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 Information:
Acknowledgements. The authors would like to thank Dr Allan Price for the clinical validation along many fruitful discussions. Also, all members of Oncology Physics and Radiography Department at the Edinburgh Cancer Centre. We 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.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
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 - http://www.scopus.com/inward/record.url?scp=85112241493&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80432-9_39
DO - 10.1007/978-3-030-80432-9_39
M3 - Conference contribution
AN - SCOPUS:85112241493
SN - 9783030804312
T3 - Lecture Notes in Computer Science
SP - 532
EP - 546
BT - Medical Image Understanding and Analysis. MIUA 2021
A2 - Papież, Bartłomiej W.
A2 - Yaqub, Mohammad
A2 - Jiao, Jianbo
A2 - Namburete, Ana I.
A2 - Noble, J. Alison
PB - Springer
T2 - 25th Annual Conference on Medical Image Understanding and Analysis 2021
Y2 - 12 July 2021 through 14 July 2021
ER -