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An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer

T0 review · 2 major / 1 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read The uncertainty-aware dose accumulation framework propagates DIR uncertainty through dose warping to produce probabilistic DVHs for adaptive radiotherapy.

desk verdict The paper gives a usable pipeline for pushing DIR uncertainty into pDVH estimates on CBCT data for cervical oART, but the decision to ignore vCT-generation errors narrows what the calibration numbers actually show. read the letter →

arxiv 2606.11012 v1 pith:PUU5FGZQ submitted 2026-06-09 cs.CV

Cedric Hemon , Delphine Lebret , Jean-Claude Nunes , Valentin Boussot , Karine Peignaux , Nathalie Mesgouez-Nebout , Chantal Hanzen , Antoine Simon
show 3 more authors
Anaïs Barateau Renaud de Crevoisier Caroline Lafond
This is my paper · ORCID
classification cs.CV
keywords adaptiveradiotherapydoseaccumulationuncertaintyestimationdeformableimageregistrationcervicalcancerCBCTprobabilisticDVH
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces a framework to estimate uncertainty arising from deformable image registration and propagate it into cumulative radiation dose estimates during online adaptive radiotherapy. Anatomical changes between daily fractions make it difficult to determine the actual total dose received by targets and organs at risk. Two strategies for quantifying DIR uncertainty, one Bayesian segmentation-guided and one based on an ensemble of models, generate voxel-wise uncertainty maps that are carried forward through dose recalculation and accumulation on virtual CTs derived from CBCT scans. The result is a set of probabilistic dose-volume histograms whose calibration is checked against geometric error measures in nine patients with locally advanced cervical cancer. A weighting step based on anatomical variability is used to combine results across treatment fractions.

What carries the argument

Voxel-wise uncertainty maps generated by Bayesian or ensemble DIR strategies, propagated through dose warping and accumulation steps to yield probabilistic dose-volume histograms with anatomical-variability weighting.

What would settle it

A finding that probabilistic DVH coverage falls well below 96 percent or that voxel-wise uncertainty shows no correlation with measured surface distances on warped contours would indicate the propagation step does not reliably represent the uncertainty.

Watch

Extended reading notes

Core claim

The framework focuses on uncertainty from DIR without modeling vCT-generation uncertainty. Two DIR uncertainty strategies were tested: a Bayesian segmentation-guided approach using one probabilistic model to quantify anatomical uncertainty, and an ensemble of segmentation models targeting structures to capture epistemic variability. Voxel-wise uncertainty maps were propagated through dose warping and accumulation to generate probabilistic dose-volume histograms. Ensemble uncertainty was quantified from voxel-wise standard deviation across deformation fields, and geometric error was assessed using surface distance between warped and validated contours.

Load-bearing premise

The two tested DIR uncertainty strategies sufficiently capture the dominant sources of anatomical uncertainty for dose accumulation.

Editorial extensions

If this is right

  • Ensemble DIR uncertainty correlated with geometric error at Pearson coefficients of 0.63 for CTVt and 0.66 for bladder.
  • Probabilistic DVHs for CTVt achieved 96.3 plus or minus 3.9 percent coverage.
  • Anatomical-variability weighting stabilized dose estimates across fractions and organs.
  • The approach integrates with 3DSlicer to support reproducible workflows.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same propagation steps could be tested on other disease sites where daily anatomical variation affects cumulative dose interpretation.
  • Because vCT-generation uncertainty is left out, the reported uncertainty ranges may understate the full variability present in clinical use.
  • If the uncertainty maps prove reliable, they could be used to set thresholds for triggering plan adaptation when accumulated dose confidence intervals become too wide.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces IMPACT-DoseAcc, a framework for propagating DIR uncertainty through dose warping and accumulation to produce probabilistic DVHs in CBCT-guided online adaptive radiotherapy for locally advanced cervical cancer. It tests two segmentation-based DIR uncertainty strategies (Bayesian and ensemble) on nine retrospective patients, reports Pearson correlations of 0.63-0.66 between uncertainty and surface distance, 96.3% pDVH coverage, and provides 3D Slicer integration.

