Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Navodini Wijethilake [verfasserIn] Oscar MacCormac [verfasserIn] Tom Vercauteren [verfasserIn] Jonathan Shapey [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	extra-axial intracranial biomarker marker imaging growth

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 13(2023)
Übergeordnetes Werk:	volume:13 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2023.1131013

Katalog-ID:	DOAJ089450809

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authorswithroles_txt_mv	Navodini Wijethilake @@aut@@ Oscar MacCormac @@aut@@ Tom Vercauteren @@aut@@ Jonathan Shapey @@aut@@
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callnumber-first	R - Medicine
author	Navodini Wijethilake
spellingShingle	Navodini Wijethilake misc RC254-282 misc extra-axial misc intracranial misc biomarker misc marker misc imaging misc growth misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
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topic_title	RC254-282 Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review extra-axial intracranial biomarker marker imaging growth
topic	misc RC254-282 misc extra-axial misc intracranial misc biomarker misc marker misc imaging misc growth misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc extra-axial misc intracranial misc biomarker misc marker misc imaging misc growth misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
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title_full	Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
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title_sort	imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
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title_auth	Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
abstract	Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity.Systematic Review Registration: PROSPERO, CRD42022306922
abstractGer	Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity.Systematic Review Registration: PROSPERO, CRD42022306922
abstract_unstemmed	Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity.Systematic Review Registration: PROSPERO, CRD42022306922
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title_short	Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review
url	https://doi.org/10.3389/fonc.2023.1131013 https://doaj.org/article/664471dc278a480db3513bd43572e3ec https://www.frontiersin.org/articles/10.3389/fonc.2023.1131013/full https://doaj.org/toc/2234-943X
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up_date	2024-07-03T23:08:10.567Z
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Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

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