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Bakx N, Rijkaart D, van der Sangen M, Theuws J, van der Toorn PP, Verrijssen AS, van der Leer J, Mutsaers J, van Nunen T, Reinders M, Schuengel I, Smits J, Hagelaar E, van Gruijthuijsen D, Bluemink H, Hurkmans C. 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. [PMID: 37229460 PMCID: PMC10205480 DOI: 10.1016/j.tipsro.2023.100211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/23/2023] [Accepted: 05/09/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively. Methods For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1-4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to compare both the automatic (not-corrected) and corrected contours with the manual contours. Qualitative scoring was performed by five radiotherapy technologists and five radiation oncologists using a 3-point Likert scale. Results Time reduction was achieved using auto-segmentation in 95% of the cases, including correction. The time reduction (mean ± std) was 42.4% ± 26.5% and 58.5% ± 19.1% for OARs and CTVs, respectively, corresponding to an absolute mean reduction (hh:mm:ss) of 00:08:51 and 00:25:38. Good quantitative results were achieved before correction, e.g. mean DSC for the right-sided CTVp was 0.92 ± 0.06, whereas correction statistically significantly improved this contour by only 0.02 ± 0.05, respectively. In 92% of the cases, auto-contours were scored as clinically acceptable, with or without corrections. Conclusions A DL segmentation model was trained and was shown to be a time-efficient way to generate clinically acceptable contours for locally advanced breast cancer.
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Affiliation(s)
- Nienke Bakx
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Dorien Rijkaart
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | | | - Jacqueline Theuws
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | | | - An-Sofie Verrijssen
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Jorien van der Leer
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Joline Mutsaers
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Thérèse van Nunen
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Marjon Reinders
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Inge Schuengel
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Julia Smits
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Els Hagelaar
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | | | - Hanneke Bluemink
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Coen Hurkmans
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
- Technical University Eindhoven, Faculties of Physics and Electrical Engineering, Eindhoven, the Netherlands
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