Significance. If the calibration and correlation results hold under broader validation, the framework offers a practical, modality-agnostic approach to quantifying registration-driven uncertainty in cumulative dose metrics, which could support more informed clinical decisions in adaptive workflows. The explicit 3D Slicer integration is a clear strength for reproducibility.

major comments (2)
  1. [Material and Methods] Material and Methods: The framework is explicitly scoped to DIR uncertainty only ("without modeling vCT-generation uncertainty"), yet the CBCT-to-vCT pipeline for LACC involves vCT generation steps (HU calibration, scatter correction, density mapping) that precede dose recalculation and warping. If these sources are comparable to or larger than DIR errors, the voxel-wise uncertainty maps and resulting 96.3% pDVH coverage capture only a subset of total anatomical variability, weakening the claim that propagation improves interpretation of accumulated dose under anatomical variations.
  2. [Results] Results: The reported Pearson correlations (0.63 for CTVt, 0.66 for bladder) and pDVH coverage of 96.3 +/- 3.9% are derived from an internal cohort of only nine patients, with no comparison to a non-uncertainty baseline dose accumulation method and no external validation cohort. This leaves the evidence for calibration and improved interpretation preliminary and limits assessment of whether the uncertainty estimates add value beyond standard practice.
minor comments (1)
  1. [Abstract] Abstract and Results: Clarify whether the +/- 3.9% on pDVH coverage refers to a figure that lacks visible error bars, and ensure consistency between text and any accompanying figures.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments. We provide point-by-point responses to the major comments below.

read point-by-point responses

  1. Referee: [Material and Methods] Material and Methods: The framework is explicitly scoped to DIR uncertainty only ("without modeling vCT-generation uncertainty"), yet the CBCT-to-vCT pipeline for LACC involves vCT generation steps (HU calibration, scatter correction, density mapping) that precede dose recalculation and warping. If these sources are comparable to or larger than DIR errors, the voxel-wise uncertainty maps and resulting 96.3% pDVH coverage capture only a subset of total anatomical variability, weakening the claim that propagation improves interpretation of accumulated dose under anatomical variations.

    Authors: We agree that the manuscript explicitly limits the scope to DIR uncertainty propagation, as stated in the Methods section. vCT-generation steps introduce additional uncertainties that are not modeled here. The framework is designed to isolate and propagate registration uncertainty; we will revise the Discussion to clarify that the reported pDVH coverage reflects only this component and that total anatomical variability may be larger, with a note on potential future extensions. revision: yes

  2. Referee: [Results] Results: The reported Pearson correlations (0.63 for CTVt, 0.66 for bladder) and pDVH coverage of 96.3 +/- 3.9% are derived from an internal cohort of only nine patients, with no comparison to a non-uncertainty baseline dose accumulation method and no external validation cohort. This leaves the evidence for calibration and improved interpretation preliminary and limits assessment of whether the uncertainty estimates add value beyond standard practice.

    Authors: The study is indeed limited to a retrospective internal cohort of nine patients, with results presented as a proof-of-concept. No external validation or baseline comparison was performed. We will add an explicit limitations paragraph acknowledging the preliminary nature of the calibration metrics and the need for larger multi-center validation to assess added clinical value. revision: partial

standing simulated objections not resolved
  • External validation on an independent cohort and direct comparison against a non-uncertainty baseline accumulation method cannot be provided without new data collection and experiments outside the current retrospective study.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical correlations and coverage metrics are independent of fitted inputs

full rationale

The manuscript introduces IMPACT-DoseAcc as a framework that propagates voxel-wise DIR uncertainty (from Bayesian or ensemble segmentation models) through dose warping to produce pDVHs and reports empirical outcomes: Pearson correlations of 0.63 (CTVt) and 0.66 (bladder) between ensemble uncertainty and surface distance, plus 96.3 +/- 3.9% pDVH coverage. These quantities are measured directly from the nine-patient CBCT dataset and validated contours; no equations define the uncertainty maps or coverage statistics in terms of themselves, and no self-citation chain supplies a uniqueness theorem or ansatz that forces the reported calibration. The derivation chain therefore remains self-contained against external benchmarks.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The framework rests on the domain assumption that DIR uncertainty dominates over other sources and can be adequately captured by segmentation-derived probabilistic or ensemble maps; no free parameters or invented entities are named in the abstract.

assumptions (1)
  • domain assumption DIR uncertainty can be quantified using either a single Bayesian segmentation model or an ensemble of segmentation models
    This premise underpins both tested strategies and the subsequent propagation to dose metrics.

