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Hajianfar G, Sabouri M, Salimi Y, Amini M, Bagheri S, Jenabi E, Hekmat S, Maghsudi M, Mansouri Z, Khateri M, Hosein Jamshidi M, Jafari E, Bitarafan Rajabi A, Assadi M, Oveisi M, Shiri I, Zaidi H. Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance. Z Med Phys 2024; 34:242-257. [PMID: 36932023 PMCID: PMC11156776 DOI: 10.1016/j.zemedi.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/22/2022] [Accepted: 01/18/2023] [Indexed: 03/17/2023]
Abstract
PURPOSE Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. MATERIALS AND METHODS After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. RESULTS DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. CONCLUSION Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Maziar Sabouri
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Soroush Bagheri
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sepideh Hekmat
- Hasheminejad Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Hosein Jamshidi
- Department of Medical Imaging and Radiation Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Esmail Jafari
- The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Ahmad Bitarafan Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Assadi
- The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Dyer MR, Jing Z, Duncan K, Godbe J, Shokeen M. Advancements in the development of radiopharmaceuticals for nuclear medicine applications in the treatment of bone metastases. Nucl Med Biol 2024; 130-131:108879. [PMID: 38340369 DOI: 10.1016/j.nucmedbio.2024.108879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
Bone metastases are a painful and complex condition that overwhelmingly impacts the prognosis and quality of life of cancer patients. Over the years, nuclear medicine has made remarkable progress in the diagnosis and management of bone metastases. This review aims to provide a comprehensive overview of the recent advancements in nuclear medicine for the diagnosis and management of bone metastases. Furthermore, the review explores the role of targeted radiopharmaceuticals in nuclear medicine for bone metastases, focusing on radiolabeled molecules that are designed to selectively target biomarkers associated with bone metastases, including osteocytes, osteoblasts, and metastatic cells. The applications of radionuclide-based therapies, such as strontium-89 (Sr-89) and radium-223 (Ra-223), are also discussed. This review also highlights the potential of theranostic approaches for bone metastases, enabling personalized treatment strategies based on individual patient characteristics. Importantly, the clinical applications and outcomes of nuclear medicine in osseous metastatic disease are discussed. This includes the assessment of treatment response, predictive and prognostic value of imaging biomarkers, and the impact of nuclear medicine on patient management and outcomes. The review identifies current challenges and future perspectives on the role of nuclear medicine in treating bone metastases. It addresses limitations in imaging resolution, radiotracer availability, radiation safety, and the need for standardized protocols. The review concludes by emphasizing the need for further research and advancements in imaging technology, radiopharmaceutical development, and integration of nuclear medicine with other treatment modalities. In summary, advancements in nuclear medicine have significantly improved the diagnosis and management of osseous metastatic disease and future developements in the integration of innovative imaging modalities, targeted radiopharmaceuticals, radionuclide production, theranostic approaches, and advanced image analysis techniques hold great promise in improving patient outcomes and enhancing personalized care for individuals with bone metastases.
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Affiliation(s)
- Michael R Dyer
- Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Zhenghan Jing
- Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kathleen Duncan
- Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline Godbe
- Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Monica Shokeen
- Edward Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Alvin J. Siteman Cancer Center, Washington University School of Medicine and Barnes-Jewish Hospital, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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Mohseninia N, Zamani-Siahkali N, Harsini S, Divband G, Pirich C, Beheshti M. Bone Metastasis in Prostate Cancer: Bone Scan Versus PET Imaging. Semin Nucl Med 2024; 54:97-118. [PMID: 37596138 DOI: 10.1053/j.semnuclmed.2023.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 08/20/2023]
Abstract
Prostate cancer is the second most common cause of malignancy among men, with bone metastasis being a significant source of morbidity and mortality in advanced cases. Detecting and treating bone metastasis at an early stage is crucial to improve the quality of life and survival of prostate cancer patients. This objective strongly relies on imaging studies. While CT and MRI have their specific utilities, they also possess certain drawbacks. Bone scintigraphy, although cost-effective and widely available, presents high false-positive rates. The emergence of PET/CT and PET/MRI, with their ability to overcome the limitations of standard imaging methods, offers promising alternatives for the detection of bone metastasis. Various radiotracers targeting cell division activity or cancer-specific membrane proteins, as well as bone seeking agents, have been developed and tested. The use of positron-emitting isotopes such as fluorine-18 and gallium-68 for labeling allows for a reduced radiation dose and unaffected biological properties. Furthermore, the integration of artificial intelligence (AI) and radiomics techniques in medical imaging has shown significant advancements in reducing interobserver variability, improving accuracy, and saving time. This article provides an overview of the advantages and limitations of bone scan using SPECT and SPECT/CT and PET imaging methods with different radiopharmaceuticals and highlights recent developments in hybrid scanners, AI, and radiomics for the identification of prostate cancer bone metastasis using molecular imaging.
