Evidence map›Paper›PMID 36474297›Full record

ArticleRadiation oncology (London, England)2022

Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors.

Antonella Fogliata, Sara Parabicoli, Lucia Paganini, Giacomo Reggiori, Francesca Lobefalo, Luca Cozzi, Ciro Franzese, Davide Franceschini, Ruggero Spoto, Marta Scorsetti

Open access · goldAbstract read
In one paragraph

Article in Radiation oncology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
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

15 citing papers in PubMed, 18 citations in OpenAlex.

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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

10 authors at 1 institution in 1 country.

Antonella FogliataRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy. antonella.fogliata@humanitas.it.
Sara ParabicoliRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Lucia PaganiniRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Giacomo ReggioriRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Francesca LobefaloRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Luca CozziRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Ciro FranzeseRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Davide FranceschiniRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Ruggero SpotoRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
Marta ScorsettiRadiotherapy and Radiosurgery Department, Humanitas Research Hospital IRCCS, Milan-Rozzano, Italy.
IRCCS Humanitas Research Hospital · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS).

methodsCT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization.

resultsThe PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation.

conclusionsThe RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.

Indexed as

Avoidance sectorBreastDVH estimation modelKnowledge-based planningRapidPlanVMAT

Identifiers

PMID36474297
PMCPMC9724419
OpenAlexW4311584126

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.