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Cite this review

Pith. "Pith review of An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer." pith.science (2026). https://pith.science/paper/PUU5FGZQ

@misc{pith2026260611012,
  author       = {Pith},
  title        = {Pith review of: An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PUU5FGZQ}},
  note         = {Machine review of arXiv:2606.11012}
}
read the original abstract

Background and purpose: oART enables daily plan adaptation to interfraction anatomical variations, but cumulative dose estimation remains limited by DIR, segmentation, and anatomical uncertainties. We introduce IMPACT-DoseAcc, an uncertainty-aware dose accumulation framework, within IMPACT for semantic feature-driven image analysis. The framework is modality- and disease-agnostic and is applied to CBCT-guided oART for cervical cancer (LACC). Material and Methods: Nine LACC patients were retrospectively analyzed using daily CBCT-derived virtual CTs for dose recalculation. IMPACT-DoseAcc focuses on uncertainty from DIR, without modeling vCT-generation uncertainty. Two DIR uncertainty strategies were tested within IMPACT-Reg: a Bayesian segmentation-guided approach using one probabilistic model to quantify anatomical uncertainty, and an ensemble of segmentation models targeting structures to capture epistemic variability. Voxel-wise uncertainty maps were propagated through dose warping and accumulation to generate probabilistic dose-volume histograms. Ensemble uncertainty was quantified from voxel-wise standard deviation across deformation fields, and geometric error was assessed using surface distance between warped and validated contours. Anatomical-variability weighting refined aggregation. Results: Ensemble DIR uncertainty correlated with geometric error, with Pearson coefficients of 0.63 for CTVt and 0.66 for bladder. For CTVt, pDVHs achieved 96.3 +/- 3.9% coverage, showing calibration of propagated uncertainty. Weighting stabilized estimates across fractions and organs. Conclusions: IMPACT-DoseAcc propagates registration-driven uncertainty to cumulative dose metrics, improving interpretation of accumulated dose under anatomical variations. Its 3DSlicer integration supports reproducible, uncertainty-informed ART workflows.

Figures

Figures reproduced from arXiv: 2606.11012 by the authors.

Figure 1
Figure 1. Study workflow: (1) Daily CBCT images were converted into vCTs using ANACONDA DIR with the pCT, (2) the daily dose was then [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Roadmap of the investigation pipeline and evaluated uncertainty configurations. Case 1 estimates DIR uncertainty using an ensemble of [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Boxplots of Dice similarity coefficients (DSC) between warped segmentations and clinician-validated ground truth contours for the CTVt, bladder, and rectum, across all patients. Results are shown for each individual model and for the three Cases: Case 1 (ensemble-based DIR), Case 2a (Bayesian segmentation–guided DIR with cervix and peritoneal cavity labels), and Case 2b (Bayesian DIR without peritoneal cavity label)… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Scatterplots illustrate the relationship between contour registration uncertainty (x-axis) and average surface distance (ASD) to ground [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Comparison of accumulated DVHs for Case 1 and Case 2 (quadratic) with respect to the ground truth (GT) for two representative [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Comparison of two uncertainty aggregation strategies for Case 1 (ensemble-based): quadratic summation versus Case 1 weighted [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Illustration of how probabilistic DVHs (Case 1) support clinical decision-making. For two representative patients, accumulated DVHs [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Temporal evolution of Case 1 dose propagation and uncertainty for Patient 4. Columns 1 to 5 show selected treatment fractions (vCT1, [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

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Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.