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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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Zamani-Siahkali N, Mirshahvalad SA, Farbod A, Divband G, Pirich C, Veit-Haibach P, Cook G, Beheshti M. SPECT/CT, PET/CT, and PET/MRI for Response Assessment of Bone Metastases. Semin Nucl Med 2024; 54:356-370. [PMID: 38172001 DOI: 10.1053/j.semnuclmed.2023.11.005] [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: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
Recent developments in hybrid SPECT/CT systems and the use of cadmium-zinc-telluride (CZT) detectors have improved the diagnostic accuracy of bone scintigraphy. These advancements have paved the way for novel quantitative approaches to accurate and reproducible treatment monitoring of bone metastases. PET/CT imaging using [18F]F-FDG and [18F]F-NaF have shown promising clinical utility in bone metastases assessment and monitoring response to therapy and prediction of treatment response in a broad range of malignancies. Additionally, specific tumor-targeting tracers like [99mTc]Tc-PSMA, [68Ga]Ga-PSMA, or [11C]C- or [18F]F-Choline revealed high diagnostic performance for early assessment and prognostication of bone metastases, particularly in prostate cancer. PET/MRI appears highly accurate imaging modality, but has associated limitations notably, limited availability, more complex logistics and high installation costs. Advances in artificial intelligence (Al) seem to improve the accuracy of imaging modalities and provide an assistant role in the evaluation of treatment response of bone metastases.
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Affiliation(s)
- Nazanin Zamani-Siahkali
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Abolfazl Farbod
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Canada
| | - Gary Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Lawal IO, Ndlovu H, Kgatle M, Mokoala KMG, Sathekge MM. Prognostic Value of PSMA PET/CT in Prostate Cancer. Semin Nucl Med 2024; 54:46-59. [PMID: 37482489 DOI: 10.1053/j.semnuclmed.2023.07.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
Prostate-specific membrane antigen (PSMA) is a transmembrane glycoprotein expressed in the majority of prostate cancer (PCa). PSMA has an enzymatic function that makes metabolic substrates such as folate available for utilization by PCa cells. Intracellular folate availability drives aggressive tumor phenotype. PSMA expression is, therefore, a marker of aggressive tumor biology. The large extracellular domain of PSMA is available for targeting by diagnostic and therapeutic radionuclides, making it a suitable cellular epitope for theranostics. PET imaging of radiolabeled PSMA ligands has several prognostic utilities. In the prebiopsy setting, intense PSMA avidity in a prostate lesion correlate well with clinically significant PCa (csPCa) on histology. When used for staging, PSMA PET imaging outperforms conventional imaging for the accurate staging of primary PCa, and findings on imaging predict post-treatment outcomes. The biggest contribution of PSMA PET imaging to PCa management is in the biochemical recurrence setting, where it has emerged as the most sensitive imaging modality for the localization of PCa recurrence by helping to guide salvage therapy. PSMA PET obtained for localizing the site of recurrence is prognostic, such that a higher lesion number predicts a less favorable outcome to salvage radiotherapy or surgical intervention. Systemic therapy is given to patients with advanced PCa with distant metastasis. PSMA PET is useful for predicting response to treatments with chemotherapy, first- and second-line androgen deprivation therapies, and PSMA-targeted radioligand therapy. Artificial intelligence using machine learning algorithms allows for the mining of information from clinical images not visible to the human eyes. Artificial intelligence applied to PSMA PET images, therefore, holds great promise for prognostication in PCa management.
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Affiliation(s)
- Ismaheel O Lawal
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa
| | - Honest Ndlovu
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
| | - Mankgopo Kgatle
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
| | - Kgomotso M G Mokoala
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
| | - Mike M Sathekge
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa.
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