Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy

Roushanak Rahmat; David Harris-Birtill; David Finn; Yang Feng; Dean Montgomery; William H. Nailon; Stephen McLaughlin

doi:10.1007/978-3-030-80432-9_39

Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy

Roushanak Rahmat^*, David Harris-Birtill, David Finn, Yang Feng, Dean Montgomery, William H. Nailon, Stephen McLaughlin

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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Abstract

Co-locating the gross tumour volume (GTV) on cone-beam computed tomography (CBCT) of non small cell lung cancer (NSCLC) patients receiving radiotherapy (RT) is difficult because of the lack of image contrast between the tumour and surrounding tissue. This paper presents a new image analysis approach, based on second-order statistics obtained from gray level co-occurrence matrices (GLCM) combined with level sets, for assisting clinicians in identifying the GTV on CBCT images. To demonstrate the potential of the approach planning CT images from 50 NSCLC patients were rigidly registered with CBCT images from fractions 1 and 10. Image texture analysis was combined with two level set methodologies and used to automatically identify the GTV on the registered CBCT images. The Dice correlation coefficients (μ± σ) calculated between the clinician-defined and image analysis defined GTV on the planning CT and the CBCT for three different parameterisations of the model were: 0.69 ± 0.19, 0.63 ± 0.17, 0.86 ± 0.13 on fraction 1 CBCT images and 0.70 ± 0.17, 0.62 ± 0.15, 0.86 ± 0.12 on fraction 10 CBCT images. This preliminary data suggests that the image analysis approach presented may have potential for clinicians in identifying the GTV in low contrast CBCT images of NSCLC patients. Additional validation and further work, particularly in overcoming the lack of gold standard reference images, are required to progress this approach.

Original language	English
Title of host publication	Medical image understanding and analysis
Subtitle of host publication	25th annual conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings
Editors	Bartłomiej W. Papież, Mohammad Yaqub, Jianbo Jiao, Ana I. L. Namburete, J. Alison Noble
Place of Publication	Cham
Publisher	Springer
Pages	532–546
Number of pages	15
ISBN (Electronic)	9783030804329
ISBN (Print)	9783030804312
DOIs	https://doi.org/10.1007/978-3-030-80432-9_39
Publication status	Published - 6 Jul 2021
Event	Medical Image Understanding and Analysis: 25th Annual Conference - Oxford, United Kingdom Duration: 12 Jul 2021 → 14 Jul 2021 https://link.springer.com/book/10.1007/978-3-030-80432-9#about

Publication series

Name	Lecture notes in computer science
Volume	12722
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Medical Image Understanding and Analysis
Abbreviated title	MIUA 2021
Country/Territory	United Kingdom
City	Oxford
Period	12/07/21 → 14/07/21
Internet address	https://link.springer.com/book/10.1007/978-3-030-80432-9#about

Keywords

Image segmentation
Level set
Lung cancer
Radiomics
Radiotherapy

UN SDGs

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

Access to Document

10.1007/978-3-030-80432-9_39

Rahmat-Radiomics-led-monitoring-MIUA2021-AAM
Copyright © 2021 Springer Nature Switzerland AG. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-030-80432-9_39.
Accepted author manuscript, 3.51 MB

Cite this

Rahmat, R., Harris-Birtill, D., Finn, D., Feng, Y., Montgomery, D., Nailon, W. H., & McLaughlin, S. (2021). Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy. In B. W. Papież, M. Yaqub, J. Jiao, A. I. L. Namburete, & J. A. Noble (Eds.), Medical image understanding and analysis: 25th annual conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings (pp. 532–546). (Lecture notes in computer science; Vol. 12722). Springer. https://doi.org/10.1007/978-3-030-80432-9_39

Rahmat, Roushanak ; Harris-Birtill, David ; Finn, David et al. / Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy. Medical image understanding and analysis: 25th annual conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings. editor / Bartłomiej W. Papież ; Mohammad Yaqub ; Jianbo Jiao ; Ana I. L. Namburete ; J. Alison Noble. Cham : Springer, 2021. pp. 532–546 (Lecture notes in computer science).

