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@ARTICLE{Rabe:301473,
      author       = {M. Rabe and E. F. Meliadò and S. N. Marschner$^*$ and C.
                      Belka$^*$ and S. Corradini and C. A. T. van den Berg and G.
                      Landry and C. Kurz},
      title        = {{P}atient-specific uncertainty calibration of deep
                      learning-based autosegmentation networks for adaptive
                      {MRI}-guided lung radiotherapy.},
      journal      = {Physics in medicine and biology},
      volume       = {70},
      number       = {10},
      issn         = {0031-9155},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2025-01015},
      pages        = {105018},
      year         = {2025},
      abstract     = {Objective.Uncertainty assessment of deep learning
                      autosegmentation (DLAS) models can support contour
                      corrections in adaptive radiotherapy (ART), e.g. by
                      utilizing Monte Carlo Dropout (MCD) uncertainty maps.
                      However, poorly calibrated uncertainties at the patient
                      level often render these clinically nonviable. We evaluated
                      population-based and patient-specific DLAS accuracy and
                      uncertainty calibration and propose a patient-specific
                      post-training uncertainty calibration method for DLAS in
                      ART.Approach.The study included 122 lung cancer patients
                      treated with a low-field MR-linac (80/19/23
                      training/validation/test cases). Ten single-label 3D-U-Net
                      population-based baseline models (BM) were trained with
                      dropout using planning MRIs (pMRIs) and contours for nine
                      organs-at-riks (OARs) and gross tumor volumes (GTVs).
                      Patient-specific models (PS) were created by fine-tuning BMs
                      with each test patient's pMRI. Model uncertainty was
                      assessed with MCD, averaged into probability maps.
                      Uncertainty calibration was evaluated with reliability
                      diagrams and expected calibration error (ECE). A proposed
                      post-training calibration method rescaled MCD probabilities
                      for fraction images in BM (calBM) and PS (calPS) after
                      fitting reliability diagrams from pMRIs. All models were
                      evaluated on fraction images using Dice similarity
                      coefficient (DSC), 95th percentile Hausdorff distance (HD95)
                      and ECE. Metrics were compared among models for all OARs
                      combined (n = 163), and the GTV (n = 23), using Friedman and
                      posthoc-Nemenyi tests (α = 0.05).Main results.For the OARs,
                      patient-specific fine-tuning significantly (p < 0.001)
                      increased median DSC from 0.78 (BM) to 0.86 (PS) and reduced
                      HD95 from 14 mm (BM) to 6.0 mm (PS). Uncertainty calibration
                      achieved substantial reductions in ECE, from 0.25 (BM) to
                      0.091 (calBM) and 0.22 (PS) to 0.11 (calPS) (p < 0.001),
                      without significantly affecting DSC or HD95 (p > 0.05). For
                      the GTV, BM performance was poor (DSC = 0.05) but
                      significantly (p < 0.001) improved with PS training (DSC =
                      0.75) while uncertainty calibration reduced ECE from 0.22
                      (PS) to 0.15 (calPS) (p = 0.45).Significance.Post-training
                      uncertainty calibration yields geometrically accurate DLAS
                      models with well-calibrated uncertainty estimates, crucial
                      for ART applications.},
      keywords     = {Humans / Deep Learning / Uncertainty / Radiotherapy,
                      Image-Guided: methods / Calibration / Lung Neoplasms:
                      radiotherapy / Lung Neoplasms: diagnostic imaging / Magnetic
                      Resonance Imaging / Organs at Risk: radiation effects /
                      Radiotherapy Planning, Computer-Assisted: methods / MR-linac
                      (Other) / Monte Carlo dropout (Other) / adaptive
                      radiotherapy (Other) / autosegmentation (Other) / deep
                      learning (Other) / epistemic uncertainty (Other) /
                      uncertainty calibration (Other)},
      cin          = {MU01},
      ddc          = {530},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40340988},
      doi          = {10.1088/1361-6560/add640},
      url          = {https://inrepo02.dkfz.de/record/301473},
}