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Affiliation(s)
- Nasibeh Mohseninia
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Nazanin Zamani-Siahkali
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Department of Nuclear Medicine, Research center for Nuclear Medicine and Molecular Imaging, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sara Harsini
- Department of Molecular Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | | | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Ceranka J, Wuts J, Chiabai O, Lecouvet F, Vandemeulebroucke J. Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107811. [PMID: 37742486 DOI: 10.1016/j.cmpb.2023.107811] [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: 01/24/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023]
Abstract
The confident detection of metastatic bone disease is essential to improve patients' comfort and increase life expectancy. Multi-parametric magnetic resonance imaging (MRI) has been successfully used for monitoring of metastatic bone disease, allowing for comprehensive and holistic evaluation of the total tumour volume and treatment response assessment. The major challenges of radiological reading of whole-body MRI come from the amount of data to be reviewed and the scattered distribution of metastases, often of complex shapes. This makes bone lesion detection and quantification demanding for a radiologist and prone to error. Additionally, whole-body MRI are often corrupted with multiple spatial and intensity distortions, which further degrade the performance of image reading and image processing algorithms. In this work we propose a fully automated computer-aided diagnosis system for the detection and segmentation of metastatic bone disease using whole-body multi-parametric MRI. The system consists of an extensive image preprocessing pipeline aiming at enhancing the image quality, followed by a deep learning framework for detection and segmentation of metastatic bone disease. The system outperformed state-of-the-art methodologies, achieving a detection sensitivity of 63% with a mean of 6.44 false positives per image, and an average lesion Dice coefficient of 0.53. A provided ablation study performed to investigate the relative importance of image preprocessing shows that introduction of region of interest mask and spatial registration have a significant impact on detection and segmentation performance in whole-body MRI. The proposed computer-aided diagnosis system allows for automatic quantification of disease infiltration and could provide a valuable tool during radiological examination of whole-body MRI.
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Affiliation(s)
- Jakub Ceranka
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, Brussels, 1050, Belgium; imec, Kapeldreef 75, Leuven, B-3001, Belgium.
| | - Joris Wuts
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, Brussels, 1050, Belgium; imec, Kapeldreef 75, Leuven, B-3001, Belgium; Cliniques universitaires Saint Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Avenue Hippocrate 10, Brussels, 1200, Belgium.
| | - Ophélye Chiabai
- Cliniques universitaires Saint Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Avenue Hippocrate 10, Brussels, 1200, Belgium
| | - Frédéric Lecouvet
- Cliniques universitaires Saint Luc, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Avenue Hippocrate 10, Brussels, 1200, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, Brussels, 1050, Belgium; imec, Kapeldreef 75, Leuven, B-3001, Belgium; Universitair Ziekenhuis Brussel, Department of Radiology, Laarbeeklaan 101, Brussels, 1090, Belgium
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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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Liao CW, Hsieh TC, Lai YC, Hsu YJ, Hsu ZK, Chan PK, Kao CH. Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases. Diagnostics (Basel) 2023; 13:diagnostics13040685. [PMID: 36832173 PMCID: PMC9955026 DOI: 10.3390/diagnostics13040685] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND When cancer has metastasized to bone, doctors must identify the site of the metastases for treatment. In radiation therapy, damage to healthy areas or missing areas requiring treatment should be avoided. Therefore, it is necessary to locate the precise bone metastasis area. The bone scan is a commonly applied diagnostic tool for this purpose. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation. The study evaluated object detection techniques to improve the efficacy of bone metastases detection on bone scans. METHODS We retrospectively examined the data of 920 patients, aged 23 to 95 years, who underwent bone scans between May 2009 and December 2019. The bone scan images were examined using an object detection algorithm. RESULTS After reviewing the image reports written by physicians, nursing staff members annotated the bone metastasis sites as ground truths for training. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. The optimal dice similarity coefficient (DSC) in our study was 0.6640, which differs by 0.04 relative to the optimal DSC of different physicians (0.7040). CONCLUSIONS Object detection can help physicians to efficiently notice bone metastases, decrease physician workload, and improve patient care.
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Affiliation(s)
- Chiung-Wei Liao
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
| | - Te-Chun Hsieh
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404327, Taiwan
| | - Yung-Chi Lai
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Nuclear Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung 420550, Taiwan
| | - Yu-Ju Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan
| | - Zong-Kai Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan
| | - Pak-Ki Chan
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan
| | - Chia-Hung Kao
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404327, Taiwan
- Artificial Intelligence Center, China Medical University Hospital, Taichung 404327, Taiwan
- Graduate Institute of Biomedical Sciences, School of Medicine, College of Medicine, China Medical University, Taichung 404327, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan
- Correspondence: or ; Tel.: +886-4-22052121
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Kepenek F, Kaplan İ, Can C, Karaoğlan H, Güzel Y, Kömek H. Comparison of 68GA-FAPI-04 PET/CT and 18F-FDG PET/CT in detection of metastatic bone disease in various cancers. MÉDECINE NUCLÉAIRE 2023. [DOI: 10.1016/j.mednuc.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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Kao YS, Huang CP, Tsai WW, Yang J. A systematic review for using deep learning in bone scan classification. Clin Transl Imaging 2023. [DOI: 10.1007/s40336-023-00539-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Semin Radiat Oncol 2023; 33:70-75. [PMID: 36517196 DOI: 10.1016/j.semradonc.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.
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Lacroix M, Aouad T, Feydy J, Biau D, Larousserie F, Fournier L, Feydy A. Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:18-23. [PMID: 36270953 DOI: 10.1016/j.diii.2022.10.004] [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: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.
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Affiliation(s)
- Maxime Lacroix
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Theodore Aouad
- Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France
| | - Jean Feydy
- Université Paris Cité, HeKA team, Inria Paris, Inserm, 75006, Paris, France
| | - David Biau
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Orthopedic Surgery, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Frédérique Larousserie
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Pathology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Laure Fournier
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
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Nasef MM, Eid FT, Amin M, Sauber AM. An efficient segmentation technique for skeletal scintigraphy image based on sharpness index and salp swarm algorithm. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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