@inproceedings{bd70915da2524ef1acecf7132402a087,

title = "Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy",

abstract = "Co-locating the gross tumour volume (GTV) on cone-beam computed tomography (CBCT) of non small cell lung cancer (NSCLC) patients receiving radiotherapy (RT) is difficult because of the lack of image contrast between the tumour and surrounding tissue. This paper presents a new image analysis approach, based on second-order statistics obtained from gray level co-occurrence matrices (GLCM) combined with level sets, for assisting clinicians in identifying the GTV on CBCT images. To demonstrate the potential of the approach planning CT images from 50 NSCLC patients were rigidly registered with CBCT images from fractions 1 and 10. Image texture analysis was combined with two level set methodologies and used to automatically identify the GTV on the registered CBCT images. The Dice correlation coefficients (μ± σ) calculated between the clinician-defined and image analysis defined GTV on the planning CT and the CBCT for three different parameterisations of the model were: 0.69 ± 0.19, 0.63 ± 0.17, 0.86 ± 0.13 on fraction 1 CBCT images and 0.70 ± 0.17, 0.62 ± 0.15, 0.86 ± 0.12 on fraction 10 CBCT images. This preliminary data suggests that the image analysis approach presented may have potential for clinicians in identifying the GTV in low contrast CBCT images of NSCLC patients. Additional validation and further work, particularly in overcoming the lack of gold standard reference images, are required to progress this approach.",

keywords = "Image segmentation, Level set, Lung cancer, Radiomics, Radiotherapy",

author = "Roushanak Rahmat and David Harris-Birtill and David Finn and Yang Feng and Dean Montgomery and Nailon, {William H.} and Stephen McLaughlin",

note = "Funding: The authors would like to thank EPSRC impact acceleration fund (EP/K503940/1) for helping support this project. RR was supported as part of the James-Watt Scholarship during her PhD research at the Heriot-Watt University.; Medical Image Understanding and Analysis : 25th Annual Conference, MIUA 2021 ; Conference date: 12-07-2021 Through 14-07-2021",

year = "2021",

month = jul,

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doi = "10.1007/978-3-030-80432-9_39",

language = "English",

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pages = "532–546",

editor = "Papie{\.z}, {Bart{\l}omiej W. } and Yaqub, {Mohammad } and Jianbo Jiao and Namburete, {Ana I. L.} and Noble, {J. Alison }",

booktitle = "Medical image understanding and analysis",

address = "Netherlands",

url = "https://link.springer.com/book/10.1007/978-3-030-80432-9#about",

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Rahmat, R, Harris-Birtill, D, Finn, D, Feng, Y, Montgomery, D, Nailon, WH & McLaughlin, S 2021, Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy. in BW Papież, M Yaqub, J Jiao, AIL Namburete & JA Noble (eds), Medical image understanding and analysis: 25th annual conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings. Lecture notes in computer science, vol. 12722, Springer, Cham, pp. 532–546, Medical Image Understanding and Analysis, Oxford, United Kingdom, 12/07/21. https://doi.org/10.1007/978-3-030-80432-9_39

Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy. / Rahmat, Roushanak; Harris-Birtill, David; Finn, David et al.
Medical image understanding and analysis: 25th annual conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings. ed. / Bartłomiej W. Papież; Mohammad Yaqub; Jianbo Jiao; Ana I. L. Namburete; J. Alison Noble. Cham: Springer, 2021. p. 532–546 (Lecture notes in computer science; Vol. 12722).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy

AU - Rahmat, Roushanak

AU - Harris-Birtill, David

AU - Finn, David

AU - Feng, Yang

AU - Montgomery, Dean

AU - Nailon, William H.

AU - McLaughlin, Stephen

N1 - Funding: The authors would like to thank EPSRC impact acceleration fund (EP/K503940/1) for helping support this project. RR was supported as part of the James-Watt Scholarship during her PhD research at the Heriot-Watt University.

