016/j.ijrobp.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/25/2023] [Accepted: 09/02/2023] [Indexed: 03/18/2024]
Affiliation(s)
- Lawrence B Marks
- Department of Radiation Oncology, UNC School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
| | - Shiva K Das
- Department of Radiation Oncology, UNC School of Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Joel E Tepper
- Department of Radiation Oncology, UNC School of Medicine, University of North Carolina, Chapel Hill, North Carolina
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Liu G, Shi Y, Hou X, Yu H, Hu Y, Zhang Y, Shi H. Dynamic total-body PET/CT imaging with reduced acquisition time shows acceptable performance in quantification of [ 18F]FDG tumor kinetic metrics. Eur J Nucl Med Mol Imaging 2024; 51:1371-1382. [PMID: 38078950 DOI: 10.1007/s00259-023-06526-4] [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: 05/06/2023] [Accepted: 11/14/2023] [Indexed: 03/22/2024]
Abstract
PURPOSE To investigate the feasibility of reducing the acquisition time for continuous dynamic positron emission tomography (PET) while retaining acceptable performance in quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-D-glucose ([18F]FDG) in tumors. METHODS In total, 78 oncological patients underwent total-body dynamic PET imaging for ≥ 60 min, with 8, 20, and 50 patients receiving full activity (3.7 MBq/kg), half activity (1.85 MBq/kg), and ultra-low activity (0.37 MBq/kg) of [18F]FDG, respectively. The dynamic data were divided into 21-, 30-, 45- and ≥ 60-min groups. The kinetic analysis involved model fitting to derive constant rates (VB, K1 to k3, and Ki) for both tumors and normal tissues, using both reversible and irreversible two-tissue-compartment models. One-way ANOVA with repeated measures or the Freidman test compared the kinetic metrics among groups, while the Deming regression assessed the correlation of kinetic metrics among groups. RESULTS All kinetic metrics in the 30-min and 45-min groups were statistically comparable to those in the ≥ 60-min group. The relative differences between the 30-min and ≥ 60-min groups ranged from 12.3% ± 15.1% for K1 to 29.8% ± 30.0% for VB, and those between the 45-min and ≥ 60-min groups ranged from 7.5% ± 8.7% for Ki to 24.0% ± 24.3% for VB. However, this comparability was not observed between the 21-min and ≥ 60-min groups. The significance trend of these comparisons remained consistent across different models (reversible or irreversible), administrated activity levels, and partial volume corrections for lesions. Significant correlations in tumor kinetic metrics were identified between the 30-/45-min and ≥ 60-min groups, with Deming regression slopes > 0.813. In addition, the comparability of kinetic metrics between the 30-min and ≥ 60-min groups were established for normal tissues. CONCLUSION The acquisition time for dynamic PET imaging can be reduced to 30 min without compromising the ability to reveal tumor kinetic metrics of [18F]FDG, using the total-body PET/CT system.
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Affiliation(s)
- Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Department of Nuclear Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, 361015, China
| | - Yimeng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiaoguang Hou
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Haojun Yu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yan Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiqiu Zhang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, P.R. China.
- Department of Nuclear Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, 361015, China.
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, 361015, China.
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10
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Liu Z, Mhlanga JC, Xia H, Siegel BA, Jha AK. Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data. J Nucl Med 2024; 65:jnumed.123.266018. [PMID: 38360049 PMCID: PMC10924158 DOI: 10.2967/jnumed.123.266018] [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: 05/12/2023] [Revised: 12/19/2023] [Accepted: 12/19/2023] [Indexed: 02/17/2024] Open
Abstract
Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD). The objective of this study was to investigate whether evaluating PET segmentation algorithms using these task-agnostic FoMs yields interpretations consistent with evaluation on clinically relevant quantitative tasks. Methods: We conducted a retrospective study to assess the concordance in the evaluation of segmentation algorithms using the DSC, JSC, and HD and on the tasks of estimating the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors from PET images of patients with non-small cell lung cancer. The PET images were collected from the American College of Radiology Imaging Network 6668/Radiation Therapy Oncology Group 0235 multicenter clinical trial data. The study was conducted in 2 contexts: (1) evaluating conventional segmentation algorithms, namely those based on thresholding (SUVmax40% and SUVmax50%), boundary detection (Snakes), and stochastic modeling (Markov random field-Gaussian mixture model); (2) evaluating the impact of network depth and loss function on the performance of a state-of-the-art U-net-based segmentation algorithm. Results: Evaluation of conventional segmentation algorithms based on the DSC, JSC, and HD showed that SUVmax40% significantly outperformed SUVmax50%. However, SUVmax40% yielded lower accuracy on the tasks of estimating MTV and TLG, with a 51% and 54% increase, respectively, in the ensemble normalized bias. Similarly, the Markov random field-Gaussian mixture model significantly outperformed Snakes on the basis of the task-agnostic FoMs but yielded a 24% increased bias in estimated MTV. For the U-net-based algorithm, our evaluation showed that although the network depth did not significantly alter the DSC, JSC, and HD values, a deeper network yielded substantially higher accuracy in the estimated MTV and TLG, with a decreased bias of 91% and 87%, respectively. Additionally, whereas there was no significant difference in the DSC, JSC, and HD values for different loss functions, up to a 73% and 58% difference in the bias of the estimated MTV and TLG, respectively, existed. Conclusion: Evaluation of PET segmentation algorithms using task-agnostic FoMs could yield findings discordant with evaluation on clinically relevant quantitative tasks. This study emphasizes the need for objective task-based evaluation of image segmentation algorithms for quantitative PET.
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Affiliation(s)
- Ziping Liu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Joyce C Mhlanga
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
| | - Huitian Xia
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Barry A Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Abhinav K Jha
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri;
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri; and
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
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11
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Gawel J, Rogulski Z. The Challenge of Single-Photon Emission Computed Tomography Image Segmentation in the Internal Dosimetry of 177Lu Molecular Therapies. J Imaging 2024; 10:27. [PMID: 38276319 PMCID: PMC10817423 DOI: 10.3390/jimaging10010027] [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: 11/27/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
The aim of this article is to review the single photon emission computed tomography (SPECT) segmentation methods used in patient-specific dosimetry of 177Lu molecular therapy. Notably, 177Lu-labelled radiopharmaceuticals are currently used in molecular therapy of metastatic neuroendocrine tumours (ligands for somatostatin receptors) and metastatic prostate adenocarcinomas (PSMA ligands). The proper segmentation of the organs at risk and tumours in targeted radionuclide therapy is an important part of the optimisation process of internal patient dosimetry in this kind of therapy. Because this is the first step in dosimetry assessments, on which further dose calculations are based, it is important to know the level of uncertainty that is associated with this part of the analysis. However, the robust quantification of SPECT images, which would ensure accurate dosimetry assessments, is very hard to achieve due to the intrinsic features of this device. In this article, papers on this topic were collected and reviewed to weigh up the advantages and disadvantages of the segmentation methods used in clinical practice. Degrading factors of SPECT images were also studied to assess their impact on the quantification of 177Lu therapy images. Our review of the recent literature gives an insight into this important topic. However, based on the PubMed and IEEE databases, only a few papers investigating segmentation methods in 177Lumolecular therapy were found. Although segmentation is an important step in internal dose calculations, this subject has been relatively lightly investigated for SPECT systems. This is mostly due to the inner features of SPECT. What is more, even when studies are conducted, they usually utilise the diagnostic radionuclide 99mTc and not a therapeutic one like 177Lu, which could be of concern regarding SPECT camera performance and its overall outcome on dosimetry.
