TY  - JOUR
AU  - Wei, Chengtao
AU  - Eze, Chukwuka
AU  - Klaar, Rabea
AU  - Thorwarth, Daniela
AU  - Warda, Cora
AU  - Taugner, Julian
AU  - Hörner-Rieber, Juliane
AU  - Regnery, Sebastian
AU  - Jäkel, Oliver
AU  - Weykamp, Fabian
AU  - Palacios, Miguel A
AU  - Marschner, Sebastian
AU  - Corradini, Stefanie
AU  - Belka, Claus
AU  - Kurz, Christopher
AU  - Landry, Guillaume
AU  - Rabe, Moritz
TI  - Deep learning-based contour propagation in magnetic resonance imaging-guided radiotherapy of lung cancer patients.
JO  - Physics in medicine and biology
VL  - 70
SN  - 0031-9155
CY  - Bristol
PB  - IOP Publ.
M1  - DKFZ-2025-01305
SP  - 145018
PY  - 2025
N1  - Med. Biol. 70 145018
AB  - Fast and accurate organ-at-risk (OAR) and gross tumor volume (GTV) contour propagation methods are needed to improve the efficiency of magnetic resonance (MR) imaging-guided radiotherapy. We trained deformable image registration networks to accurately propagate contours from planning to fraction MR images. Approach: Data from 140 stage 1-2 lung cancer patients treated at a 0.35T MR-Linac were split into 102/17/21 for training/validation/testing. Additionally, 18 central lung tumor patients, treated at a 0.35T MR-Linac externally, and 14 stage 3 lung cancer patients from a phase 1 clinical trial, treated at 0.35T or 1.5T MR-Linacs at three institutions, were used for external testing. Planning and fraction images were paired (490 pairs) for training. Two hybrid transformer-convolutional neural network TransMorph models with mean squared error (MSE), Dice similarity coefficient (DSC), and regularization losses (TM_MSE+Dice) or MSE and regularization losses (TM_MSE) were trained to deformably register planning to fraction images. The TransMorph models predicted diffeomorphic dense displacement fields. Multi-label images including seven thoracic OARs and the GTV were propagated to generate fraction segmentations. Model predictions were compared with contours obtained through B-spline, vendor registration and the auto-segmentation method nnUNet. Evaluation metrics included the DSC and Hausdorff distance percentiles (50th and 95th) against clinical contours. Main results: TM_MSE+Dice and TM_MSE achieved mean OARs/GTV DSCs of 0.90/0.82 and 0.90/0.79 for the internal and 0.84/0.77 and 0.85/0.76 for the central lung tumor external test data. On stage 3 data, TM_MSE+Dice achieved mean OARs/GTV DSCs of 0.87/0.79 and 0.83/0.78 for the 0.35 T MR-Linac datasets, and 0.87/0.75 for the 1.5 T MR-Linac dataset. TM_MSE+Dice and TM_MSE had significantly higher geometric accuracy than other methods on external data. No significant difference between TM_MSE+Dice and TM_MSE was found. Significance: TransMorph models achieved time-efficient segmentation of fraction MRIs with high geometrical accuracy and accurately segmented images obtained at different field strengths.
KW  - MR-linac (Other)
KW  - MRgRT (Other)
KW  - PUMA (Other)
KW  - TransMorph (Other)
KW  - deep learning (Other)
KW  - image registration (Other)
KW  - lung cancer (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40570891
DO  - DOI:10.1088/1361-6560/ade8d0
UR  - https://inrepo02.dkfz.de/record/302286
ER  -