Evidence map›Paper›PMID 39917897›Full record

ArticleJournal of applied clinical medical physics2025

The implementation of knowledge-based planning with partial OAR contours for prostate radiotherapy.

Ositomiwa O Osipitan, David Wiant, Han Liu

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Ositomiwa O OsipitanDepartment of Radiation Oncology, Cone Health Cancer Center, Greensboro, North Carolina, USA.
David WiantDepartment of Radiation Oncology, Cone Health Cancer Center, Greensboro, North Carolina, USA.
Han LiuDepartment of Radiation Oncology, Cone Health Cancer Center, Greensboro, North Carolina, USA.ORCID https://orcid.org/0000-0001-8939-7080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIntra- and inter-observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or non-overlapping portions of the organs-at-risk (OARs) with the planning target volume (PTV).

methodsThe partial contours systematically eliminate overlapping regions. The partial contours were evaluated against fully contoured OARs. We incorporated advanced tools like knowledge-based planning (KBP) to create treatment plans and artificial intelligence (AI) to create auto-segmented contours. We developed two models, Rapid Plan (RP) and Rapid Plan partial uninvolved (RP_Part_Un), using 70 previous clinically approved volumetric arc therapy (VMAT) plans each prescribed with 70 Gy/28 fractions. From these models, we created three plans, RP, RP_Part_Un, and MIM AI_Part_Un. In this retrospective study, 60 prostate patients were analyzed using the three plans. For determining OAR sparing, D

resultsWhen comparing the DSC and OI for full contours to the partial contours, in both groups, all comparisons were significantly increased for both organs. This indicated the partial contours had a higher degree of concordance. In patients with SpaceOAR, RP_Part_Un plans exhibited significantly reduced bladder D

conclusionsPartial contours, defined at a specified distance from the PTV, yielded dosimetry comparable to fully contoured plans, highlighting their potential efficacy in treatment planning.

Indexed as

Artificial IntelligenceKnowledge BasesOrgans at RiskProstatic NeoplasmsRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAlgorithmsHumansMaleRadiotherapy DosageRetrospective Studiesauto‐segmentationknowledge‐based planningpartial contours

Identifiers

PMID39917897
PMCPMC11969073

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