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Affiliation(s)
- Joanna Gawel
- Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland
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12
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Cerina V, Crivellaro C, Morzenti S, Pozzi FE, Bigiogera V, Jonghi-Lavarini L, Moresco RM, Basso G, De Bernardi E. A ROI-based quantitative pipeline for 18F-FDG PET metabolism and pCASL perfusion joint analysis: Validation of the 18F-FDG PET line. Heliyon 2024; 10:e23340. [PMID: 38163125 PMCID: PMC10755331 DOI: 10.1016/j.heliyon.2023.e23340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
In Mild Cognitive Impairment (MCI), the study of brain metabolism, provided by 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) can be integrated with brain perfusion through pseudo-Continuous Arterial Spin Labeling Magnetic Resonance sequences (MR pCASL). Cortical hypometabolism identification generally relies on wide control group datasets; pCASL control groups are instead not publicly available yet, due to lack of standardization in the acquisition parameters. This study presents a quantitative pipeline to be applied to PET and pCASL data to coherently analyze metabolism and perfusion inside 16 matching cortical regions of interest (ROIs) derived from the AAL3 atlas. The PET line is tuned on 36 MCI patients and 107 healthy control subjects, to agree in identifying hypometabolic regions with clinical reference methods (visual analysis supported by a vendor tool and Statistical Parametric Mapping, SPM, with two parametrizations here identified as SPM-A and SPM-B). The analysis was conducted for each ROI separately. The proposed PET analysis pipeline obtained accuracy 78 % and Cohen's к 60 % vs visual analysis, accuracy 79 % and Cohen's к 58 % vs SPM-A, accuracy 77 % and Cohen's к 54 % vs SPM-B. Cohen's к resulted not significantly different from SPM-A and SPM-B Cohen's к when assuming visual analysis as reference method (p-value 0.61 and 0.31 respectively). Considering SPM-A as reference method, Cohen's к is not significantly different from SPM-B Cohen's к as well (p-value = 1.00). The complete PET-pCASL pipeline was then preliminarily applied on 5 MCI patients and metabolism-perfusion regional correlations were assessed. The proposed approach can be considered as a promising tool for PET-pCASL joint analyses in MCI, even in the absence of a pCASL control group, to perform metabolism-perfusion regional correlation studies, and to assess and compare perfusion in hypometabolic or normo-metabolic areas.
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Affiliation(s)
- Valeria Cerina
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Cinzia Crivellaro
- Nuclear Medicine, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Sabrina Morzenti
- Medical Physics, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Federico E. Pozzi
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neurology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
| | | | | | - Rosa M. Moresco
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Gianpaolo Basso
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neuroradiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
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13
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Kaczanowska S, Murty T, Alimadadi A, Contreras CF, Duault C, Subrahmanyam PB, Reynolds W, Gutierrez NA, Baskar R, Wu CJ, Michor F, Altreuter J, Liu Y, Jhaveri A, Duong V, Anbunathan H, Ong C, Zhang H, Moravec R, Yu J, Biswas R, Van Nostrand S, Lindsay J, Pichavant M, Sotillo E, Bernstein D, Carbonell A, Derdak J, Klicka-Skeels J, Segal JE, Dombi E, Harmon SA, Turkbey B, Sahaf B, Bendall S, Maecker H, Highfill SL, Stroncek D, Glod J, Merchant M, Hedrick CC, Mackall CL, Ramakrishna S, Kaplan RN. Immune determinants of CAR-T cell expansion in solid tumor patients receiving GD2 CAR-T cell therapy. Cancer Cell 2024; 42:35-51.e8. [PMID: 38134936 PMCID: PMC10947809 DOI: 10.1016/j.ccell.2023.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 09/18/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023]
Abstract
Chimeric antigen receptor T cells (CAR-Ts) have remarkable efficacy in liquid tumors, but limited responses in solid tumors. We conducted a Phase I trial (NCT02107963) of GD2 CAR-Ts (GD2-CAR.OX40.28.z.iC9), demonstrating feasibility and safety of administration in children and young adults with osteosarcoma and neuroblastoma. Since CAR-T efficacy requires adequate CAR-T expansion, patients were grouped into good or poor expanders across dose levels. Patient samples were evaluated by multi-dimensional proteomic, transcriptomic, and epigenetic analyses. T cell assessments identified naive T cells in pre-treatment apheresis associated with good expansion, and exhausted T cells in CAR-T products with poor expansion. Myeloid cell assessment identified CXCR3+ monocytes in pre-treatment apheresis associated with good expansion. Longitudinal analysis of post-treatment samples identified increased CXCR3- classical monocytes in all groups as CAR-T numbers waned. Together, our data uncover mediators of CAR-T biology and correlates of expansion that could be utilized to advance immunotherapies for solid tumor patients.
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Affiliation(s)
- Sabina Kaczanowska
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tara Murty
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Ahmad Alimadadi
- La Jolla Institute for Immunology, La Jolla, CA, USA; Immunology Center of Georgia, Augusta University, Augusta, GA, USA; Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Cristina F Contreras
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Oncology, University of Oxford, Oxford, UK
| | - Caroline Duault
- Stanford Human Immune Monitoring Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Priyanka B Subrahmanyam
- Stanford Human Immune Monitoring Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Warren Reynolds
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Reema Baskar
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Catherine J Wu
- Broad Institute, Cambridge, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Yang Liu
- Broad Institute, Cambridge, MA, USA
| | | | - Vandon Duong
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Hima Anbunathan
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Claire Ong
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hua Zhang
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Radim Moravec
- Cancer Therapy Evaluation Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joyce Yu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Mina Pichavant
- Immunology Center of Georgia, Augusta University, Augusta, GA, USA
| | - Elena Sotillo
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Donna Bernstein
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amanda Carbonell
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanne Derdak
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacquelyn Klicka-Skeels
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Julia E Segal
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eva Dombi
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bita Sahaf
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean Bendall
- Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Holden Maecker
- Immunology Center of Georgia, Augusta University, Augusta, GA, USA
| | - Steven L Highfill
- Center for Cellular Engineering, Department of Transfusion Medicine, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - David Stroncek
- Center for Cellular Engineering, Department of Transfusion Medicine, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - John Glod
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melinda Merchant
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Catherine C Hedrick
- La Jolla Institute for Immunology, La Jolla, CA, USA; Immunology Center of Georgia, Augusta University, Augusta, GA, USA; Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Crystal L Mackall
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Sneha Ramakrishna
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Rosandra N Kaplan
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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14
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Alshamrani K, Alshamrani HA. A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images1. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:123-139. [PMID: 37458060 DOI: 10.3233/xst-230095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images.
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Affiliation(s)
- Khalaf Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | - Hassan A Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
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15
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Pasini G, Russo G, Mantarro C, Bini F, Richiusa S, Morgante L, Comelli A, Russo GI, Sabini MG, Cosentino S, Marinozzi F, Ippolito M, Stefano A. A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer. Diagnostics (Basel) 2023; 13:3640. [PMID: 38132224 PMCID: PMC10743045 DOI: 10.3390/diagnostics13243640] [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: 10/30/2023] [Revised: 11/29/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. AIM We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. CONCLUSIONS Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.
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Affiliation(s)
- Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95125 Catania, Italy
| | - Cristina Mantarro
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Selene Richiusa
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
| | - Lucrezia Morgante
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Giorgio Ivan Russo
- Department of Surgery, Urology Section, University of Catania, 95125 Catania, Italy;
| | | | - Sebastiano Cosentino
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Massimo Ippolito
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95125 Catania, Italy
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Andrearczyk V, Oreiller V, Boughdad S, Le Rest CC, Tankyevych O, Elhalawani H, Jreige M, Prior JO, Vallières M, Visvikis D, Hatt M, Depeursinge A. Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge. Med Image Anal 2023; 90:102972. [PMID: 37742374 DOI: 10.1016/j.media.2023.102972] [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: 09/20/2022] [Revised: 07/27/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.
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Affiliation(s)
- Vincent Andrearczyk
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
| | - Valentin Oreiller
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France; Poitiers University Hospital, nuclear medicine, Poitiers, France
| | - Olena Tankyevych
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France; Poitiers University Hospital, nuclear medicine, Poitiers, France
| | - Hesham Elhalawani
- Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, United States of America
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Adrien Depeursinge
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
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Ning J, Li C, Yu P, Cui J, Xu X, Jia Y, Zuo P, Tian J, Kenner L, Xu B. Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [ 18F]FDG and [ 18F]FLT PET/CT. Insights Imaging 2023; 14:197. [PMID: 37980611 PMCID: PMC10657912 DOI: 10.1186/s13244-023-01530-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 09/25/2023] [Indexed: 11/21/2023] Open
Abstract
PURPOSE To investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs). METHODS Data of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs. RESULTS A total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively). CONCLUSION Radiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs. CLINICAL RELEVANCE STATEMENT Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT. KEY POINTS • Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes. • Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects. • Radiomics helps us get to the histological view in a non-invasive approach.
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Affiliation(s)
- Jing Ning
- Department of Nuclear Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, China
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Vienna, Austria
- Department of Clinical Pathology, Vienna General Hospital, Vienna, Austria
| | - Can Li
- Department of Nuclear Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Peng Yu
- Department of Nuclear Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jingjing Cui
- United Imaging Intelligence (Beijing) Co., Ltd., Beijing, China Yongteng North Road, Haidian District, Beijing, China
| | - Xiaodan Xu
- Department of Nuclear Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yan Jia
- Huiying Medical Technology Co., Ltd., Room C103, B2, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Panli Zuo
- Huiying Medical Technology Co., Ltd., Room C103, B2, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Jiahe Tian
- Department of Nuclear Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lukas Kenner
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria.
| | - Baixuan Xu
- Department of Nuclear Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, China.
