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人工智能方法在脊柱转移瘤成像中的应用。

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

作者信息

Ong Wilson, Zhu Lei, Zhang Wenqiao, Kuah Tricia, Lim Desmond Shi Wei, Low Xi Zhen, Thian Yee Liang, Teo Ee Chin, Tan Jiong Hao, Kumar Naresh, Vellayappan Balamurugan A, Ooi Beng Chin, Quek Swee Tian, Makmur Andrew, Hallinan James Thomas Patrick Decourcy

机构信息

Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore.

Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore.

出版信息

Cancers (Basel). 2022 Aug 20;14(16):4025. doi: 10.3390/cancers14164025.


DOI:10.3390/cancers14164025
PMID:36011018
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9406500/
Abstract

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

摘要

脊柱转移瘤是脊柱最常见的恶性疾病。近年来,机器学习和人工智能技术取得了重大进展,导致它们在肿瘤影像学中的应用日益增加。本研究的目的是回顾和总结目前人工智能在脊柱转移瘤的检测、分类和管理中的应用证据,以及它们潜在地融入临床实践的情况。按照PRISMA指南对主要电子医学数据库进行了系统、详细的检索。从数据库中检索并审查了共30篇文章。汇总并总结了当前人工智能应用的主要发现。人工智能技术的主要临床应用包括图像处理、诊断、决策支持、治疗辅助和预后结果。在脊柱肿瘤学领域,人工智能技术已经取得了相对较好的表现,并具有巨大的潜力来帮助临床医生,包括提高工作效率和减少不良事件。需要进一步的研究来验证人工智能工具的临床性能,并促进它们融入常规临床实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/6a51b945a207/cancers-14-04025-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/43393978dbbf/cancers-14-04025-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/48678a886012/cancers-14-04025-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/e836e4a61687/cancers-14-04025-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/e3b933bfcc84/cancers-14-04025-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/6a51b945a207/cancers-14-04025-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/43393978dbbf/cancers-14-04025-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/48678a886012/cancers-14-04025-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/e836e4a61687/cancers-14-04025-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/e3b933bfcc84/cancers-14-04025-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c1/9406500/6a51b945a207/cancers-14-04025-g005.jpg

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[1]
Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Cancers (Basel). 2022-8-20

[2]
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[6]
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[7]
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引用本文的文献

[1]
Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.

J Clin Med. 2025-8-20

[2]
Artificial Intelligence for Non-Invasive Prediction of Molecular Signatures in Spinal Metastases: A Systematic Review.

Bioengineering (Basel). 2025-7-23

[3]
Current Advancements in the Diagnosis and Treatment of Metastatic Spinal Cord Compression and Its Postintervention Care: A Comprehensive Review.

Cureus. 2025-6-15

[4]
Exploring the Potential of a Deep Learning Model for Early CT Detection of High-Grade Metastatic Epidural Spinal Cord Compression and Its Impact on Treatment Delays.

Cancers (Basel). 2025-6-28

[5]
Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.

BMC Cancer. 2025-4-15

[6]
Deep learning models for MRI-based clinical decision support in cervical spine degenerative diseases.

Front Neurosci. 2024-12-6

[7]
AI-Assisted Detection and Localization of Spinal Metastatic Lesions.

Diagnostics (Basel). 2024-11-3

[8]
Advances in Imaging for Metastatic Epidural Spinal Cord Compression: A Comprehensive Review of Detection, Diagnosis, and Treatment Planning.

Cureus. 2024-9-24

[9]
Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.

Cancers (Basel). 2024-8-28

[10]
Development of a natural language processing algorithm for the detection of spinal metastasis based on magnetic resonance imaging reports.

N Am Spine Soc J. 2024-7-3

本文引用的文献

[1]
Artificial intelligence for renal cancer: From imaging to histology and beyond.

Asian J Urol. 2022-7

[2]
Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Cancers (Basel). 2022-6-9

[3]
Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.

Sci Rep. 2022-6-14

[4]
Radiomics analysis based on multiple parameters MR imaging in the spine: Predicting treatment response of osteolytic bone metastases to chemotherapy in breast cancer patients.

Magn Reson Imaging. 2022-10

[5]
Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI.

Front Oncol. 2022-5-4

[6]
Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.

Medicina (Kaunas). 2022-3-22

[7]
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction.

JHEP Rep. 2022-2-2

[8]
Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases.

Phys Imaging Radiat Oncol. 2022-2-17

[9]
Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review.

Int J Environ Res Public Health. 2022-2-8

[10]
Single-Photon Emission Computed Tomography/Computed Tomography Image-Based Radiomics for Discriminating Vertebral Bone Metastases From Benign Bone Lesions in Patients With Tumors.

Front Med (Lausanne). 2022-1-4

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