Foundations of multiparametric brain tumor imaging characterization using machine learning

Anne Jian; Kevin Jang; Carlo Russo; Sidong Liu; Antonio Di Ieva

doi:10.1007/978-3-030-85292-4_22

Foundations of multiparametric brain tumor imaging characterization using machine learning

Anne Jian, Kevin Jang, Carlo Russo, Sidong Liu, Antonio Di Ieva^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

13 Citations (Scopus)

Abstract

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

Original language	English
Title of host publication	Machine learning in clinical neuroscience
Subtitle of host publication	foundations and applications
Editors	Victor E. Staartjes, Luca Regli, Carlo Serra
Place of Publication	Switzerland
Publisher	Springer, Springer Nature
Chapter	22
Pages	183-193
Number of pages	11
ISBN (Electronic)	9783030852924
ISBN (Print)	9783030852917
DOIs	https://doi.org/10.1007/978-3-030-85292-4_22
Publication status	Published - 2022

Publication series

Name	Acta Neurochirurgica Supplement
Publisher	Springer
Volume	134
ISSN (Print)	0065-1419
ISSN (Electronic)	2197-8395

Keywords

Brain tumour
Machine learning
MRI
Multiparametric characterisation
Radiomics

Access to Document

10.1007/978-3-030-85292-4_22Licence: Unspecified

Cite this

Jian, A., Jang, K., Russo, C., Liu, S., & Di Ieva, A. (2022). Foundations of multiparametric brain tumor imaging characterization using machine learning. In V. E. Staartjes, L. Regli, & C. Serra (Eds.), Machine learning in clinical neuroscience: foundations and applications (pp. 183-193). (Acta Neurochirurgica Supplement; Vol. 134). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-85292-4_22

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title = "Foundations of multiparametric brain tumor imaging characterization using machine learning",

abstract = "The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.",

keywords = "Brain tumour, Machine learning, MRI, Multiparametric characterisation, Radiomics",

author = "Anne Jian and Kevin Jang and Carlo Russo and Sidong Liu and \{Di Ieva\}, Antonio",

year = "2022",

doi = "10.1007/978-3-030-85292-4\_22",

language = "English",

isbn = "9783030852917",

series = "Acta Neurochirurgica Supplement",

publisher = "Springer, Springer Nature",

pages = "183--193",

editor = "Staartjes, \{Victor E.\} and Luca Regli and Carlo Serra",

booktitle = "Machine learning in clinical neuroscience",

address = "United States",

}

Jian, A, Jang, K, Russo, C , Liu, S & Di Ieva, A 2022, Foundations of multiparametric brain tumor imaging characterization using machine learning. in VE Staartjes, L Regli & C Serra (eds), Machine learning in clinical neuroscience: foundations and applications. Acta Neurochirurgica Supplement, vol. 134, Springer, Springer Nature, Switzerland, pp. 183-193. https://doi.org/10.1007/978-3-030-85292-4_22

Foundations of multiparametric brain tumor imaging characterization using machine learning. / Jian, Anne; Jang, Kevin; Russo, Carlo et al.
Machine learning in clinical neuroscience: foundations and applications. ed. / Victor E. Staartjes; Luca Regli; Carlo Serra. Switzerland: Springer, Springer Nature, 2022. p. 183-193 (Acta Neurochirurgica Supplement; Vol. 134).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Foundations of multiparametric brain tumor imaging characterization using machine learning

AU - Jian, Anne

AU - Jang, Kevin

AU - Russo, Carlo

AU - Liu, Sidong

AU - Di Ieva, Antonio

PY - 2022

Y1 - 2022

N2 - The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

AB - The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

KW - Brain tumour

KW - Machine learning

KW - MRI

KW - Multiparametric characterisation

KW - Radiomics

UR - https://www.scopus.com/pages/publications/85120922650

U2 - 10.1007/978-3-030-85292-4_22

DO - 10.1007/978-3-030-85292-4_22

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C2 - 34862542

AN - SCOPUS:85120922650

SN - 9783030852917

T3 - Acta Neurochirurgica Supplement

SP - 183

EP - 193

BT - Machine learning in clinical neuroscience

A2 - Staartjes, Victor E.

A2 - Regli, Luca

A2 - Serra, Carlo

PB - Springer, Springer Nature

CY - Switzerland

ER -

Jian A, Jang K, Russo C , Liu S , Di Ieva A. Foundations of multiparametric brain tumor imaging characterization using machine learning. In Staartjes VE, Regli L, Serra C, editors, Machine learning in clinical neuroscience: foundations and applications. Switzerland: Springer, Springer Nature. 2022. p. 183-193. (Acta Neurochirurgica Supplement). Epub 2021 Dec 4. doi: 10.1007/978-3-030-85292-4_22

Foundations of multiparametric brain tumor imaging characterization using machine learning

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