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Shiri I, Razeghi B, Vafaei Sadr A, Amini M, Salimi Y, Ferdowsi S, Boor P, Gündüz D, Voloshynovskiy S, Zaidi H. Multi-institutional PET/CT image segmentation using federated deep transformer learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107706. [PMID: 37506602 DOI: 10.1016/j.cmpb.2023.107706] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/02/2023] [Indexed: 07/30/2023]
Abstract
BACKGROUND AND OBJECTIVE Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets between different centers. To overcome these challenges, we developed a federated learning (FL) framework for multi-institutional PET/CT image segmentation. METHODS A dataset consisting of 328 FL (HN) cancer patients who underwent clinical PET/CT examinations gathered from six different centers was enrolled. A pure transformer network was implemented as fully core segmentation algorithms using dual channel PET/CT images. We evaluated different frameworks (single center-based, centralized baseline, as well as seven different FL algorithms) using 68 PET/CT images (20% of each center data). In particular, the implemented FL algorithms include clipping with the quantile estimator (ClQu), zeroing with the quantile estimator (ZeQu), federated averaging (FedAvg), lossy compression (LoCo), robust aggregation (RoAg), secure aggregation (SeAg), and Gaussian differentially private FedAvg with adaptive quantile clipping (GDP-AQuCl). RESULTS The Dice coefficient was 0.80±0.11 for both centralized and SeAg FL algorithms. All FL approaches achieved centralized learning model performance with no statistically significant differences. Among the FL algorithms, SeAg and GDP-AQuCl performed better than the other techniques. However, there was no statistically significant difference. All algorithms, except the center-based approach, resulted in relative errors less than 5% for SUVmax and SUVmean for all FL and centralized methods. Centralized and FL algorithms significantly outperformed the single center-based baseline. CONCLUSIONS The developed FL-based (with centralized method performance) algorithms exhibited promising performance for HN tumor segmentation from PET/CT images.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Sohrab Ferdowsi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Deniz Gündüz
- Department of Electrical and Electronic Engineering, Imperial College London, UK
| | | | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, University of Geneva, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Zhang W, Ray S. From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images. FRONTIERS IN RADIOLOGY 2023; 3:1225215. [PMID: 37745205 PMCID: PMC10512384 DOI: 10.3389/fradi.2023.1225215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023]
Abstract
With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, such as intensity modulated radiation therapy (IMRT) dose painting, demand the precise delineation of multiple intensity value contours to ensure optimal tumour dose distribution. Recently, convolutional neural networks (CNNs) have made significant strides in 3D image segmentation tasks, most of which present the output map at a voxel-wise level. However, because of information loss in subsequent downsampling layers, they frequently fail to precisely identify precise object boundaries. Moreover, in the context of dose painting strategies, there is an imperative need for reliable and precise image segmentation techniques to delineate high recurrence-risk contours. To address these challenges, we introduce a 3D coarse-to-fine framework, integrating a CNN with a kernel smoothing-based probability volume contour approach (KsPC). This integrated approach generates contour-based segmentation volumes, mimicking expert-level precision and providing accurate probability contours crucial for optimizing dose painting/IMRT strategies. Our final model, named KsPC-Net, leverages a CNN backbone to automatically learn parameters in the kernel smoothing process, thereby obviating the need for user-supplied tuning parameters. The 3D KsPC-Net exploits the strength of KsPC to simultaneously identify object boundaries and generate corresponding probability volume contours, which can be trained within an end-to-end framework. The proposed model has demonstrated promising performance, surpassing state-of-the-art models when tested against the MICCAI 2021 challenge dataset (HECKTOR).
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Affiliation(s)
- Wenhui Zhang
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
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20
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Gålne A, Enqvist O, Sundlöv A, Valind K, Minarik D, Trägårdh E. AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT. Eur J Hybrid Imaging 2023; 7:14. [PMID: 37544941 PMCID: PMC10404578 DOI: 10.1186/s41824-023-00172-7] [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: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images. METHODS A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. RESULTS There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians. CONCLUSION It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.
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Affiliation(s)
- Anni Gålne
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden.
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden.
| | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Anna Sundlöv
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Lund, Sweden
| | - Kristian Valind
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
| | - David Minarik
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
- Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden
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Li C, Yang Y, Hu F, Xu Y, Wu B, Huang J, Yang K, Lan X. Evaluation of 11 C-Choline PET/CT for T Staging and Tumor Volume Delineation in Nasopharyngeal Cancer Patients in Comparison to 18 F-FDG PET/CT. Clin Nucl Med 2023; 48:563-573. [PMID: 37115936 DOI: 10.1097/rlu.0000000000004645] [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: 04/30/2023]
Abstract
PURPOSE Accurate determination of the primary tumor extension of nasopharyngeal carcinoma (NPC) by 18 F-FDG PET/CT is limited by the high physiological 18 F-FDG uptake in the surrounding area, especially in the brain tissue. We aimed to assess whether 11 C-choline PET/CT could improve the accuracy of T staging and tumor volume delineation for NPC patients. METHODS Patients with pathologically confirmed diagnosis of NPC were enrolled. The primary tumor extension of each patient was evaluated by 11 C-choline PET/CT, 18 F-FDG PET/CT, and contrast-enhanced MRI. The PET/CT-based tumor volume ( VPET ) was measured by 3 threshold methods, including the threshold of SUV 2.5 (Th 2.5 ), 40% of maximal SUV (Th 40% ), and the relative background-dependent threshold (Th bgd ). Tumor volume and Dice similarity coefficient were compared among VPET with different segmentation methods and VMR . RESULTS Thirty-three patients with treatment-naive NPC and 6 patients with suspicious recurrent disease were enrolled. The NPC lesions were avid for both 11 C-choline and 18 F-FDG. Visual analysis showed that 11 C-choline PET/CT had better contrast and higher discernability than 18 F-FDG PET/CT for intracranial, skull base, and orbital involvement. 11 C-choline PET/CT also exhibited advantage over MRI for differentiation between local recurrence and radiation-induced alterations. For the tumor delineated, the VMR was larger than VPET in general, except for 18 F-FDG PET/CT with Th 2.5 threshold. For all 3 threshold methods applied, 11 C-choline PET/CT produced more consistent and comparable tumor volume to MRI than 18 F-FDG PET/CT. 11 C-choline PET/CT with Th bgd threshold showed the closest tumor volume and highest similarity to MRI. CONCLUSIONS 11 C-choline PET/CT provides a higher accuracy than 18 F-FDG PET/CT in mapping tumor extension in locally advanced NPC and may be a promising complement to MRI in delineating the primary tumor.
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Affiliation(s)
| | - Yuhui Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
| | | | | | - Bian Wu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
| | - Jing Huang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
| | - Kunyu Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
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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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23
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Wang W, Song S, Liu W, Xia T, Du G, Zhai X, Jin B. Two-photon excited luminescence of structural light enhancement in subwavelength SiO 2 coating europium ion-doped paramagnetic gadolinium oxide nanoparticle and application for magnetic resonance imaging. DISCOVER NANO 2023; 18:85. [PMID: 37382861 DOI: 10.1186/s11671-023-03864-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/01/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND Oxides of lanthanide rare-earth elements show great potential in the fields of imaging and therapeutics due to their unique electrical, optical and magnetic properties. Oxides of lanthanide-based nanoparticles enable high-resolution imaging of biological tissues by magnetic resonance imaging (MRI), computed tomography (CT) imaging, and fluorescence imaging. In addition, they can be used to detect, treat, and regulate diseases by fine-tuning their structure and function. It remains challenging to achieve safer, efficient, and more sensitive nanoparticles for clinical applications through the structural design of functional and nanostructured rare-earth materials. RESULT In this study, we designed a mesoporous silica-coated core-shell structure of europium oxide ions to obtain near-infrared two-photon excitation fluorescence while maintaining high contrast and resolution in MRI. We designed enhanced 800 nm photoexcitation nanostructures, which were simulated by the finite-difference method (FDM) and finite-difference time-domain method (FDTD). The nanoparticle structure, two-photon absorption, up-conversion fluorescence, magnetic properties, cytotoxicity, and MRI were investigated in vivo and in vitro. The nanoparticle has an extremely strong optical fluorescence response and multiple excitation peaks in the visible light band under the 405 nm continuous-wave laser excitation. The nanoparticle was found to possess typical optical nonlinearity induced by two-photon absorption by ultrafast laser Z-scan technique. Two-photon excited fluorescence of visible red light at wavelengths of 615 nm and 701 nm, respectively, under excitation of the more biocompatible near-infrared (pulsed laser at 800 nm). In an in vitro MRI study, a T1 relaxation rate of 6.24 mM-1 s-1 was observed. MRI in vivo showed that the nanoparticles could significantly enhance the signal intensity in liver tissue. CONCLUSIONS These results suggest that this sample has applied potential in visible light fluorescence imaging and MRI.
