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计算建模与人工智能在放射神经肿瘤学和放射外科中的应用。

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

机构信息

Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.

Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.

出版信息

Adv Exp Med Biol. 2024;1462:307-322. doi: 10.1007/978-3-031-64892-2_18.


DOI:10.1007/978-3-031-64892-2_18
PMID:39523273
Abstract

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed from various aspects of radiosurgery, including the successful detection of spontaneous tumors and the automated delineation or segmentation of lesions. These applications show potential for extension to longitudinal treatment follow-up. Additionally, the chapter highlights the established use of machine learning algorithms, particularly those incorporating radiomic-based analysis, in predicting treatment outcomes. The discussion encompasses current achievements, existing limitations, and the need for further investigation in the dynamic intersection of AI and radiosurgery.

摘要

本章探讨了人工智能(AI)在医疗保健系统中的广泛融合,特别关注其在立体定向放射外科中的应用。人工智能技术的快速发展带来了该领域的有前景的进展,特别是通过利用机器学习和深度学习模型。从放射外科的各个方面开发了各种 AI 算法的实施,包括成功检测自发性肿瘤和自动描绘或分割病变。这些应用显示出在纵向治疗随访中扩展的潜力。此外,本章强调了机器学习算法的既定用途,特别是那些纳入基于放射组学分析的算法,用于预测治疗结果。讨论包括当前的成就、现有局限性以及在 AI 和放射外科的动态交叉点进一步研究的必要性。

相似文献

[1]
Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

Adv Exp Med Biol. 2024

[2]
Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.

J Neurooncol. 2023-2

[3]
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.

Strahlenther Onkol. 2020-5-11

[4]
Radiomics in radiation oncology-basics, methods, and limitations.

Strahlenther Onkol. 2020-7-9

[5]
Artificial Intelligence in Brain Tumors.

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[6]
Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.

Radiat Oncol. 2023-4-4

[7]
Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

Sci Rep. 2024-5-15

[8]
Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks.

Neuro Oncol. 2021-9-1

[9]
Machine Learning-Based Radiomics in Neuro-Oncology.

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[10]
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本文引用的文献

[1]
Assessment of gamma knife radiosurgery for unruptured cerebral arterioveneus malformations based on multi-parameter radiomics of MRI.

Magn Reson Imaging. 2022-10

[2]
A Machine Learning Model Predicts the Outcome of SRS for Residual Arteriovenous Malformations after Partial Embolization: A Real-World Clinical Obstacle.

World Neurosurg. 2022-7

[3]
Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study.

Neuro Oncol. 2022-9-1

[4]
Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.

Sci Rep. 2022-1-17

[5]
Factors Affecting Volume Reduction Velocity for Arteriovenous Malformations After Treatment With Dose-Stage Stereotactic Radiosurgery.

Front Oncol. 2021-12-20

[6]
Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.

Neurooncol Adv. 2021-10-25

[7]
Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases.

Cancers (Basel). 2021-8-10

[8]
Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps.

Med Phys. 2021-9

[9]
Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases.

Radiol Artif Intell. 2021-3-10

[10]
A radiomics-based model for predicting local control of resected brain metastases receiving adjuvant SRS.

Clin Transl Radiat Oncol. 2021-5-8

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