ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
Stable or not? unraveling the reliability of radiomic features in 4d-computed tomography in early-stage non-small cell lung cancer.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 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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Abstract
aimFour-dimensional computed tomography (4D-CT) is the gold standard for radiotherapy planning in non-small cell lung cancer (NSCLC), yet its use in radiomics remains underexplored. This study proposes a reproducible, scalable methodology for assessing radiomic feature (RF) stability in 4D-CT and evaluates whether image filtering identifies additional stable RFs compared to unfiltered images.
methodsEarly-stage NSCLC patients treated with SBRT with 4D-CT were included. Gross tumor volumes (GTVs) were re-segmented on all available phases. RFs were extracted using PyRadiomics. Features with near-zero variance in > 85% of patients were excluded. RF stability was evaluated using two complementary approaches: (i) coefficient of variation (COV), quantifying the magnitude of inter-phase variability, and (ii) repeated-measures modeling, assessing the presence of a statistically significant association between RF values and respiratory phase. RFs with COV < 5% and 5-10% were considered highly stable and stable, respectively. Repeated-measures analyses were performed separately for expiratory (0-40%) and inspiratory (50-90%) phases.
resultsSeventy patients met the inclusion criteria. 1892 RFs were analyzable. Based on COV, about 21% (397/1892) of RFs were highly stable, and 18% (338/1892) were stable, while the remaining showed intermediate or high variability. The largest proportion of highly stable RFs derived from lbp-3D (25%) and log-sigma (12%) filtered images. Repeated measures analysis showed that only a limited subset of RFs had a statistically-significant dependence on respiratory phase, with 1747 and 1744 RFs remaining time-independent across expiratory and inspiratory phases, respectively.
conclusionRadiomic features extracted from 4D-CT images in early-stage NSCLC patients show heterogeneous stability across respiratory phases. Radiomic features extracted from 4D-CT images in early-stage NSCLC exhibit heterogeneous quantitative variability across respiratory phases. However, only a minority of features show statistically significant time dependence. The study provides a reproducible methodological framework to identify stable radiomic features from 4D-CT, enabling their more reliable use in lung cancer radiomic studies.
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