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Affiliation(s)
- Wei Wang
- Medical Integration and Practice Center, Shandong University, Jinan, Shandong, China
| | - Shangling Song
- Medical Equipment Department, The Second Hospital of Shandong University, Jinan, Shandong, China
| | - Wendong Liu
- Department of Hepatobiliary Surgery, The Second Hospital of Shandong University, Jinan, Shandong, China
| | - Tong Xia
- Organ Transplant Department, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Gang Du
- Organ Transplant Department, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiangyu Zhai
- Department of Hepatobiliary Surgery, The Second Hospital of Shandong University, Jinan, Shandong, China.
- Organ Transplant Department, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| | - Bin Jin
- Department of Hepatobiliary Surgery, The Second Hospital of Shandong University, Jinan, Shandong, China.
- Organ Transplant Department, Qilu Hospital of Shandong University, Jinan, Shandong, China.
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Wu Y, Jiang H, Pang W. MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention. Comput Biol Med 2023; 158:106818. [PMID: 36966557 DOI: 10.1016/j.compbiomed.2023.106818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 03/08/2023] [Accepted: 03/20/2023] [Indexed: 03/31/2023]
Abstract
Automatic Medical segmentation of medical images is an important part in the field of computer medical diagnosis, among which tumor segmentation is an important branch of medical image segmentation. Accurate automatic segmentation method is very important in medical diagnosis and treatment. Positron emission computed tomography (PET) and X-ray computed tomography (CT) images are widely used in medical image segmentation to help doctors accurately locate information such as tumor location and shape, providing metabolic and anatomical information, respectively. At present, PET/CT images have not been effectively combined in the research of medical image segmentation, and the complementary semantic information between the superficial and deep layers of neural network has not been ensured. To solve the above problems, this paper proposed a Multi-scale Residual Attention network (MSRA-Net) for tumor segmentation of PET/CT. We first use an attention-fusion based approach to automatically learn the tumor-related areas of PET images and weaken the irrelevant area. Then, the segmentation results of PET branch are processed to optimize the segmentation results of CT branch by using attention mechanism. The proposed neural network (MSRA-Net) can effectively fuse PET image and CT image, which can improve the precision of tumor segmentation by using complementary information of the multi-modal image, and reduce the uncertainty of single modal image segmentation. Proposed model uses multi-scale attention mechanism and residual module, which fuse multi-scale features to form complementary features of different scales. We compare with state-of-the-art medical image segmentation methods. The experiment showed that the Dice coefficient of the proposed network in soft tissue sarcoma and lymphoma datasets increased by 8.5% and 6.1% respectively compared with UNet, showing a significant improvement.
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25
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Delaby N, Barateau A, Chiavassa S, Biston MC, Chartier P, Graulières E, Guinement L, Huger S, Lacornerie T, Millardet-Martin C, Sottiaux A, Caron J, Gensanne D, Pointreau Y, Coutte A, Biau J, Serre AA, Castelli J, Tomsej M, Garcia R, Khamphan C, Badey A. Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view. Phys Med 2023; 109:102568. [PMID: 37015168 DOI: 10.1016/j.ejmp.2023.102568] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 02/15/2023] [Accepted: 03/18/2023] [Indexed: 04/05/2023] Open
Abstract
Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.
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26
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Nigam R, Field M, Harris G, Barton M, Carolan M, Metcalfe P, Holloway L. Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images. Phys Eng Sci Med 2023; 46:851-863. [PMID: 37126152 DOI: 10.1007/s13246-023-01258-z] [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: 12/04/2022] [Accepted: 04/11/2023] [Indexed: 05/02/2023]
Abstract
Non-small cell lung cancer (NSCLC) patients with the metastatic spread of disease to the bone have high morbidity and mortality. Stereotactic ablative body radiotherapy increases the progression free survival and overall survival of these patients with oligometastases. FDG-PET/CT, a functional imaging technique combining positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) and computer tomography (CT) provides improved staging and identification of treatment response. It is also associated with reduction in size of the radiotherapy tumour volume delineation compared with CT based contouring in radiotherapy, thus allowing for dose escalation to the target volume with lower doses to the surrounding organs at risk. FDG-PET/CT is increasingly being used for the clinical management of NSCLC patients undergoing radiotherapy and has shown high sensitivity and specificity for the detection of bone metastases in these patients. Here, we present a software tool for detection, delineation and quantification of bone metastases using FDG-PET/CT images. The tool extracts standardised uptake values (SUV) from FDG-PET images for auto-segmentation of bone lesions and calculates volume of each lesion and associated mean and maximum SUV. The tool also allows automatic statistical validation of the auto-segmented bone lesions against the manual contours of a radiation oncologist. A retrospective review of FDG-PET/CT scans of more than 30 candidate NSCLC patients was performed and nine patients with one or more metastatic bone lesions were selected for the present study. The SUV threshold prediction model was designed by splitting the cohort of patients into a subset of 'development' and 'validation' cohorts. The development cohort yielded an optimum SUV threshold of 3.0 for automatic detection of bone metastases using FDG-PET/CT images. The validity of the derived optimum SUV threshold on the validation cohort demonstrated that auto-segmented and manually contoured bone lesions showed strong concordance for volume of bone lesion (r = 0.993) and number of detected lesions (r = 0.996). The tool has various applications in radiotherapy, including but not limited to studies determining optimum SUV threshold for accurate and standardised delineation of bone lesions and in scientific studies utilising large patient populations for instance for investigation of the number of metastatic lesions that can be treated safety with an ablative dose of radiotherapy without exceeding the normal tissue toxicity.
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Affiliation(s)
- R Nigam
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia.
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia.
- Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, NSW, 2500, Australia.
| | - M Field
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, 2170, Australia
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - G Harris
- Chris O'Brien Lifehouse, Camperdown, NSW, 2050, Australia
| | - M Barton
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, 2170, Australia
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - M Carolan
- Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, NSW, 2500, Australia
| | - P Metcalfe
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
| | - L Holloway
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, 2522, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool, NSW, 2170, Australia
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
- Institute of Medical Physics, University of Sydney, Camperdown, NSW, 2505, Australia
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27
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Park J, Kang SK, Hwang D, Choi H, Ha S, Seo JM, Eo JS, Lee JS. Automatic Lung Cancer Segmentation in [ 18F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nucl Med Mol Imaging 2023; 57:86-93. [PMID: 36998591 PMCID: PMC10043063 DOI: 10.1007/s13139-022-00745-7] [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: 09/23/2021] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 10/18/2022] Open
Abstract
Purpose Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [18F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [18F]FDG PET/CT. Methods The whole-body [18F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output. Results The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. Conclusion The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [18F]FDG PET/CT.
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Affiliation(s)
- Junyoung Park
- Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, 08826 Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Seung Kwan Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
- Brightonix Imaging Inc., Seoul, 03080 Korea
| | - Donghwi Hwang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St Mary’s Hospital, The Catholic University of Korea, Seoul, 06591 Korea
| | - Jong Mo Seo
- Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, 08826 Korea
| | - Jae Seon Eo
- Department of Nuclear Medicine, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308 Korea
| | - Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
- Brightonix Imaging Inc., Seoul, 03080 Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 Korea
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Systematic Review of Tumor Segmentation Strategies for Bone Metastases. Cancers (Basel) 2023; 15:cancers15061750. [PMID: 36980636 PMCID: PMC10046265 DOI: 10.3390/cancers15061750] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.
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Wang F, Cheng C, Cao W, Wu Z, Wang H, Wei W, Yan Z, Liu Z. MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images. Comput Biol Med 2023; 155:106657. [PMID: 36791551 DOI: 10.1016/j.compbiomed.2023.106657] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 01/29/2023] [Accepted: 02/09/2023] [Indexed: 02/12/2023]
Abstract
In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.
