guest ::
| Home > Publications database > Impact of patient-specific deep learning lung organs-at-risk segmentation on accumulated dose in online adaptive 0.35 T MR-guided radiotherapy. |
| Home > Publications database > Impact of patient-specific deep learning lung organs-at-risk segmentation on accumulated dose in online adaptive 0.35 T MR-guided radiotherapy. |
| Journal Article | DKFZ-2025-02434 |
; ; ; ; ; ; ;
2025
IOP Publ.
Bristol
Abstract: Objective.Online adaptation in magnetic resonance imaging-guided radiotherapy (MRgRT) for lung cancer is hindered by time-consuming organs-at-risk (OARs) recontouring on daily MR images (dMRIs) and inter-/intra-observer variability. Deep learning auto-segmentation of OARs offers an efficient alternative. While baseline models (BMs) provide general segmentation, patient-specific (PS) training using expert-delineated planning MR images (pMRIs) can enhance accuracy. This study evaluated accumulated dose differences between BM and PS OAR models without manual modification in online plan adaptation.Approach.Eleven lung cancer patients treated with a 0.35 T magnetic resonance linear accelerator were retrospectively analyzed. Pre-trained population-based 3D U-Nets (BM) for nine thoracic OARs served as initial models for PS fine-tuning on planning MRIs. BM- and PS-generated OAR contours per fraction were imported into an MRgRT treatment planning system, along with clinical expert target contours. Online adaptive doses were re-optimized using both models' OAR contours with the same clinical objective functions. Fraction doses were accumulated on the pMRI, and dose-volume histogram parameters of PTV, GTV, and other OARs within PTV + 3 cm were calculated using clinical contours on pMRI. A Wilcoxon signed-rank test was used to test for statistical differences (α = 0.05) compared to accumulated clinical doses.Main results.PS models improved segmentation accuracy for all OARs compared to BM. They also mitigated substantial outliers inD1ccBMversusD1ccclinicaland resulted in higher PTVD95%and GTVD98%than clinical plans. Overall,DBMmet 48/61 OAR constraints, whileDPSmet 53. For PTVs, bothDPSandDBMsatisfied 21/25 constraints.Significance.Unmodified BM and PS model contours yielded median accumulated doses comparable to clinically delivered doses. However, PS models demonstrated superior geometric alignment, improved OAR sparing, and enhanced target coverage compared to BM, potentially benefiting MRgRT lung cancer patients.
Keyword(s): Humans (MeSH) ; Deep Learning (MeSH) ; Organs at Risk: radiation effects (MeSH) ; Radiotherapy, Image-Guided: methods (MeSH) ; Magnetic Resonance Imaging (MeSH) ; Radiotherapy Dosage (MeSH) ; Lung Neoplasms: radiotherapy (MeSH) ; Lung Neoplasms: diagnostic imaging (MeSH) ; Radiotherapy Planning, Computer-Assisted: methods (MeSH) ; Radiation Dosage (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Lung: radiation effects (MeSH) ; Lung: diagnostic imaging (MeSH) ; Retrospective Studies (MeSH) ; Male (MeSH) ; MR-linac ; MRgRT ; adaptive radiotherapy ; auto-segmentation ; deep learning ; dose accumulation ; lung cancer
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |
