Evidence map›Paper›PMID 40597032›Full record

ArticleBMC cancer2025

Steps towards overcoming challenges in clinical practice at 1.5T MR-Linac for lung cancer adaptive radiotherapy.

Min Liu, Feng Yang, Bin Tang, Xin Xin, Xiongfei Liao, Mingzhe Liu, Yanhua Liu, Jie Li, Xianliang Wang, Lucia Clara Orlandini

Abstract read
In one paragraph

Article in BMC cancer, 2025. 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Min Liu *College of Computer Science and Cyber Security, Chengdu University of Technology, No.1, Section 3, Erxianqiao East Road, Chengdu, 610051, China.
Feng Yang *College of Computer Science and Cyber Security, Chengdu University of Technology, No.1, Section 3, Erxianqiao East Road, Chengdu, 610051, China.
Bin TangDepartment of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China.
Xin XinDepartment of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China.
Xiongfei LiaoDepartment of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China. fybre@163.com.
Mingzhe LiuCollege of Computer Science and Cyber Security, Chengdu University of Technology, No.1, Section 3, Erxianqiao East Road, Chengdu, 610051, China. liumz@cdut.edu.cn.
Yanhua LiuCollege of Computer Science and Cyber Security, Chengdu University of Technology, No.1, Section 3, Erxianqiao East Road, Chengdu, 610051, China.
Jie LiDepartment of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China.
Xianliang WangDepartment of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China.
Lucia Clara OrlandiniDepartment of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China.

Funding

Natural Science Foundation of Sichuan Province 2023NSFSC0711Sichuan Science and Technology Program 2023YFWZ004
6 · The paper itself

Abstract

backgroundThe MR-guided adaptive radiotherapy (MRgART) workflow at the 1.5 T Unity MR-Linac relies on synthetic CT (sCT) generated through bulk density assignment. Although sCT-based dose calculations are the standard approach, it is well known that their accuracy can be compromised in lung tumors due to the high dose gradients surrounding the targets. This study investigates clinical cases to determine whether these challenges affect all lung targets or if, for a subset partially located in high-density dose regions, the sCT calculations performed in the Unity clinical workflow are sufficiently accurate, supporting their routine clinical application.

methodsForty-eight lung cancer patients undergoing stereotactic body radiotherapy at Unity MR-Linac, were included in this study. Patients were stratified into two groups based on target position: [Formula: see text] group including targets attached to thoracic wall or mediastinum, and [Formula: see text] (group isolated pulmonary target), including targets entirely within the lung parenchyma and surrounded by air-filled lung tissue. The reference treatment plans ([Formula: see text]) were optimised on the simulation CT using inverse-planning intensity modulated radiation therapy (IMRT) with the Monaco treatment planning system to deliver 50 Gy in 5 fractions. [Formula: see text] included all contour information needed to generate the sCT through bulk electron density assignment, the standard procedure for Unity MR-Linac. To evaluate the dosimetric accuracy of sCT-based dose calculations, a second plan ([Formula: see text]) was created by recalculating [Formula: see text] on the sCT derived from the reference CT. The sCT was generated using the MRgART routine, which assigns mean ED values to all contoured structures. Dose-volume histograms were compared between [Formula: see text] and [Formula: see text] for targets and organs at risk (OARs), with additional evaluation of tumor control probability, and normal tissue complication probability. Dose distributions were further evaluated using global gamma analysis with 3%/3 mm and 2%/2 mm criteria.

resultsSignificant differences (p < 0.05) in key dosimetric parameters ([Formula: see text], [Formula: see text], [Formula: see text]) were observed between [Formula: see text] and [Formula: see text] in the [Formula: see text] group, with percentage differences reaching up to 3.49%. Conversely, in [Formula: see text], percentage differences were less than 1% and not statistically significant (p > 0.05). For OARs, no significant differences (p > 0.05) were observed in either group, except for the lungs minus the gross tumor volume (lungs-GTV), where percentage differences remained below 1.5%. Radiobiological modelling yielded consistent results, confirming the dosimetric findings. Gamma analysis showed consistent dose distributions, with a global pass rate above 95% for 3%/3 mm criteria and above 90% for 2%/2 mm criteria.

conclusionsThis study demonstrates that sCT-based dose calculations are feasible and reliable for pulmonary targets attached to the thoracic wall or mediastinum, supporting their routine integration into MRgART workflows on the Unity MR-Linac. However, for isolated pulmonary targets, deviations should be considered when implementing sCT-based planning in clinical practice.

Indexed as

Lung NeoplasmsMagnetic Resonance ImagingRadiosurgeryRadiotherapy, Image-GuidedRadiotherapy Planning, Computer-AssistedAgedAged, 80 and overFemaleHumansMaleMiddle AgedRadiotherapy DosageRadiotherapy, Intensity-ModulatedTomography, X-Ray ComputedAdaptive radiotherapyLungsMR-linacRadiotherapySynthetic computed tomography

Identifiers

PMID40597032
PMCPMC12211906

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