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Affiliation(s)
- Fei Wang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China; Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Chao Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University(Changhai Hospital), Shanghai, 200433, China
| | - Weiwei Cao
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhongyi Wu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Heng Wang
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Wenting Wei
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
| | - Zhaobang Liu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
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Zhang Y, Wang F, Wu H, Yang Y, Xu W, Wang S, Chen W, Lu L. An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107267. [PMID: 36502547 DOI: 10.1016/j.cmpb.2022.107267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/16/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVES We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated 13N-NH3 PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment. METHODS Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth. We developed an automatic segmentation method, which introduces the self-attention mechanism into 3D U-Net to capture global information of images so as to achieve fine segmentation of LV. Three cross-validation tests were performed on each gate (64 vs. 32 for training vs. validation). The effectiveness was validated by quantitative metrics (modified hausdorff distance, MHD; dice ratio, DR; 3D MHD) as well as cardiac functional parameters (end-systolic volume, ESV; end-diastolic volume, EDV; ejection fraction, EF). Furthermore, the feasibility of the proposed method was also evaluated by intra- and inter-observers with DR and 3D-MHD. RESULTS Compared with backbone network, the proposed approach improved the average DR from 0.905 ± 0.0193 to 0.9202 ± 0.0164, and decreased the average 3D MHD from 0.4611 ± 0.0349 to 0.4304 ± 0.0339. The average relative error of LV volume between proposed method and ground truth is 1.09±3.66%, and the correlation coefficient is 0.992 ± 0.007 (P < 0.001). The EDV, ESV, EF deduced from the proposed approach were highly correlated with ground truth (r ≥ 0.864, P < 0.001), and the correlation with commercial software is fair (r ≥ 0.871, P < 0.001). DR and 3D MHD of contours and myocardium from two observers are higher than 0.899 and less than 0.5194. CONCLUSION The proposed approach is highly feasible for automatic segmentation of the LV cavity and myocardium, with potential to benefit the precision of LV function assessment.
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Affiliation(s)
- Yangmei Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Fanghu Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Huiqin Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yuling Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Weiping Xu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Shuxia Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China; Pazhou Lab, Guangzhou, China.
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Liu Z, Mhlanga JC, Siegel BA, Jha AK. Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12467:124670R. [PMID: 37990707 PMCID: PMC10659582 DOI: 10.1117/12.2647894] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance. One such widely used metric is the Dice score, a figure of merit that measures the spatial overlap between the estimated segmentation and a reference standard (e.g., manual segmentation). In this work, we investigated whether evaluating AI-based segmentation methods using Dice scores yields a similar interpretation as evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumor from PET images of patients with non-small cell lung cancer. The investigation was conducted via a retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical trial data. Specifically, we evaluated different structures of a commonly used AI-based segmentation method using both Dice scores and the accuracy in quantifying MTV/TLG. Our results show that evaluation using Dice scores can lead to findings that are inconsistent with evaluation using the task-based figure of merit. Thus, our study motivates the need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.
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Affiliation(s)
- Ziping Liu
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Joyce C. Mhlanga
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Barry A. Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Abhinav K. Jha
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
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Dynamic PET images denoising using spectral graph wavelet transform. Med Biol Eng Comput 2023; 61:97-107. [PMID: 36323982 DOI: 10.1007/s11517-022-02698-7] [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: 10/12/2020] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Positron emission tomography (PET) is a non-invasive molecular imaging method for quantitative observation of physiological and biochemical changes in living organisms. The quality of the reconstructed PET image is limited by many different physical degradation factors. Various denoising methods including Gaussian filtering (GF) and non-local mean (NLM) filtering have been proposed to improve the image quality. However, image denoising usually blurs edges, of which high frequency components are filtered as noises. On the other hand, it is well-known that edges in a PET image are important to detection and recognition of a lesion. Denoising while preserving the edges of PET images remains an important yet challenging problem in PET image processing. In this paper, we propose a novel denoising method with good edge-preserving performance based on spectral graph wavelet transform (SGWT) for dynamic PET images denoising. We firstly generate a composite image from the entire time series, then perform SGWT on the PET images, and finally reconstruct the low graph frequency content to get the denoised dynamic PET images. Experimental results on simulation and in vivo data show that the proposed approach significantly outperforms the GF, NLM and graph filtering methods. Compared with deep learning-based method, the proposed method has the similar denoising performance, but it does not need lots of training data and has low computational complexity.
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Lei Y, Wang T, Jeong JJ, Janopaul-Naylor J, Kesarwala AH, Roper J, Tian S, Bradley JD, Liu T, Higgins K, Yang X. Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network. Med Phys 2023; 50:274-283. [PMID: 36203393 PMCID: PMC9868056 DOI: 10.1002/mp.16001] [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: 04/16/2021] [Revised: 09/20/2022] [Accepted: 09/20/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. PURPOSE In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. METHODS The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis. RESULTS In fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. CONCLUSION The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Jiwoong J Jeong
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - James Janopaul-Naylor
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Aparna H Kesarwala
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Sibo Tian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Jeffrey D Bradley
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Kristin Higgins
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, School of Medicine, Atlanta, Georgia, USA
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Liu G, Chen S, Hu Y, Cao S, Yang X, Zhou Y, Shi H. Respiratory-gated PET imaging with reduced acquisition time for suspect malignancies: the first experience in application of total-body PET/CT. Eur Radiol 2022; 33:3366-3376. [PMID: 36565352 DOI: 10.1007/s00330-022-09369-z] [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: 07/09/2022] [Revised: 09/08/2022] [Accepted: 12/08/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVES This study aimed to investigate the performance of respiratory-gating imaging with reduced acquisition time using the total-body positron emission tomography/computed tomography (PET/CT) scanner. METHODS Imaging data of 71 patients with suspect malignancies who underwent total-body 2-[18F]-fluoro-2-deoxy-D-glucose PET/CT for 15 min with respiration recorded were analyzed. For each examination, four reconstructions were performed: Ungated-15, using all coincidences; Ungated-5, using data of the first 5 min; Gated-15 using all coincidences but with respiratory gating; and Gated-6 using data of the first 6 min with respiratory gating. Lesions were quantified and image quality was evaluated; both were compared between the four image sets. RESULTS A total of 390 lesions were found in the thorax and upper abdomen. Lesion detectability was significantly higher in gated-15 (97.2%) than in ungated-15 (93.6%, p = 0.001) and ungated-5 (92.3%, p = 0.001), but comparable to Gated-6 (95.9%, p = 0.993). A total of 131 lesions were selected for quantitative analyses. Lesions in Gated-15 presented significantly larger standardized uptake values, tumor-to-liver ratio, and tumor-to-blood ratio, but smaller metabolic tumor volume, compared to those in Ungated-15 and Ungated-5 (all p < 0.001). These differences were more obvious in small lesions and in lesions from sites other than mediastinum/retroperitoneum. However, these indices were not significantly different between Gated-15 and Gated-6. Higher, but acceptable, image noise was identified in gated images than in ungated images. CONCLUSIONS Respiratory-gating imaging with reduced scanning time using the total-body PET/CT scanner is superior to ungated imaging and can be used in the clinic. KEY POINTS • In PET imaging, respiratory gating can improve lesion presentation and detectability but requires longer imaging time. • This single-center study showed that the total-body PET scanner allows respiratory-gated imaging with reduced and clinically acceptable scanning time.
