ArticleJournal of applied clinical medical physics2026
PCA-based respiratory motion modeling for individualized PTV margin optimization in NSCLC radiotherapy.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRespiratory motion remains a major challenge in radiotherapy for non-small cell lung cancer (NSCLC), often requiring a balance between target coverage and normal tissue sparing.
introductionThis study aimed to develop and validate a clinically implementable, individualized PTV margin strategy by integrating principal component analysis (PCA) with key clinical predictors.
methodsA cohort of 61 NSCLC patients (Stage III) underwent 4DCT simulation. 4DCT datasets were used both for motion modeling and as a reference planning approach (ITV-based). The proposed method was compared against conventional 3DCT and 4DCT-based plans. PCA was applied to the deformable vector fields to extract dominant motion modes and quantify directional amplitudes (A
resultsTumor location (p = 0.007) and T stage (p = 0.04) were identified as significant predictors of S-I motion amplitude. Middle/lower lobe tumors and early T1-T2 stages exhibited higher mobility (≥ 15 mm). By applying adaptive weighting factors (
conclusionsIntegrating PCA-derived motion with clinical weighting provides a practical framework for individualized margin design, improving lung sparing while maintaining target coverage.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.