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DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy — arXiv:2608.10173

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10173
License (as archived): CC0-1.0
Archived: 2026-10-19T06:30:10.755Z
SHA-256 of the archived text: 4ad2c311e99741cc8cfbbf8c7dbc76ddf036500de91d1c0611b90d33104ff139
Stable URL of this page: https://metraipolis.com/documento/dc_aaa2ab6ac6ee

How to cite

DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy — arXiv:2608.10173 — arXiv (en), https://arxiv.org/abs/2608.10173 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aaa2ab6ac6ee (Metraipolis archive dc_aaa2ab6ac6ee, sha256 4ad2c311e99741cc8cfbbf8c7dbc76ddf036500de91d1c0611b90d33104ff139)

BibTeX

@misc{dcaaa2ab6ac6ee,
  title = {DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy — arXiv:2608.10173},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10173},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aaa2ab6ac6ee; sha256 4ad2c311e99741cc8cfbbf8c7dbc76ddf036500de91d1c0611b90d33104ff139},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10173. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Most radiotherapy dose-prediction models use only CT images and anatomical structures, although intensity-modulated proton therapy (IMPT) dose also depends strongly on beam geometry and available clinical datasets are often small. We present DoseBridge, a denoising diffusion bridge model that uses the patient CT as a structured bridge endpoint and encodes plan-specific beam geometry in a spatially aligned beam mask. Multiscale fusion combines CT, target, organ-at-risk, and beam-mask representations with 1.95% additional parameters. DoseBridge was retrospectively evaluated on single-institution CT images and treatment plans from 52 patients with advanced-stage lung cancer treated with 60 Gy in 30 fractions; 42 cases were used for training and 10 for testing. Performance was assessed using image-similarity, dose-volume, and Lyman-Kutcher-Burman normal-tissue complication probability (NTCP) metrics and compared with two deep-learning models. On the test cohort, DoseBridge achieved a mean absolute error of 4.170 Gy, peak signal-to-noise ratio of 23.06 dB, and structural similarity index of 0.798, outperforming both comparison models on these metrics. Clinical target volume D95 differed from the reference dose by 0.62 +/- 1.6 Gy; signed organ-at-risk mean-dose differences ranged from -0.32 to 0.24 Gy, and NTCP differences were -0.40 +/- 2.2 and 0.52 +/- 3.4 percentage points for acute esophagitis and radiation pneumonitis, respectively. Changing only the beam mask redirected predicted low-dose entrance regions while preserving the high-dose target region. To our knowledge, DoseBridge is the first denoising diffusion bridge model for radiotherapy dose prediction. These results support its feasibility as a beam-aware planning prior for lung IMPT, pending evaluation in larger external cohorts.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-12 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.10173 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T06:25:27.233Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).