Evidence map›Paper›PMID 42253171›Full record

ArticleJournal of applied clinical medical physics2026

Dosimetric comparison of FF and FFF modes under non-ideal bolus-fitting conditions for post-mastectomy chest wall irradiations.

Long-Gang Gui, Yin Poo

Abstract readComparative Study
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Long-Gang GuiSchool of Electronic Science and Engineering, Nanjing University, Nanjing, China.
Yin PooSchool of Electronic Science and Engineering, Nanjing University, Nanjing, China.

Funding

National Natural Science Foundation of China 62571232
6 · The paper itself

Abstract

backgroundIn post-mastectomy radiotherapy, tissue-equivalent bolus is routinely used to ensure adequate skin dose. However, achieving perfect contact on curved chest walls is challenging, often resulting in non-uniform air gaps. With the increasing adoption of Flattening Filter-Free (FFF) beams, it is crucial to understand how their distinct physical properties-specifically a softer energy spectrum-respond to these low-density gaps compared to Flattening Filter (FF) beams. Additionally, the accuracy of clinical dose calculation algorithms in these electronic disequilibrium regions remains a subject of concern. PURPOSE: This study aimed to quantify the impact of non-uniform air gaps on skin dose distribution, evaluate the accuracy of Collapsed Cone Convolution (CCC) versus GPU-accelerated Monte Carlo (MC) algorithms in complex geometries, and determine the specific dosimetric thresholds for selecting FF or FFF beam modes.

methodsA cylindrical curved phantom (25 cm diameter) was constructed to simulate chest wall anatomy. Four interchangeable modules were designed to create controlled fit conditions: perfect fit, gradient gap (0-6 mm), and uniform gaps (3 and 6 mm). Skin dose measurements were performed using EBT3 films and MOSFET detectors. Dose calculations were conducted in the United Imaging uRT-TPS using 6 MV FF and FFF beams at gantry angles of 0°, 30°, and 60°.

resultsThe presence of gradient gaps substantially increased skin dose inhomogeneity, yielding a heterogeneity index of 0.28 for the FFF beam. The MC algorithm demonstrated high accuracy, agreeing with film measurements within 0.5%. In contrast, the CCC algorithm exhibited systematic underestimation in gap regions, with a mean error of -6.8% and a maximum deviation of -16.5%. Physically, the FFF beam (mean energy 1.23 MeV) was found to be more sensitive to air gaps than the FF beam (1.93 MeV) due to reduced lateral electron scatter. The FFF/FF dose ratio decreased linearly with increasing gap thickness (slope: -2.1%/mm). A clinical threshold was identified at approximately 2.3 mm; beyond this gap thickness, the FF beam exhibited superior dosimetric robustness. Furthermore, oblique incidence at 60° reduced the mean skin dose by approximately 9% compared to perpendicular incidence.

conclusionNon-uniform air gaps significantly degrade skin dose uniformity, with FFF beams being more susceptible to this effect than FF beams. The CCC algorithm shows limitations in these complex interface geometries, whereas the MC algorithm provides a reliable verification tool. Clinically, for regions where air gaps exceed 2.3 mm, the use of FF mode or optimization of bolus fitting is recommended to ensure adequate target coverage.

Indexed as

AlgorithmsBreast NeoplasmsMastectomyMonte Carlo MethodPhantoms, ImagingRadiotherapy Planning, Computer-AssistedThoracic WallFemaleHumansOrgans at RiskParticle AcceleratorsRadiometryRadiotherapy DosageRadiotherapy, Intensity-Modulatedbolus fitchest wall radiotherapycurved phantomdose heterogeneity indexMonte Carlo algorithm

Identifiers

PMID42253171
PMCPMC13244261

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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.