PY - 2021/7/6

Y1 - 2021/7/6

N2 - Co-locating the gross tumour volume (GTV) on cone-beam computed tomography (CBCT) of non small cell lung cancer (NSCLC) patients receiving radiotherapy (RT) is difficult because of the lack of image contrast between the tumour and surrounding tissue. This paper presents a new image analysis approach, based on second-order statistics obtained from gray level co-occurrence matrices (GLCM) combined with level sets, for assisting clinicians in identifying the GTV on CBCT images. To demonstrate the potential of the approach planning CT images from 50 NSCLC patients were rigidly registered with CBCT images from fractions 1 and 10. Image texture analysis was combined with two level set methodologies and used to automatically identify the GTV on the registered CBCT images. The Dice correlation coefficients (μ± σ) calculated between the clinician-defined and image analysis defined GTV on the planning CT and the CBCT for three different parameterisations of the model were: 0.69 ± 0.19, 0.63 ± 0.17, 0.86 ± 0.13 on fraction 1 CBCT images and 0.70 ± 0.17, 0.62 ± 0.15, 0.86 ± 0.12 on fraction 10 CBCT images. This preliminary data suggests that the image analysis approach presented may have potential for clinicians in identifying the GTV in low contrast CBCT images of NSCLC patients. Additional validation and further work, particularly in overcoming the lack of gold standard reference images, are required to progress this approach.

AB - Co-locating the gross tumour volume (GTV) on cone-beam computed tomography (CBCT) of non small cell lung cancer (NSCLC) patients receiving radiotherapy (RT) is difficult because of the lack of image contrast between the tumour and surrounding tissue. This paper presents a new image analysis approach, based on second-order statistics obtained from gray level co-occurrence matrices (GLCM) combined with level sets, for assisting clinicians in identifying the GTV on CBCT images. To demonstrate the potential of the approach planning CT images from 50 NSCLC patients were rigidly registered with CBCT images from fractions 1 and 10. Image texture analysis was combined with two level set methodologies and used to automatically identify the GTV on the registered CBCT images. The Dice correlation coefficients (μ± σ) calculated between the clinician-defined and image analysis defined GTV on the planning CT and the CBCT for three different parameterisations of the model were: 0.69 ± 0.19, 0.63 ± 0.17, 0.86 ± 0.13 on fraction 1 CBCT images and 0.70 ± 0.17, 0.62 ± 0.15, 0.86 ± 0.12 on fraction 10 CBCT images. This preliminary data suggests that the image analysis approach presented may have potential for clinicians in identifying the GTV in low contrast CBCT images of NSCLC patients. Additional validation and further work, particularly in overcoming the lack of gold standard reference images, are required to progress this approach.

KW - Image segmentation

KW - Level set

KW - Lung cancer

KW - Radiomics

KW - Radiotherapy

UR - https://doi.org/10.1007/978-3-030-80432-9

U2 - 10.1007/978-3-030-80432-9_39

DO - 10.1007/978-3-030-80432-9_39

M3 - Conference contribution

SN - 9783030804312

T3 - Lecture notes in computer science

SP - 532

EP - 546

BT - Medical image understanding and analysis

A2 - Papież, Bartłomiej W.

A2 - Yaqub, Mohammad

A2 - Jiao, Jianbo

A2 - Namburete, Ana I. L.

A2 - Noble, J. Alison

PB - Springer

CY - Cham

T2 - Medical Image Understanding and Analysis

Y2 - 12 July 2021 through 14 July 2021

ER -

Rahmat R, Harris-Birtill D, Finn D, Feng Y, Montgomery D, Nailon WH et al. Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy. In Papież BW, Yaqub M, Jiao J, Namburete AIL, Noble JA, editors, Medical image understanding and analysis: 25th annual conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, proceedings. Cham: Springer. 2021. p. 532–546. (Lecture notes in computer science). Epub 2021 Jul 6. doi: 10.1007/978-3-030-80432-9_39

Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy

Abstract

Publication series

Conference

Keywords

UN SDGs

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Radiomics-led monitoring of non-small cell lung cancer patients during radiotherapy

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Impact Acceleration Account: Impact Acceleration Account

Cite this