Evidence map›Paper›PMID 41665480›Full record

ArticleJournal of applied clinical medical physics2026

Quantifying the impact of tumor size and motion on 4DCT-4DCBCT image registration accuracy using machine learning and statistical analysis.

Qiaoyan Jing, Shuyu Lin, Binyun Huang, Tingjun Luo, Xianya Li, Weiming Zhang, Shaohan Sun

Abstract read
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. 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

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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

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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

7 authors.

Qiaoyan JingDepartment of Radiation Oncology, Wuming Hospital of Guangxi Medical University, Nanning, People's Republic of China.
Shuyu LinDepartment of Radiation Oncology, Wuming Hospital of Guangxi Medical University, Nanning, People's Republic of China.
Binyun HuangDepartment of Radiation Oncology, Wuming Hospital of Guangxi Medical University, Nanning, People's Republic of China.
Tingjun LuoDepartment of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, People's Republic of China.
Xianya LiDepartment of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, People's Republic of China.
Weiming ZhangDepartment of Radiation Oncology, Wuming Hospital of Guangxi Medical University, Nanning, People's Republic of China.
Shaohan SunDepartment of Radiation Oncology, Wuming Hospital of Guangxi Medical University, Nanning, People's Republic of China.ORCID https://orcid.org/0009-0009-2830-0279

Funding

Guangxi Medical and Health Appropriate Technology Development S2023097Promotion Application Project and the Guangxi Medical and Health Self Funded Project Z20201311Youth Science Foundation of Guangxi Medical University GXMUYSF202450
6 · The paper itself

Abstract

purposeThis study systematically quantifies the effects of five variables-respiratory cycle, tumor size, and motion amplitudes in the superior-inferior (SI), anterior-posterior (AP), and left-right (LR) directions-on the registration accuracy between four-dimensional computed tomography (4D CT) and four-dimensional cone-beam CT (4D CBCT) images, thereby providing a theoretical basis for optimizing registration strategies in image-guided radiotherapy (IGRT). MATERIALS AND

methodsA CIRS 008A dynamic phantom fitted with 1 and 3 cm tumor inserts was utilized to simulate various respiratory motion scenarios by manipulating respiratory cycles (T = 0, 2, 4, and 8 s) and three-dimensional motion amplitudes (SI, AP, and LR ranging from 0 to 15 mm, with AP and LR limited to 0, 1, and 5 mm). Corresponding four-dimensional images were acquired using a GE Discovery RT CT simulator and a Varian VitalBeam linear accelerator. Rigid registration between the 4D CT and 4D CBCT images was subsequently performed using the Varian imaging system, with registration quality quantitatively assessed via the Dice similarity coefficient (DSC). Furthermore, a Random Forest regression model was employed to determine the relative importance of each factor, and multifactor analysis of variance (ANOVA) was conducted to verify statistical significance.

resultsThe Random Forest analysis indicated that, for the overall registration average intensity projection, the factors were ranked in order of importance as follows: tumor size (0.509), SI motion (0.315), respiratory cycle (0.094), LR motion (0.055), and AP motion (0.028). In the maximum intensity projection, tumor size (0.722) was found to have a particularly significant impact. The multifactor ANOVA further supported these findings, demonstrating that tumor size (p < 0.001) and SI motion (p < 0.001) have a highly significant influence on registration quality, whereas the respiratory cycle and AP/LR motions did not reach statistical significance (p > 0.05). Notably, when the tumor size was small (1 cm) and accompanied by considerable SI motion (>10 mm), registration accuracy markedly deteriorated, with the greatest variability observed under these conditions.

conclusionThis study demonstrated that the registration quality between 4D CT and 4D CBCT images was significantly influenced by both tumor size and the amplitude of motion in the SI direction.

Indexed as

Cone-Beam Computed TomographyFour-Dimensional Computed TomographyImage Processing, Computer-AssistedLung NeoplasmsMachine LearningPhantoms, ImagingRadiotherapy, Image-GuidedRadiotherapy Planning, Computer-AssistedAlgorithmsHumansMovementRadiotherapy DosageRadiotherapy, Intensity-ModulatedRespirationTumor Burden4D CBCT4D CTDice similarity coefficientimage registrationrandom forest analysis

Identifiers

PMID41665480
PMCPMC12888845

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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.