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Current Oncology
Volume 17
Issue 1
10.3747/co.v17i1.394
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Current Oncology is published by MDPI from Volume 28 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Multimed Inc..
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Article

Application of Machine Learning Methodology for Pet-Based Definition of Lung Cancer

by
A. Kerhet
1,*,
C. Small
2,
H. Quon
2,
T. Riauka
1,3,
L. Schrader
4,
R. Greiner
5,
D. Yee
2,
A. McEwan
1,4 and
W. Roa
2
1
Department of Oncology, University of Alberta, Edmonton, AB T6G 1Z2, Canada
2
Department of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, Canada
3
Department of Medical Physics, Cross Cancer Institute, Edmonton, AB, Canada
4
Department of Oncologic Imaging, Cross Cancer Institute, Edmonton, AB, Canada
5
Department of Computing Science, University of Alberta, and Alberta Ingenuity Centre for Machine Learning, Edmonton, AB, Canada
*
Author to whom correspondence should be addressed.
Curr. Oncol. 2010, 17(1), 41-47; https://doi.org/10.3747/co.v17i1.394
Submission received: 5 November 2009 / Revised: 3 December 2009 / Accepted: 7 January 2010 / Published: 1 February 2010

Abstract

We applied a learning methodology framework to assist in the threshold-based segmentation of non-small-cell lung cancer (NSCLC) tumours in positron-emission tomography–computed tomography (PET–CT) imaging for use in radiotherapy planning. Gated and standard free-breathing studies of two patients were independently analysed (four studies in total). Each study had a PET–CT and a treatment-planning CT image. The reference gross tumour volume (GTV) was identified by two experienced radiation oncologists who also determined reference standardized uptake value (SUV) thresholds that most closely approximated the GTV contour on each slice. A set of uptake distribution-related attributes was calculated for each PET slice. A machine learning algorithm was trained on a subset of the PET slices to cope with slice-to-slice variation in the optimal SUV threshold: that is, to predict the most appropriate SUV threshold from the calculated attributes for each slice. The algorithm’s performance was evaluated using the remainder of the PET slices. A high degree of geometric similarity was achieved between the areas outlined by the predicted and the reference SUV thresholds (Jaccard index exceeding 0.82). No significant difference was found between the gated and the free-breathing results in the same patient. In this preliminary work, we demonstrated the potential applicability of a machine learning methodology as an auxiliary tool for radiation treatment planning in NSCLC.
Keywords: positron-emission tomography; pet; radiation treatment; lung cancer; gross tumour volume; gtv; artificial intelligence; machine learning; support vector machine; svm positron-emission tomography; pet; radiation treatment; lung cancer; gross tumour volume; gtv; artificial intelligence; machine learning; support vector machine; svm

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MDPI and ACS Style

Kerhet, A.; Small, C.; Quon, H.; Riauka, T.; Schrader, L.; Greiner, R.; Yee, D.; McEwan, A.; Roa, W. Application of Machine Learning Methodology for Pet-Based Definition of Lung Cancer. Curr. Oncol. 2010, 17, 41-47. https://doi.org/10.3747/co.v17i1.394

AMA Style

Kerhet A, Small C, Quon H, Riauka T, Schrader L, Greiner R, Yee D, McEwan A, Roa W. Application of Machine Learning Methodology for Pet-Based Definition of Lung Cancer. Current Oncology. 2010; 17(1):41-47. https://doi.org/10.3747/co.v17i1.394

Chicago/Turabian Style

Kerhet, A., C. Small, H. Quon, T. Riauka, L. Schrader, R. Greiner, D. Yee, A. McEwan, and W. Roa. 2010. "Application of Machine Learning Methodology for Pet-Based Definition of Lung Cancer" Current Oncology 17, no. 1: 41-47. https://doi.org/10.3747/co.v17i1.394

APA Style

Kerhet, A., Small, C., Quon, H., Riauka, T., Schrader, L., Greiner, R., Yee, D., McEwan, A., & Roa, W. (2010). Application of Machine Learning Methodology for Pet-Based Definition of Lung Cancer. Current Oncology, 17(1), 41-47. https://doi.org/10.3747/co.v17i1.394

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