Deep learning-based CT slice synthesis improves radiomic feature reproducibility and discriminative performance in lung nodule assessment.

Authors

Hujun Yang, Zhengping Zhang, Lei Tian, Pei Dang, Long Wan, Zhiyuan Zhou, Jingjie Li, Lei Cao, Xiaoyan Yang, Juan Chen

Published in

Insights into imaging. Volume 17. Issue 1. Jun 19, 2026. Epub Jun 19, 2026.

Abstract

To investigate the effect of CT slice thickness on radiomic features (RFs) in terms of reproducibility and discriminative power, and to assess whether a deep learning-based CT slice synthesis (DLS) algorithm can mitigate the adverse effects associated with thick-slice CT.
This retrospective multicenter study included 506 patients with lung nodules (245 benign, 261 malignant) from two independent cohorts, which were divided into a training set, internal validation set (IVS), and external validation set (EVS). Chest CT reconstructed at 1-mm and 5-mm slice thicknesses was analyzed. A DLS algorithm was applied to convert 5-mm CT into synthetic 1-mm CT. RFs were extracted from all CT types to construct radiomics models. Reproducibility was assessed using the concordance correlation coefficient (CCC) and compared with the Wilcoxon signed-rank test. Discriminative power was evaluated by the area under the receiver operating characteristic curve (AUC) and compared with DeLong's test.
The CCCs of DLS 1-mm CT were 0.48 ± 0.37 and 0.49 ± 0.37 in Cohort 1 and Cohort 2, respectively, significantly higher than real 5-mm CT (all p < 0.001). Most RFs from 5-mm CT lacked reproducibility (CCC ≥ 0.85; 0.9% in both Cohort 1 and Cohort 2), whereas DLS 1-mm CT showed marked improvement (Cohort 1, 27.6%; Cohort 2, 26.9%). The discriminative power of RFs from DLS 1-mm CT was superior to that of 5-mm CT and non-inferior to real 1-mm CT, both in model construction and evaluation.
CT slice thickness substantially influences the reproducibility and discriminative power of RFs, whereas the DLS algorithm effectively mitigates the limitations associated with thick-slice CT.
Deep learning-based CT slice synthesis significantly reduces the slice thickness-related variability in radiomics feature reproducibility and discriminative power, providing a promising methodological approach to improve radiomics standardization and support its clinical translation.
CT slice thickness variability substantially impairs radiomic feature reproducibility and discriminative performance, posing a major barrier to standardized radiomics analysis. Across two independent cohorts, deep learning-based slice synthesis mitigated the adverse effects of thick-slice CT on radiomic feature reproducibility and cross-thickness discriminative performance.

PMID:
42319610
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

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