Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer

Bakx, Nienke; Rijkaart, Dorien; van der Sangen, Maurice; Theuws, Jacqueline; van der Toorn, Peter-Paul; Verrijssen, An-Sofie; van der Leer, Jorien; Mutsaers, Joline; van Nunen, Thérèse; Reinders, Marjon; Schuengel, Inge; Smits, Julia; Hagelaar, Els; van Gruijthuijsen, Dave; Bluemink, Johanna; Hurkmans, Coen

doi:10.1016/j.tipsro.2023.100211

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Time needed (mean ± standard deviation, mm:ss) by RTTs and ROs to check and correct (a subset of ) ROIs using the DL model (column 1). Column 2 and column 4 display results of a previously performed pilot for comparison [13]. P-values indicate statistical significance between clinical phase and pilot study.…”

Section: Discussionmentioning

confidence: 99%

“…Previously, in our clinic, a pilot study was performed for an in-house trained DL segmentation model for locoregional breast ACTA ONCOLOGICA 2024, VOL. 63, 477-481 https://doi.org/10.2340/1651-226X.2024.34986 cancer radiotherapy, after which the model was implemented in the clinical workflow [13]. This study aims to assess if differences in performance or acceptability arise after clinical implementation, by evaluating the use of the model in the real-world clinical setting and comparing it to the previous results of the pilot study.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy

Bakx,

Van der Sangen,

Theuws

et al. 2024

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Background: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically implemented in-house trained DL segmentation model for breast cancer to a previously performed pilot study to assess possible differences in performance or acceptability. Material and methods: Sixty patients with whole breast radiotherapy, with or without an indication for locoregional radiotherapy were included. Structures were qualitatively scored by radiotherapy technologists and radiation oncologists. Quantitative evaluation was performed using dice-similarity coefficient (DSC), 95th percentile of Hausdorff Distance (95%HD) and surface DSC (sDSC), and time needed for generating, checking, and correcting structures was measured. Results: Ninety-three percent of all contours in clinic were scored as clinically acceptable or usable as a starting point, comparable to 92% achieved in the pilot study. Compared to the pilot study, no significant changes in time reduction were achieved for organs at risks (OARs). For target volumes, significantly more time was needed compared to the pilot study for patients including lymph node levels 1–4, although time reduction was still 33% compared to manual segmentation. Almost all contours have better DSC and 95%HD than inter-observer variations. Only CTVn4 scored worse for both metrics, and the thyroid had a higher 95%HD value. Interpretation: The use of the DL model in clinical practice is comparable to the pilot study, showing high acceptability rates and time reduction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy

Bakx,

Van der Sangen,

Theuws

et al. 2024

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our overall positive conclusion regarding the clinical implementation of deep learning-based auto-segmentation is in line with recently published literature. Our study covers all clinically relevant sites, compared to more narrow studies for head-and-neck [6] , [10] , [12] , breast [13] , prostate [12] , [14] , thorax [15] , [16] , central nervous system [12] , or cervix [17] .…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, performing an extensive evaluation of auto-segmentation prior to clinical implementation to understand their accuracy and limitations is crucial. Previous studies have focused on single sites [6] , [10] , [12] , [13] , [14] , [15] , [16] , [17] . This study aimed to develop a comprehensive procedure for evaluating AI-based auto-segmentation software for all sites before clinical implementation.…”

Section: Introductionmentioning

confidence: 99%

Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy

Heilemann,

Buschmann,

Lechner

et al. 2023

Physics and Imaging in Radiation Oncology

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Automated segmentation in planning-CT for breast cancer radiotherapy: A review of recent advances

Smine,

Poeta,

De Caluwé

et al. 2025

Radiotherapy and Oncology

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Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer

Cited by 5 publications

References 23 publications

Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy

Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy

Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy

Automated segmentation in planning-CT for breast cancer radiotherapy: A review of recent advances

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