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Affiliation(s)
- Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, China.,Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, China.,Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Shuguang Chen
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, China.,Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, China.,Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yan Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, China.,Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, China.,Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Shuangliang Cao
- Central Research Institute, United Imaging Healthcare Group Co., Ltd., Shanghai, 201807, China
| | - Xinlan Yang
- Central Research Institute, United Imaging Healthcare Group Co., Ltd., Shanghai, 201807, China
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group Co., Ltd., Shanghai, 201807, China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, China. .,Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China. .,Shanghai Institute of Medical Imaging, Shanghai, 200032, China. .,Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Payan N, Presles B, Truntzer C, Courcet E, Coutant C, Desmoulins I, Brunotte F, Vrigneaud JM, Cochet A. Critical analysis of the effect of various methodologies to compute breast cancer tumour blood flow-based texture features using first-pass 18F-FDG PET. Phys Med 2022; 103:98-107. [DOI: 10.1016/j.ejmp.2022.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 11/26/2022] Open
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Fedrigo R, Segars WP, Martineau P, Gowdy C, Bloise I, Uribe CF, Rahmim A. Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations. Med Phys 2022; 49:6871-6884. [PMID: 36053829 PMCID: PMC9742182 DOI: 10.1002/mp.15963] [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: 06/27/2022] [Revised: 08/01/2022] [Accepted: 08/16/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Digital anthropomorphic phantoms, such as the 4D extended cardiac-torso (XCAT) phantom, are actively used to develop, optimize, and evaluate a variety of imaging applications, allowing for realistic patient modeling and knowledge of ground truth. The XCAT phantom defines the activity and attenuation for a simulated patient, which includes a complete set of organs, muscle, bone, and soft tissue, while also accounting for cardiac and respiratory motion. However, the XCAT phantom does not currently include the lymphatic system, critical for evaluating medical imaging tasks such as sentinel node detection, node density measurement, and radiation dosimetry. PURPOSE In this study, we aimed to develop a scalable lymphatic system in the XCAT phantom, to facilitate improved research of the lymphatic system in medical imaging. Using this scalable lymphatic system, we modeled the lymph node conglomerate pathology that is characteristically observed in primary mediastinal B-cell lymphoma (PMBCL). As an extended application, we evaluated positron emission tomography (PET) image quantification of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of these simulated lymphomas, though the phantoms may be applied to other imaging modalities and study design paradigms (e.g., image quality, detection). METHODS A template model for the lymphatic system was developed based on anatomical data from the Visible Human Project of the National Library of Medicine. The segmented nodes and vessels were fit with non-uniform rational basis spline surfaces, and multichannel large deformation diffeomorphic metric mapping was used to propagate the template to different XCAT anatomies. To model conglomerates observed in PMBCL, lymph nodes were enlarged, converged within the mediastinum, and tracer concentration was increased. We used the phantoms as inputs to a PET simulation tool, which generated images using ordered subsets expectation maximization reconstruction with 2-8 mm Gaussian filters. Fixed thresholding (FT) and gradient segmentation were used to determine MTV and TLG. Percent bias (%Bias) and coefficient of variation (COV) were computed as measures of accuracy and precision, respectively, for each MTV and TLG measurement. RESULTS Using the methodology described above, we introduced a scalable lymphatic system in the XCAT phantom, which allows for the radioactivity and attenuation ground truth to be generated in 116 ± 2.5 s using a 2.3 GHz processor. Within the Rhinoceros interface, lymph node anatomy and function were modified to create a cohort of 10 phantoms with lymph node conglomerates. Using the lymphoma phantoms to evaluate PET quantification of MTV, mean %Bias values were -9.3%, -41.3%, and 20.9%, while COV values were 4.08%, 7.6%, and 3.4% using 25% FT, 40% FT, and gradient segmentations, respectively. Comparatively for TLG, mean %Bias values were -27.4%, -45.8%, and -16.0%, while COV values were 1.9%, 5.7%, and 1.4%, for the 25% FT, 40% FT, and gradient segmentations, respectively. CONCLUSIONS In this work, we upgraded the XCAT phantom to include a lymphatic system, comprised of a network of 276 scalable lymph nodes and corresponding vessels. As an application, we created a cohort of phantoms with lymph node conglomerates to evaluate lymphoma quantification in PET imaging, which highlights an important application of this work.
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Affiliation(s)
- Roberto Fedrigo
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | | | | | - Claire Gowdy
- Department of Radiology, BC Children’s Hospital, Vancouver, BC V6H 0B3, Canada
| | - Ingrid Bloise
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Carlos F. Uribe
- Functional Imaging, BC Cancer, Vancouver, BC V5Z 4E6, Canada
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 2B5, Canada
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Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions. Methods Protoc 2022; 5:mps5050078. [PMID: 36287050 PMCID: PMC9611546 DOI: 10.3390/mps5050078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 12/05/2022] Open
Abstract
Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).
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Bamneshin K, Rabi Mahdavi S, Bitarafan-Rajabi A, Geramifar P, Hejazi P, Jadidi M. Breathing-induced Errors in Quantification and Description of Dominant Intra-Prostatic Lesions (Dils) in PET Images: A Simulation Study by Means of The 4D NCAT Phantom. J Biomed Phys Eng 2022; 12:497-504. [PMID: 36313408 PMCID: PMC9589085 DOI: 10.31661/jbpe.v0i0.1912-1015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 05/25/2020] [Indexed: 06/16/2023]
Abstract
BACKGROUND Respiratory movement and the motion range of the diaphragm can affect the quality and quantity of prostate images. OBJECTIVE This study aimed to investigate the magnitude of respiratory-induced errors to determine Dominant Intra- prostatic Lesions (DILs) in positron emission tomography (PET) images. MATERIAL AND METHODS In this simulation study, we employed the 4D NURBS-based cardiac-torso (4D-NCAT) phantom with a realistic breathing model to simulate the respiratory cycles of a patient to assess the displacement, volume, maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), signal to noise ratio (SNR), and the contrast of DILs in frames within the respiratory cycle. RESULTS Respiration in a diaphragm motion resulted in the maximum superior-inferior displacement of 3.9 and 6.1 mm, and the diaphragm motion amplitudes of 20 and 35 mm. In a no-motion image, the volume measurement of DILs had the smallest percentage of errors. Compared with the no-motion method, the percentages of errors in the average method in 20 and 35 mm- diaphragm motion were 25% and 105%, respectively. The motion effect was significantly reduced in terms of the values of SUVmax and SUVmean in comparison with the values of SUVmax and SUVmean in no- motion images. The contrast values in respiratory cycle frames were at a range of 3.3-19.2 mm and 6.5-46 for diaphragm movements' amplitudes of 20 and 35 mm. CONCLUSION The respiratory movement errors in quantification and delineation of DILs were highly dependent on the range of motion, while the average method was not suitable to precisely delineate DILs in PET/CT in the dose-painting technique.
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Affiliation(s)
- Khadijeh Bamneshin
- PhD, Department of Radiology Technology, Faculty of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran
- PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- PhD, Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- PhD, Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- PhD, Department of Nuclear Medicine, Shariati Hospital Tehran University of Medical Sciences, Tehran, Iran
| | - Payman Hejazi
- PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Jadidi
- PhD, Department of Radiology Technology, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Zhu Y, Ruan G, Zou S, Liu L, Zhu X. Age-matched control or age-specific template, which is essential for voxel-wise analysis of cerebral metabolism abnormality in pediatric patients with epilepsy? Hum Brain Mapp 2022; 44:472-483. [PMID: 36069128 PMCID: PMC9842903 DOI: 10.1002/hbm.26063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 08/10/2022] [Accepted: 08/21/2022] [Indexed: 01/25/2023] Open
Abstract
The aim of this study was to explore the influences of age-matched control and/or age-specific template on voxel-wise analysis of brain 18 F-fluorodeoxyglucose positron emission tomography (18 F-FDG PET) data in pediatric epilepsy patients. We, retrospectively, included 538 pediatric (196 females; age range of 12 months to 18 years) and 35 adult subjects (18 females; age range of 20-50 years) without any cerebral pathology as pediatric and adult control group, respectively, as well as 109 pediatric patients with drug-resistant epilepsy (38 females; age range of 13 months to 18 years) as epilepsy group. Statistical parametric mapping (SPM) analysis for 18 F-FDG PET data of each epilepsy patients was performed in four types of procedures, by using age-matched controls with age-specific template, age-matched controls with adult template, adult controls with age-specific template or adult controls with adult template. The numbers of brain regions affected by artifacts among these four types of SPM analysis procedures were further compared. Any template being adopted, the artifacts were significantly less in SPM analysis procedures using age-matched controls than those using adult controls in each age range (p < .001 in each comparison), except in the age range of 15-18 (p > .05 in each comparison). No significant difference was found in artifacts, when compared procedures using the identical control group with different templates (p = 1.000 in each comparison). In conclusion, the age stratification for age-matched control should be divided as many layers as possible for the SPM analysis of brain 18 F-FDG PET images, especially in pediatric patients ≤14-year-old, while age-specific template is not mandatory.
