Machine learning helps identifying volume-confounding effects in radiomics

Alberto Traverso; Michal Kazmierski; Ivan Zhovannik; Mattea Welch; Leonard Wee; David Jaffray; Andre Dekker; Andrew Hope

doi:10.1016/j.ejmp.2020.02.010

Machine learning helps identifying volume-confounding effects in radiomics

Alberto Traverso, Michal Kazmierski, Ivan Zhovannik, Mattea Welch, Leonard Wee, David Jaffray, Andre Dekker, Andrew Hope

Research output: Contribution to journal › Article › Academic › peer-review

Original language	English
Pages (from-to)	24-30
Number of pages	7
Journal	Physica Medica: European journal of medical physics
Volume	71
DOIs	https://doi.org/10.1016/j.ejmp.2020.02.010
Publication status	Published - Mar 2020
Event	International Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA) - Montreal, Canada Duration: 17 Jun 2019 → 21 Jun 2019

Keywords

Radiomics
Machine learning
Predictions
Lung
Head and neck
TUMOR VOLUME
HETEROGENEITY

Access to Document

10.1016/j.ejmp.2020.02.010

Cite this

Traverso, A., Kazmierski, M., Zhovannik, I., Welch, M., Wee, L., Jaffray, D., Dekker, A., & Hope, A. (2020). Machine learning helps identifying volume-confounding effects in radiomics. Physica Medica: European journal of medical physics, 71, 24-30. https://doi.org/10.1016/j.ejmp.2020.02.010

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title = "Machine learning helps identifying volume-confounding effects in radiomics",

keywords = "Radiomics, Machine learning, Predictions, Lung, Head and neck, TUMOR VOLUME, HETEROGENEITY",

author = "Alberto Traverso and Michal Kazmierski and Ivan Zhovannik and Mattea Welch and Leonard Wee and David Jaffray and Andre Dekker and Andrew Hope",

year = "2020",

month = mar,

doi = "10.1016/j.ejmp.2020.02.010",

language = "English",

volume = "71",

pages = "24--30",

journal = "Physica Medica: European journal of medical physics",

issn = "1120-1797",

publisher = "ELSEVIER SCI LTD",

note = "International Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA) ; Conference date: 17-06-2019 Through 21-06-2019",

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Machine learning helps identifying volume-confounding effects in radiomics. / Traverso, Alberto; Kazmierski, Michal; Zhovannik, Ivan; Welch, Mattea; Wee, Leonard; Jaffray, David; Dekker, Andre; Hope, Andrew.

In: Physica Medica: European journal of medical physics, Vol. 71, 03.2020, p. 24-30.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Machine learning helps identifying volume-confounding effects in radiomics

AU - Traverso, Alberto

AU - Kazmierski, Michal

AU - Zhovannik, Ivan

AU - Welch, Mattea

AU - Wee, Leonard

AU - Jaffray, David

AU - Dekker, Andre

AU - Hope, Andrew

PY - 2020/3

Y1 - 2020/3

KW - Radiomics

KW - Machine learning

KW - Predictions

KW - Lung

KW - Head and neck

KW - TUMOR VOLUME

KW - HETEROGENEITY

U2 - 10.1016/j.ejmp.2020.02.010

DO - 10.1016/j.ejmp.2020.02.010

M3 - Article

VL - 71

SP - 24

EP - 30

JO - Physica Medica: European journal of medical physics

JF - Physica Medica: European journal of medical physics

SN - 1120-1797

T2 - International Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA)

Y2 - 17 June 2019 through 21 June 2019

ER -