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超越胶质瘤:放射组学分析在非胶质细胞性颅内肿瘤中的应用

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors.

作者信息

Kalasauskas Darius, Kosterhon Michael, Keric Naureen, Korczynski Oliver, Kronfeld Andrea, Ringel Florian, Othman Ahmed, Brockmann Marc A

机构信息

Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany.

Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany.

出版信息

Cancers (Basel). 2022 Feb 7;14(3):836. doi: 10.3390/cancers14030836.

DOI:10.3390/cancers14030836
PMID:35159103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8834271/
Abstract

The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

摘要

放射组学领域正在迅速扩展,并在神经肿瘤学中发挥着重要作用。与放射组学分析相关的可能性,例如区分恶性肿瘤类型、预测肿瘤分级、确定特定分子标志物的存在、一致性、治疗反应和预后等,在不久的将来会对医学决策产生重大影响。尽管放射组学分析的主要重点一直是胶质细胞性中枢神经系统肿瘤,但对其他颅内肿瘤的研究也取得了令人鼓舞的结果。因此,作为本综述的主要重点,我们对PubMed和Web of Science数据库中的出版物进行了分析,重点关注中枢神经系统转移瘤、淋巴瘤、脑膜瘤、髓母细胞瘤和垂体瘤的放射组学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82fa/8834271/2e1c7257c67c/cancers-14-00836-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82fa/8834271/4345d2f3af99/cancers-14-00836-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82fa/8834271/2e1c7257c67c/cancers-14-00836-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82fa/8834271/4345d2f3af99/cancers-14-00836-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82fa/8834271/2e1c7257c67c/cancers-14-00836-g002.jpg

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2
Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.利用基于心脏 MRI 衍生放射组学的机器学习模型进行放射组学辅助实验和 DAFIT 方法识别肺动脉高压。
Sci Rep. 2021 Jun 16;11(1):12686. doi: 10.1038/s41598-021-92155-6.
3
Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.
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Eur Radiol. 2024 Mar;34(3):2113-2120. doi: 10.1007/s00330-023-10202-4. Epub 2023 Sep 4.
4
Recent Emerging Immunological Treatments for Primary Brain Tumors: Focus on Chemokine-Targeting Immunotherapies.原发性脑肿瘤的新兴免疫治疗方法:聚焦趋化因子靶向免疫疗法。
Cells. 2023 Mar 8;12(6):841. doi: 10.3390/cells12060841.
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4
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5
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9
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Sci Rep. 2021 Mar 9;11(1):5506. doi: 10.1038/s41598-021-85168-8.