Validation of Different Automated Segmentation Models for Target Volume Contouring in Postoperative Radiotherapy for Breast Cancer and Regional Nodal Irradiation

Overview

Journal Clin Transl Radiat Oncol

Specialty Oncology

Date 2024 Sep 23

PMID 39308634

Authors

Eva Meixner

Benjamin Glogauer

Sebastian Kluter

Friedrich Wagner

David Neugebauer

Line Hoeltgen

Lisa A Dinges

Semi Harrabi

Jakob Liermann

Maria Vinsensia

Fabian Weykamp

Philipp Hoegen-Sassmannshausen

Jurgen Debus

Juliane Horner-Rieber

Affiliations

Soon will be listed here.

Abstract

Introduction: Target volume delineation is routinely performed in postoperative radiotherapy (RT) for breast cancer patients, but it is a time-consuming process. The aim of the present study was to validate the quality, clinical usability and institutional-specific implementation of different auto-segmentation tools into clinical routine.

Methods: Three different commercially available, artificial intelligence-, ESTRO-guideline-based segmentation models (M1-3) were applied to fifty consecutive reference patients who received postoperative local RT including regional nodal irradiation for breast cancer for the delineation of clinical target volumes: the residual breast, implant or chestwall, axilla levels 1 and 2, the infra- and supraclavicular regions, the interpectoral and internal mammary nodes. Objective evaluation metrics of the created structures were conducted with the Dice similarity index (DICE) and the Hausdorff distance, and a manual evaluation of usability.

Results: The resulting geometries of the segmentation models were compared to the reference volumes for each patient and required no or only minor corrections in 72 % (M1), 64 % (M2) and 78 % (M3) of the cases. The median DICE and Hausdorff values for the resulting planning target volumes were 0.87-0.88 and 2.96-3.55, respectively. Clinical usability was significantly correlated with the DICE index, with calculated cut-off values used to define no or minor adjustments of 0.82-0.86. Right or left sided target and breathing method (deep inspiration breath hold vs. free breathing) did not impact the quality of the resulting structures.

Conclusion: Artificial intelligence-based auto-segmentation programs showed high-quality accuracy and provided standardization and efficient support for guideline-based target volume contouring as a precondition for fully automated workflows in radiotherapy treatment planning.

References

Strolin S, Santoro M, Paolani G, Ammendolia I, Arcelli A, Benini A . How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images. Front Oncol. 2023; 13:1089807. PMC: 10019504. DOI: 10.3389/fonc.2023.1089807. View

Vaassen F, Hazelaar C, Vaniqui A, Gooding M, van der Heyden B, Canters R . Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy. Phys Imaging Radiat Oncol. 2021; 13:1-6. PMC: 7807544. DOI: 10.1016/j.phro.2019.12.001. View

Vandewinckele L, Claessens M, Dinkla A, Brouwer C, Crijns W, Verellen D . Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance. Radiother Oncol. 2020; 153:55-66. DOI: 10.1016/j.radonc.2020.09.008. View

Liu Z, Liu F, Chen W, Liu X, Hou X, Shen J . Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks. Front Oncol. 2021; 10:581347. PMC: 7921705. DOI: 10.3389/fonc.2020.581347. View

Boero I, Paravati A, Xu B, Cohen E, Mell L, Le Q . Importance of Radiation Oncologist Experience Among Patients With Head-and-Neck Cancer Treated With Intensity-Modulated Radiation Therapy. J Clin Oncol. 2016; 34(7):684-90. PMC: 4872027. DOI: 10.1200/JCO.2015.63.9898. View

Delaney A, Dahele M, Slotman B, Verbakel W . Is accurate contouring of salivary and swallowing structures necessary to spare them in head and neck VMAT plans?. Radiother Oncol. 2018; 127(2):190-196. DOI: 10.1016/j.radonc.2018.03.012. View

Bakx N, Van der Sangen M, Theuws J, Bluemink H, Hurkmans C . Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets. Tech Innov Patient Support Radiat Oncol. 2023; 26:100209. PMC: 10199413. DOI: 10.1016/j.tipsro.2023.100209. View

Sim D, Kwon O, Park R . Object matching algorithms using robust Hausdorff distance measures. IEEE Trans Image Process. 2008; 8(3):425-9. DOI: 10.1109/83.748897. View

Huang D, Bai H, Wang L, Hou Y, Li L, Xia Y . The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technol Cancer Res Treat. 2021; 20:15330338211016386. PMC: 8216350. DOI: 10.1177/15330338211016386. View

10.

Bakx N, Rijkaart D, Van der Sangen M, Theuws J, van der Toorn P, Verrijssen A . Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer. Tech Innov Patient Support Radiat Oncol. 2023; 26:100211. PMC: 10205480. DOI: 10.1016/j.tipsro.2023.100211. View

11.

Chung S, Chang J, Choi M, Chang Y, Choi B, Chun J . Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery. Radiat Oncol. 2021; 16(1):44. PMC: 7905884. DOI: 10.1186/s13014-021-01771-z. View

12.

Sharp G, Fritscher K, Pekar V, Peroni M, Shusharina N, Veeraraghavan H . Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014; 41(5):050902. PMC: 4000389. DOI: 10.1118/1.4871620. View

13.

Li X, Tai A, Arthur D, Buchholz T, MacDonald S, Marks L . Variability of target and normal structure delineation for breast cancer radiotherapy: an RTOG Multi-Institutional and Multiobserver Study. Int J Radiat Oncol Biol Phys. 2009; 73(3):944-51. PMC: 2911777. DOI: 10.1016/j.ijrobp.2008.10.034. View

14.

Radici L, Ferrario S, Casanova Borca V, Cante D, Paolini M, Piva C . Implementation of a Commercial Deep Learning-Based Auto Segmentation Software in Radiotherapy: Evaluation of Effectiveness and Impact on Workflow. Life (Basel). 2022; 12(12). PMC: 9782080. DOI: 10.3390/life12122088. View

15.

Duke S, Tan L, Jensen N, Rumpold T, de Leeuw A, Kirisits C . Implementing an online radiotherapy quality assurance programme with supporting continuous medical education - report from the EMBRACE-II evaluation of cervix cancer IMRT contouring. Radiother Oncol. 2020; 147:22-29. DOI: 10.1016/j.radonc.2020.02.017. View

16.

Sherer M, Lin D, Elguindi S, Duke S, Tan L, Cacicedo J . Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol. 2021; 160:185-191. PMC: 9444281. DOI: 10.1016/j.radonc.2021.05.003. View

17.

Almberg S, Lervag C, Frengen J, Eidem M, Abramova T, Nordstrand C . Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer. Radiother Oncol. 2022; 173:62-68. DOI: 10.1016/j.radonc.2022.05.018. View

18.

Offersen B, Boersma L, Kirkove C, Hol S, Aznar M, Sola A . ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer, version 1.1. Radiother Oncol. 2016; 118(1):205-8. DOI: 10.1016/j.radonc.2015.12.027. View

19.

Tao C, Yi J, Chen N, Ren W, Cheng J, Tung S . Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: A multi-institution clinical study. Radiother Oncol. 2015; 115(3):407-11. DOI: 10.1016/j.radonc.2015.05.012. View

20.

Taha A, Hanbury A . Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015; 15:29. PMC: 4533825. DOI: 10.1186/s12880-015-0068-x. View