Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

I. El Naqa; P. W. Grigsby; A. Apte; E. Kidd; E. Donnelly; D. Khullar; S. Chaudhari; D. Yang; M. Schmitt; Richard Laforest; W. L. Thorstad; J. O. Deasy

doi:10.1016/j.patcog.2008.08.011

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

I. El Naqa^*, P. W. Grigsby, A. Apte, E. Kidd, E. Donnelly, D. Khullar, S. Chaudhari, D. Yang, M. Schmitt, Richard Laforest, W. L. Thorstad, J. O. Deasy

^*Corresponding author for this work

Radiation Oncology

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

421 Scopus citations

Abstract

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

Original language	English (US)
Pages (from-to)	1162-1171
Number of pages	10
Journal	Pattern Recognition
Volume	42
Issue number	6
DOIs	https://doi.org/10.1016/j.patcog.2008.08.011
State	Published - Jun 2009

Keywords

Co-occurrence matrix
Image morphology
Intensity-volume histograms
Positron emission tomography
Treatment outcomes
Tumor heterogeneity
Tumor shape
Uptake values

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.patcog.2008.08.011

Cite this

@article{021c414fde5c44088db580b3583d599a,

title = "Exploring feature-based approaches in PET images for predicting cancer treatment outcomes",

abstract = "Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.",

keywords = "Co-occurrence matrix, Image morphology, Intensity-volume histograms, Positron emission tomography, Treatment outcomes, Tumor heterogeneity, Tumor shape, Uptake values",

author = "{El Naqa}, I. and Grigsby, {P. W.} and A. Apte and E. Kidd and E. Donnelly and D. Khullar and S. Chaudhari and D. Yang and M. Schmitt and Richard Laforest and Thorstad, {W. L.} and Deasy, {J. O.}",

note = "Funding Information: This work was supported in part by NIH Grant R01 CA 85181 and American Cancer Society IRG-58-010-50. ",

year = "2009",

month = jun,

doi = "10.1016/j.patcog.2008.08.011",

language = "English (US)",

volume = "42",

pages = "1162--1171",

journal = "Pattern Recognition",

issn = "0031-3203",

publisher = "Elsevier Ltd",

number = "6",

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T1 - Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

AU - El Naqa, I.

AU - Grigsby, P. W.

AU - Apte, A.

AU - Kidd, E.

AU - Donnelly, E.

AU - Khullar, D.

AU - Chaudhari, S.

AU - Yang, D.

AU - Schmitt, M.

AU - Laforest, Richard

AU - Thorstad, W. L.

AU - Deasy, J. O.

N1 - Funding Information: This work was supported in part by NIH Grant R01 CA 85181 and American Cancer Society IRG-58-010-50.

PY - 2009/6

Y1 - 2009/6

N2 - Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

AB - Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

KW - Co-occurrence matrix

KW - Image morphology

KW - Intensity-volume histograms

KW - Positron emission tomography

KW - Treatment outcomes

KW - Tumor heterogeneity

KW - Tumor shape

KW - Uptake values

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DO - 10.1016/j.patcog.2008.08.011

M3 - Article

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SN - 0031-3203

VL - 42

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JO - Pattern Recognition

JF - Pattern Recognition

IS - 6

ER -

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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