Evidence map›Paper›PMID 40265325›Full record

ArticleJournal of applied clinical medical physics2025

Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.

Conor Sinclair Smith, Isabelle Gagne, Karl Otto, Carter Kolbeck, Joshua Giambattista, Abraham Alexander, Sonja Murchison, Andrew Pritchard, Erika Chin

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Conor Sinclair SmithDepartment of Physics and Astronomy, University of Victoria, Victoria, British Columbia, Canada.
Isabelle GagneDepartment of Medical Physics, BC Cancer Victoria, Victoria, British Columbia, Canada.
Karl OttoRadformation Inc, New York, New York, USA.
Carter KolbeckRadformation Inc, New York, New York, USA.
Joshua GiambattistaRadformation Inc, New York, New York, USA.
Abraham AlexanderDepartment of Radiation Oncology, BC Cancer Victoria, Victoria, British Columbia, Canada.
Sonja MurchisonDepartment of Radiation Oncology, BC Cancer Victoria, Victoria, British Columbia, Canada.
Andrew PritchardDepartment of Radiation Oncology, BC Cancer Victoria, Victoria, British Columbia, Canada.
Erika ChinDepartment of Computer Science, Mathematics, Physics, and Statistics, University of British Columbia-Okanagan, Kelowna, British Columbia, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variability, inconsistent patient adherence still results in OAR variability. At many centers without online adaptive machines, radiation therapists use decision trees (DTs) to visually assess patient setup, yet their application varies. To evaluate our center's DTs, we employed deep learning-generated cone-beam computed tomography (CBCT) contours to estimate daily doses to the rectum and bladder, comparing these with planned dose-volume metrics to guide future personalized DT development. Two hundred pretreatment CBCT scans from 40 prostate SABR patients (each receiving 40 Gy in five fractions) were auto-contoured retrospectively, and daily rectum and bladder doses were estimated by overlaying the planned dose on the CBCT using online rigid registration data. Dose-volume metrics were classified as "no", "minor", or "major" violations based on meeting preferred or mandatory goals. Twenty-seven percent of fractions exhibited at least one major bladder violation (with an additional 34% minor), while 14% of fractions had a major rectum violation (10% minor). Across treatments, five patients had recurring bladder V37 Gy major violations and two had rectum V36 Gy major violations. Bowel and bladder preparation significantly influenced OAR position and volume, leading to unmet mandatory goals. Our retrospective analysis underscores the significant impact of patient preparation on dosimetric outcomes. Our findings highlight that DTs based solely on visual assessment miss dose metric violations due to human error; only 23 of 59 under-filled bladder fractions were flagged. In addition to the insensitivity of visual assessments, variability in DT application further compromises patient setup evaluation. These analyses confirm that reliance on visual inspection alone can overlook deviations, emphasizing the need for automated tools to ensure adherence to dosimetric constraints in prostate SABR.

Indexed as

Cone-Beam Computed TomographyDeep LearningOrgans at RiskProstatic NeoplasmsRadiosurgeryRadiotherapy Planning, Computer-AssistedHumansMaleRadiotherapy DosageRadiotherapy, Intensity-ModulatedRectumRetrospective StudiesUrinary Bladderautocontouringautosegmentationinter‐fraction variabilityorgans‐at‐riskprostate SABR

Identifiers

PMID40265325
PMCPMC12148783

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.