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Affiliation(s)
- Yuankai Zhu
- Department of Nuclear Medicine and PET CenterTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Ge Ruan
- Department of RadiologyHospital, Hubei UniversityWuhanChina
| | - Sijuan Zou
- Department of Nuclear Medicine and PET CenterTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Luoxia Liu
- Department of Nuclear Medicine and PET CenterTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
| | - Xiaohua Zhu
- Department of Nuclear Medicine and PET CenterTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanChina
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Convolution Neural Networks for the Automatic Segmentation of 18F-FDG PET Brain as an Aid to Alzheimer’s Disease Diagnosis. ELECTRONICS 2022. [DOI: 10.3390/electronics11142260] [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
Our work aims to exploit deep learning (DL) models to automatically segment diagnostic regions involved in Alzheimer’s disease (AD) in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) volumetric scans in order to provide a more objective diagnosis of this disease and to reduce the variability induced by manual segmentation. The dataset used in this study consists of 102 volumes (40 controls, 39 with established Alzheimer’s disease (AD), and 23 with established mild cognitive impairment (MCI)). The ground truth was generated by an expert user who identified six regions in original scans, including temporal lobes, parietal lobes, and frontal lobes. The implemented architectures are the U-Net3D and V-Net networks, which were appropriately adapted to our data to optimize performance. All trained segmentation networks were tested on 22 subjects using the Dice similarity coefficient (DSC) and other similarity indices, namely the overlapping area coefficient (AOC) and the extra area coefficient (EAC), to evaluate automatic segmentation. The results of each labeled brain region demonstrate an improvement of 50%, with DSC from about 0.50 for V-Net-based networks to about 0.77 for U-Net3D-based networks. The best performance was achieved by using U-Net3D, with DSC on average equal to 0.76 for frontal lobes, 0.75 for parietal lobes, and 0.76 for temporal lobes. U-Net3D is very promising and is able to segment each region and each class of subjects without being influenced by the presence of hypometabolic regions.
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Reddy RP, Ross Schmidtlein C, Giancipoli RG, Mauguen A, LaFontaine D, Schoder H, Bodei L. The Quest for an Accurate Functional Tumor Volume with 68Ga-DOTATATE PET/CT. J Nucl Med 2022; 63:1027-1032. [PMID: 34772795 PMCID: PMC9258575 DOI: 10.2967/jnumed.121.262782] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/25/2021] [Indexed: 01/03/2023] Open
Abstract
68Ga-labeled somatostatin analog (SSA) PET/CT is now a standard-of-care component in the management of neuroendocrine tumors (NETs). However, treatment response for NETs is still assessed with morphologic size measurements from other modalities, which can result in inaccuracy about the disease burden. Functional tumor volume (FTV) acquired from SSA PET/CT has been suggested as a possible metric, but no validated measurement tool to measure FTV exists. We tested the precision of multiple FTV computational approaches compared with morphologic volume measurements to identify a candidate for incorporation into future FTV studies to assess tumor burden more completely and accurately. Methods: The clinical and imaging data of 327 NET patients were collected at Memorial Sloan Kettering Cancer Center between December 2016 and April 2018. Patients were required to have SSA PET/CT and dedicated CT scans within 6 wk and were excluded if they had any intervention between scans. When paired studies were evaluated, 150 correlating lesions demonstrated SSA. Lesions were excluded if they contained necrotic components or were lobulated. This exclusion resulted in 94 lesions in 20 patients. The FTV for each lesion was evaluated with a hand-drawn assessment and 3 automated techniques: 50% threshold from SUVmax, 42% threshold from SUVmax, and background-subtracted lesion activity. These measurements were compared with volume calculated from morphologic volume measurements. Results: The FTV calculation methods showed varying correlations with morphologic volume measurements. FTV using a 42% threshold had a 0.706 correlation, hand-drawn volume from PET imaging had a 0.657 correlation, FTV using a 50% threshold had a 0.645 correlation, and background-subtracted lesion activity had a 0.596 correlation. The Bland-Altman plots indicated that all FTV methods had a positive mean difference from morphologic volume, with a 50% threshold showing the smallest mean difference. Conclusion: FTV determined with thresholding of SUVmax demonstrated the strongest correlation with traditional morphologic lesion volume assessment and the least bias. This method was more accurate than FTV calculated from hand-drawn volume assessments. Threshold-based automated FTV assessment promises to better determine disease extent and prognosis in patients with NETs.
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Affiliation(s)
- Ryan P. Reddy
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New, York, New York
| | - C. Ross Schmidtlein
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New, York, New York
| | - Romina G. Giancipoli
- Department of Nuclear Medicine, La Sapienza University of Rome, Rome, Italy; and
| | - Audrey Mauguen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daniel LaFontaine
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New, York, New York
| | - Heiko Schoder
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New, York, New York
| | - Lisa Bodei
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New, York, New York
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Fang L, Jiang Y, Ren X. Cerebral hemorrhage segmentation with energy functional based on anatomy theory. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Liu Z, Moon HS, Li Z, Laforest R, Perlmutter JS, Norris SA, Jha AK. A tissue‐fraction estimation‐based segmentation method for quantitative dopamine transporter SPECT. Med Phys 2022; 49:5121-5137. [PMID: 35635327 PMCID: PMC9703616 DOI: 10.1002/mp.15778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/25/2022] [Accepted: 05/16/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter-single-photon emission computed tomography (DaT-SPECT) images have potential as biomarkers for measuring the severity of Parkinson's disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT-SPECT images is challenging, a major reason being partial-volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite-sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT-SPECT images. PURPOSE Design and objectively evaluate a fully automated tissue-fraction estimation-based segmentation method that segments the caudate, putamen, and GP from DaT-SPECT images. METHODS The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three-dimensional DaT-SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep-learning-based approach. RESULTS Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed ( p < 0.01 $p < 0.01$ ) all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) < 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen. CONCLUSIONS The proposed tissue-fraction estimation-based segmentation method for DaT-SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical-phantom and patient studies.
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Affiliation(s)
- Ziping Liu
- Department of Biomedical Engineering Washington University St. Louis MO 63130 United States of America
| | - Hae Sol Moon
- Department of Biomedical Engineering Washington University St. Louis MO 63130 United States of America
| | - Zekun Li
- Department of Biomedical Engineering Washington University St. Louis MO 63130 United States of America
| | - Richard Laforest
- Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MO 63110 United States of America
| | - Joel S. Perlmutter
- Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MO 63110 United States of America
- Department of Neurology Washington University School of Medicine St. Louis MO 63110 United States of America
| | - Scott A. Norris
- Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MO 63110 United States of America
- Department of Neurology Washington University School of Medicine St. Louis MO 63110 United States of America
| | - Abhinav K. Jha
- Department of Biomedical Engineering Washington University St. Louis MO 63130 United States of America
- Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis MO 63110 United States of America
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Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In this paper, we present an approach to fully automate tumor delineation in positron emission tomography (PET) images. PET images play a major role in medicine for in vivo imaging in oncology (PET images are used to evaluate oncology patients, detecting emitted photons from a radiotracer localized in abnormal cells). PET image tumor delineation plays a vital role both in pre- and post-treatment stages. The low spatial resolution and high noise characteristics of PET images increase the challenge in PET image segmentation. Despite the difficulties and known limitations, several image segmentation approaches have been proposed. This paper introduces a new unsupervised approach to perform tumor delineation in PET images using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions. Moreover, the implementation of this methodology is presented and tested against other existing methodologies. The proposed algorithm increases the accuracy of tumor delineation in PET images, and the experimental results show that the proposed method outperformed all methods tested.
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Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework. Clin Nucl Med 2022; 47:606-617. [PMID: 35442222 DOI: 10.1097/rlu.0000000000004194] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE The generalizability and trustworthiness of deep learning (DL)-based algorithms depend on the size and heterogeneity of training datasets. However, because of patient privacy concerns and ethical and legal issues, sharing medical images between different centers is restricted. Our objective is to build a federated DL-based framework for PET image segmentation utilizing a multicentric dataset and to compare its performance with the centralized DL approach. METHODS PET images from 405 head and neck cancer patients from 9 different centers formed the basis of this study. All tumors were segmented manually. PET images converted to SUV maps were resampled to isotropic voxels (3 × 3 × 3 mm3) and then normalized. PET image subvolumes (12 × 12 × 12 cm3) consisting of whole tumors and background were analyzed. Data from each center were divided into train/validation (80% of patients) and test sets (20% of patients). The modified R2U-Net was used as core DL model. A parallel federated DL model was developed and compared with the centralized approach where the data sets are pooled to one server. Segmentation metrics, including Dice similarity and Jaccard coefficients, percent relative errors (RE%) of SUVpeak, SUVmean, SUVmedian, SUVmax, metabolic tumor volume, and total lesion glycolysis were computed and compared with manual delineations. RESULTS The performance of the centralized versus federated DL methods was nearly identical for segmentation metrics: Dice (0.84 ± 0.06 vs 0.84 ± 0.05) and Jaccard (0.73 ± 0.08 vs 0.73 ± 0.07). For quantitative PET parameters, we obtained comparable RE% for SUVmean (6.43% ± 4.72% vs 6.61% ± 5.42%), metabolic tumor volume (12.2% ± 16.2% vs 12.1% ± 15.89%), and total lesion glycolysis (6.93% ± 9.6% vs 7.07% ± 9.85%) and negligible RE% for SUVmax and SUVpeak. No significant differences in performance (P > 0.05) between the 2 frameworks (centralized vs federated) were observed. CONCLUSION The developed federated DL model achieved comparable quantitative performance with respect to the centralized DL model. Federated DL models could provide robust and generalizable segmentation, while addressing patient privacy and legal and ethical issues in clinical data sharing.
