The role of computational methods for automating and improving clinical target volume definition

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review

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The role of computational methods for automating and improving clinical target volume definition. / Unkelbach, Jan; Bortfeld, Thomas; Cardenas, Carlos E.; Gregoire, Vincent; Hager, Wille; Heijmen, Ben; Jeraj, Robert; Korreman, Stine S.; Ludwig, Roman; Pouymayou, Bertrand; Shusharina, Nadya; Söderberg, Jonas; Toma-Dasu, Iuliana; Troost, Esther G.C.; Vasquez Osorio, Eliana.

In: Radiotherapy and Oncology, Vol. 153, 12.2020, p. 15-25.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review

Harvard

Unkelbach, J, Bortfeld, T, Cardenas, CE, Gregoire, V, Hager, W, Heijmen, B, Jeraj, R, Korreman, SS, Ludwig, R, Pouymayou, B, Shusharina, N, Söderberg, J, Toma-Dasu, I, Troost, EGC & Vasquez Osorio, E 2020, 'The role of computational methods for automating and improving clinical target volume definition', Radiotherapy and Oncology, vol. 153, pp. 15-25. https://doi.org/10.1016/j.radonc.2020.10.002

APA

Unkelbach, J., Bortfeld, T., Cardenas, C. E., Gregoire, V., Hager, W., Heijmen, B., Jeraj, R., Korreman, S. S., Ludwig, R., Pouymayou, B., Shusharina, N., Söderberg, J., Toma-Dasu, I., Troost, E. G. C., & Vasquez Osorio, E. (2020). The role of computational methods for automating and improving clinical target volume definition. Radiotherapy and Oncology, 153, 15-25. https://doi.org/10.1016/j.radonc.2020.10.002

CBE

Unkelbach J, Bortfeld T, Cardenas CE, Gregoire V, Hager W, Heijmen B, Jeraj R, Korreman SS, Ludwig R, Pouymayou B, Shusharina N, Söderberg J, Toma-Dasu I, Troost EGC, Vasquez Osorio E. 2020. The role of computational methods for automating and improving clinical target volume definition. Radiotherapy and Oncology. 153:15-25. https://doi.org/10.1016/j.radonc.2020.10.002

MLA

Vancouver

Unkelbach J, Bortfeld T, Cardenas CE, Gregoire V, Hager W, Heijmen B et al. The role of computational methods for automating and improving clinical target volume definition. Radiotherapy and Oncology. 2020 Dec;153:15-25. https://doi.org/10.1016/j.radonc.2020.10.002

Author

Unkelbach, Jan ; Bortfeld, Thomas ; Cardenas, Carlos E. ; Gregoire, Vincent ; Hager, Wille ; Heijmen, Ben ; Jeraj, Robert ; Korreman, Stine S. ; Ludwig, Roman ; Pouymayou, Bertrand ; Shusharina, Nadya ; Söderberg, Jonas ; Toma-Dasu, Iuliana ; Troost, Esther G.C. ; Vasquez Osorio, Eliana. / The role of computational methods for automating and improving clinical target volume definition. In: Radiotherapy and Oncology. 2020 ; Vol. 153. pp. 15-25.

Bibtex

@article{9e31752d5a7949ac80f05b923877b688,
title = "The role of computational methods for automating and improving clinical target volume definition",
abstract = "Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.",
keywords = "Automatic image segmentation, Clinical target volume, Computational tumor growth models",
author = "Jan Unkelbach and Thomas Bortfeld and Cardenas, {Carlos E.} and Vincent Gregoire and Wille Hager and Ben Heijmen and Robert Jeraj and Korreman, {Stine S.} and Roman Ludwig and Bertrand Pouymayou and Nadya Shusharina and Jonas S{\"o}derberg and Iuliana Toma-Dasu and Troost, {Esther G.C.} and {Vasquez Osorio}, Eliana",
year = "2020",
month = dec,
doi = "10.1016/j.radonc.2020.10.002",
language = "English",
volume = "153",
pages = "15--25",
journal = "Radiotherapy & Oncology",
issn = "0167-8140",
publisher = "Elsevier Ireland Ltd.",

}

RIS

TY - JOUR

T1 - The role of computational methods for automating and improving clinical target volume definition

AU - Unkelbach, Jan

AU - Bortfeld, Thomas

AU - Cardenas, Carlos E.

AU - Gregoire, Vincent

AU - Hager, Wille

AU - Heijmen, Ben

AU - Jeraj, Robert

AU - Korreman, Stine S.

AU - Ludwig, Roman

AU - Pouymayou, Bertrand

AU - Shusharina, Nadya

AU - Söderberg, Jonas

AU - Toma-Dasu, Iuliana

AU - Troost, Esther G.C.

AU - Vasquez Osorio, Eliana

PY - 2020/12

Y1 - 2020/12

N2 - Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.

AB - Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.

KW - Automatic image segmentation

KW - Clinical target volume

KW - Computational tumor growth models

UR - http://www.scopus.com/inward/record.url?scp=85094825064&partnerID=8YFLogxK

U2 - 10.1016/j.radonc.2020.10.002

DO - 10.1016/j.radonc.2020.10.002

M3 - Review

C2 - 33039428

AN - SCOPUS:85094825064

VL - 153

SP - 15

EP - 25

JO - Radiotherapy & Oncology

JF - Radiotherapy & Oncology

SN - 0167-8140

ER -