Evidence map›Paper›PMID 39626092›Full record

ArticleJournal of applied clinical medical physics2025

Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.

Justin Visak, Chien-Yi Liao, Xinran Zhong, Biling Wang, Sean Domal, Hui-Ju Wang, Austen Maniscalco, Arnold Pompos, Dan Nyguen, David Parsons and 6 more

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

16 authors.

Justin VisakDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Chien-Yi LiaoDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Xinran ZhongDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Biling WangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Sean DomalDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Hui-Ju WangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Austen ManiscalcoDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Arnold PomposDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Dan NyguenDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
David ParsonsDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Andrew GodleyDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Weiguo LuDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Steve JiangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Dominic MoonDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
David SherDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Mu-Han LinDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Funding

Human-like automated radiotherapy treatment planning via imitation learningR01CA254377 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI JIA, XUN, JIANG, STEVE BIN · 2021 to 2025
$2.9M
Artificial Intelligence-Based Quality Assurance for Online Adaptive RadiotherapyR01CA258987 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Steve Bin Jiang, Dan Nguyen · 2022 to 2026
$2.8M
NCI NIH HHS R01 CA254377NCI NIH HHS R01 CA258987NIH HHS R01CA254377NIH HHS R01CA258987Varian Medical Systems
6 · The paper itself

Abstract

purposeOptimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment planning system (TPS) version 2.0 emulator includes novel methods to drive the planning process including the revised intelligent optimization engine algorithm (IOE2). HNC is among the most challenging and complex sites and heavily depends on planner skill and experience to successfully generate a reference plan. Therefore, we evaluate the new TPS performance via conventionally accepted planning strategies with/without artificial intelligence (AI) and knowledge-based planning (KBP).

methodsOur institution has a pre-clinical release of the Varian Ethos2.0 TPS emulator which includes several changes that may affect current planning strategies. Twenty definitive and post-operative HNC patients were retrospectively selected with a two or three-level simultaneous integrated boost (SIB) dosing scheme. Patients were replanned in the emulator using population-based, KBP-guided with/without human intervention and AI-guided planning goals. These planning strategies were compared both dosimetrically and for plan deliverability.

resultsAll strategies generally demonstrated acceptable plan quality with KBP- and AI-guided goals offering enhanced dosimetric sparing in organs-at-risk (OAR). The average contralateral parotid gland mean dose was 20.0 ± 6.1 Gy (p < 0.001) for population-based and 15.0 ± 6.1 Gy (p = n.s.) for KBP-with human intervention versus 15.1 ± 7.4 Gy for clinical plans. Target coverage, minimum dose, and plan hotspot were acceptable in all cases. KBP-enabled strategy demonstrated higher modulation and faster optimization time than both population-based and AI-guided strategies.

conclusionSimply entering population, automatic KBP-enabled or AI-generated planning goals into the new Ethos2.0 TPS produced dosimetrically compliant plans, with AI-guided goals demonstrating the most OAR sparing. Several of these approaches are easy to translate to other treatment sites and will help lower the barrier to entry for x-ray-based online-ART.

Indexed as

AlgorithmsArtificial IntelligenceHead and Neck NeoplasmsOrgans at RiskPrecision MedicineRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedHumansRadiotherapy DosageRetrospective Studiesadaptive radiotherapyhead & neckmachine‐learning

Identifiers

PMID39626092
PMCPMC11905247

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.