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Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images. HEAD AND NECK TUMOR SEGMENTATION AND OUTCOME PREDICTION : SECOND CHALLENGE, HECKTOR 2021, HELD IN CONJUNCTION WITH MICCAI 2021, STRASBOURG, FRANCE, SEPTEMBER 27, 2021, PROCEEDINGS. HEAD AND NECK TUMOR SEGMENTATION CHALLENGE (2ND : 2021 ... 2022; 13209:121-132. [PMID: 35399869 DOI: 10.1007/978-3-030-98253-9_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient (DSC) up to 0.771 and median 95% Hausdorff distance (95% HD) as low as 2.919 mm. We employ label fusion ensemble approaches, including Simultaneous Truth and Performance Level Estimation (STAPLE) and a voxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achieves a mean DSC of 0.770 and median 95% HD of 3.143 mm through independent external validation on the test set. Our DSC and 95% HD test results are within 0.01 and 0.06 mm of the top ranked model in the competition, respectively. Concordance of internal and external validation results suggests our models are robust and can generalize well to unseen PET/CT data. We advocate that ResUNet models coupled to label fusion ensembling approaches are promising candidates for PET/CT oropharyngeal primary tumors auto-segmentation. Future investigations should target the ideal combination of channel combinations and label fusion strategies to maximize segmentation performance.
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Wang X, Jemaa S, Fredrickson J, Coimbra AF, Nielsen T, De Crespigny A, Bengtsson T, Carano RAD. Heart and bladder detection and segmentation on FDG PET/CT by deep learning. BMC Med Imaging 2022; 22:58. [PMID: 35354384 PMCID: PMC8977865 DOI: 10.1186/s12880-022-00785-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 03/22/2022] [Indexed: 12/04/2022] Open
Abstract
Purpose Positron emission tomography (PET)/ computed tomography (CT) has been extensively used to quantify metabolically active tumors in various oncology indications. However, FDG-PET/CT often encounters false positives in tumor detection due to 18fluorodeoxyglucose (FDG) accumulation from the heart and bladder that often exhibit similar FDG uptake as tumors. Thus, it is necessary to eliminate this source of physiological noise. Major challenges for this task include: (1) large inter-patient variability in the appearance for the heart and bladder. (2) The size and shape of bladder or heart may appear different on PET and CT. (3) Tumors can be very close or connected to the heart or bladder. Approach A deep learning based approach is proposed to segment the heart and bladder on whole body PET/CT automatically. Two 3D U-Nets were developed separately to segment the heart and bladder, where each network receives the PET and CT as a multi-modal input. Data sets were obtained from retrospective clinical trials and include 575 PET/CT for heart segmentation and 538 for bladder segmentation. Results The models were evaluated on a test set from an independent trial and achieved a Dice Similarity Coefficient (DSC) of 0.96 for heart segmentation and 0.95 for bladder segmentation, Average Surface Distance (ASD) of 0.44 mm on heart and 0.90 mm on bladder. Conclusions This methodology could be a valuable component to the FDG-PET/CT data processing chain by removing FDG physiological noise associated with heart and/or bladder accumulation prior to image analysis by manual, semi- or automated tumor analysis methods.
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Pantel AR, Viswanath V, Muzi M, Doot RK, Mankoff DA. Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology. J Nucl Med 2022; 63:342-352. [PMID: 35232879 DOI: 10.2967/jnumed.121.263518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 01/12/2022] [Indexed: 12/12/2022] Open
Abstract
Learning Objectives: On successful completion of this activity, participants should be able to describe (1) describe principles of PET tracer kinetic analysis for oncologic applications; (2) list methods used for PET kinetic analysis for oncology; and (3) discuss application of kinetic modeling for cancer-specific diagnostic needs.Financial Disclosure: This work was supported by KL2 TR001879, R01 CA211337, R01 CA113941, R33 CA225310, Komen SAC130060, R50 CA211270, and K01 DA040023. Dr. Pantel is a consultant or advisor for Progenics and Blue Earth Diagnostics and is a meeting participant or lecturer for Blue Earth Diagnostics. Dr. Mankoff is on the scientific advisory boards of GE Healthcare, Philips Healthcare, Reflexion, and ImaginAb and is the owner of Trevarx; his wife is the chief executive officer of Trevarx. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest.CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through March 2025PET enables noninvasive imaging of regional in vivo cancer biology. By engineering a radiotracer to target specific biologic processes of relevance to cancer (e.g., cancer metabolism, blood flow, proliferation, and tumor receptor expression or ligand binding), PET can detect cancer spread, characterize the cancer phenotype, and assess its response to treatment. For example, imaging of glucose metabolism using the radiolabeled glucose analog 18F-FDG has widespread applications to all 3 of these tasks and plays an important role in cancer care. However, the current clinical practice of imaging at a single time point remote from tracer injection (i.e., static imaging) does not use all the information that PET cancer imaging can provide, especially to address questions beyond cancer detection. Reliance on tracer measures obtained only from static imaging may also lead to misleading results. In this 2-part continuing education paper, we describe the principles of tracer kinetic analysis for oncologic PET (part 1), followed by examples of specific implementations of kinetic analysis for cancer PET imaging that highlight the added benefits over static imaging (part 2). This review is designed to introduce nuclear medicine clinicians to basic concepts of kinetic analysis in oncologic imaging, with a goal of illustrating how kinetic analysis can augment our understanding of in vivo cancer biology, improve our approach to clinical decision making, and guide the interpretation of quantitative measures derived from static images.
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Affiliation(s)
- Austin R Pantel
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Varsha Viswanath
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, Washington
| | - Robert K Doot
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - David A Mankoff
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
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Lee J, Ha S, Kim REY, Lee M, Kim D, Lim HK. Development of Amyloid PET Analysis Pipeline Using Deep Learning-Based Brain MRI Segmentation—A Comparative Validation Study. Diagnostics (Basel) 2022; 12:diagnostics12030623. [PMID: 35328176 PMCID: PMC8947654 DOI: 10.3390/diagnostics12030623] [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: 02/09/2022] [Revised: 02/25/2022] [Accepted: 02/27/2022] [Indexed: 02/04/2023] Open
Abstract
Amyloid positron emission tomography (PET) scan is clinically essential for the non-invasive assessment of the presence and spatial distribution of amyloid-beta deposition in subjects with cognitive impairment suspected to have been a result of Alzheimer’s disease. Quantitative assessment can enhance the interpretation reliability of PET scan; however, its clinical application has been limited due to the complexity of preprocessing. This study introduces a novel deep-learning-based approach for SUVR quantification that simplifies the preprocessing step and significantly reduces the analysis time. Using two heterogeneous amyloid ligands, our proposed method successfully distinguished standardized uptake value ratio (SUVR) between amyloidosis-positive and negative groups. The proposed method’s intra-class correlation coefficients were 0.97 and 0.99 against PETSurfer and PMOD, respectively. The difference of global SUVRs between the proposed method and PETSurfer or PMOD were 0.04 and −0.02, which are clinically acceptable. The AUC-ROC exceeded 0.95 for three tools in the amyloid positive assessment. Moreover, the proposed method had the fastest processing time and had a low registration failure rate (1%). In conclusion, our proposed method calculates SUVR that is consistent with PETSurfer and PMOD, and has advantages of fast processing time and low registration failure rate. Therefore, PET quantification provided by our proposed method can be used in clinical practice.
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Affiliation(s)
- Jiyeon Lee
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
| | - Regina E. Y. Kim
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
| | - Minho Lee
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
- Correspondence: (D.K.); (H.K.L.); Tel.: +82-10-9361-3781 (D.K.); +82-10-3797-6315 (H.K.L.)
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Korea
- Correspondence: (D.K.); (H.K.L.); Tel.: +82-10-9361-3781 (D.K.); +82-10-3797-6315 (H.K.L.)
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