The effect of editing clinical contours on deep-learning segmentation accuracy of the gross tumor volume in glioblastoma
| Autor: | Kim M. Hochreuter, Jintao Ren, Jasper Nijkamp, Stine S. Korreman, Slávka Lukacova, Jesper F. Kallehauge, Anouk K. Trip |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Physics and Imaging in Radiation Oncology, Vol 31, Iss , Pp 100620- (2024) |
| Druh dokumentu: | article |
| ISSN: | 2405-6316 99993759 |
| DOI: | 10.1016/j.phro.2024.100620 |
| Popis: | Background and purpose: Deep-learning (DL) models for segmentation of the gross tumor volume (GTV) in radiotherapy are generally based on clinical delineations which suffer from inter-observer variability. The aim of this study was to compare performance of a DL-model based on clinical glioblastoma GTVs to a model based on a single-observer edited version of the same GTVs. Materials and methods: The dataset included imaging data (Computed Tomography (CT), T1, contrast-T1 (T1C), and fluid-attenuated-inversion-recovery (FLAIR)) of 259 glioblastoma patients treated with post-operative radiotherapy between 2012 and 2019 at a single institute. The clinical GTVs were edited using all imaging data. The dataset was split into 207 cases for training/validation and 52 for testing.GTV segmentation models (nnUNet) were trained on clinical and edited GTVs separately and compared using Surface Dice with 1 mm tolerance (sDSC1mm). We also evaluated model performance with respect to extent of resection (EOR), and different imaging combinations (T1C/T1/FLAIR/CT, T1C/FLAIR/CT, T1C/FLAIR, T1C/CT, T1C/T1, T1C). A Wilcoxon test was used for significance testing. Results: The median (range) sDSC1mm of the clinical-GTV-model and edited-GTV-model both evaluated with the edited contours, was 0.76 (0.43–0.94) vs. 0.92 (0.60–0.98) respectively (p |
| Databáze: | Directory of Open Access Journals |
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