User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Mattea L. Welch; Chris McIntosh; Andrea McNiven; Shao Hui Huang; Bei-Bei Zhang; Leonard Wee; Alberto Traverso; Brian O'Sullivan; Frank Hoebers; Andre Dekker; David A. Jaffray

doi:10.1016/j.ejmp.2020.01.027

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Mattea L. Welch^*, Chris McIntosh, Andrea McNiven, Shao Hui Huang, Bei-Bei Zhang, Leonard Wee, Alberto Traverso, Brian O'Sullivan, Frank Hoebers, Andre Dekker, David A. Jaffray

^*Corresponding author for this work

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

Original language	English
Pages (from-to)	145-152
Number of pages	8
Journal	Physica Medica: European journal of medical physics
Volume	70
DOIs	https://doi.org/10.1016/j.ejmp.2020.01.027
Publication status	Published - Feb 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

Head and neck
Outcome prediction
Deep learning
Machine learning
User-controlled
Bias
PROGNOSTIC-FACTORS
RADIOTHERAPY
CANCER
RADIOMICS
CARCINOMA
SURVIVAL
IMPACT
RISK

Access to Document

10.1016/j.ejmp.2020.01.027

Cite this

Welch, M. L., McIntosh, C., McNiven, A., Huang, S. H., Zhang, B-B., Wee, L., Traverso, A., O'Sullivan, B., Hoebers, F., Dekker, A., & Jaffray, D. A. (2020). User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions. Physica Medica: European journal of medical physics, 70, 145-152. https://doi.org/10.1016/j.ejmp.2020.01.027

Welch, Mattea L. ; McIntosh, Chris ; McNiven, Andrea ; Huang, Shao Hui ; Zhang, Bei-Bei ; Wee, Leonard ; Traverso, Alberto ; O'Sullivan, Brian ; Hoebers, Frank ; Dekker, Andre ; Jaffray, David A. / User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions. In: Physica Medica: European journal of medical physics. 2020 ; Vol. 70. pp. 145-152.

@article{0ae1aba01a194ada90bf1bf9716481b9,

title = "User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions",

keywords = "Head and neck, Outcome prediction, Deep learning, Machine learning, User-controlled, Bias, PROGNOSTIC-FACTORS, RADIOTHERAPY, CANCER, RADIOMICS, CARCINOMA, SURVIVAL, IMPACT, RISK",

author = "Welch, {Mattea L.} and Chris McIntosh and Andrea McNiven and Huang, {Shao Hui} and Bei-Bei Zhang and Leonard Wee and Alberto Traverso and Brian O'Sullivan and Frank Hoebers and Andre Dekker and Jaffray, {David A.}",

year = "2020",

month = feb,

doi = "10.1016/j.ejmp.2020.01.027",

language = "English",

volume = "70",

pages = "145--152",

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",

}

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions. / Welch, Mattea L.; McIntosh, Chris; McNiven, Andrea; Huang, Shao Hui; Zhang, Bei-Bei; Wee, Leonard ; Traverso, Alberto; O'Sullivan, Brian; Hoebers, Frank ; Dekker, Andre; Jaffray, David A.

In: Physica Medica: European journal of medical physics, Vol. 70, 02.2020, p. 145-152.

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

TY - JOUR

T1 - User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

AU - Welch, Mattea L.

AU - McIntosh, Chris

AU - McNiven, Andrea

AU - Huang, Shao Hui

AU - Zhang, Bei-Bei

AU - Wee, Leonard

AU - Traverso, Alberto

AU - O'Sullivan, Brian

AU - Hoebers, Frank

AU - Dekker, Andre

AU - Jaffray, David A.

PY - 2020/2

Y1 - 2020/2

KW - Head and neck

KW - Outcome prediction

KW - Deep learning

KW - Machine learning

KW - User-controlled

KW - Bias

KW - PROGNOSTIC-FACTORS

KW - RADIOTHERAPY

KW - CANCER

KW - RADIOMICS

KW - CARCINOMA

KW - SURVIVAL

KW - IMPACT

KW - RISK

U2 - 10.1016/j.ejmp.2020.01.027

DO - 10.1016/j.ejmp.2020.01.027

M3 - Article

VL - 70

SP - 145

EP - 152

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 -