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TRUVACE RECORD VERSION
record: TRV-2026-2043
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T22:02:35.695303Z
status: published
lens: trace
sector: health
headline: Impact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules
dek: Objective Ensuring CT feature reproducibility across imaging protocols and reconstruction algorithms is essential for the generalizability of radiomics applications. This study aimed to evaluate the reproducibility of CT radiomic features in pulmonary nodules using deep learning image reconstruction (DLIR) compared with conventional reconstruction techniques under different radiation doses. Materials and methods We included 94 patients between March and June 2023. Standard- and low-dose CT images were scanned to…
gain_title: Deep learning image reconstruction improved the reproducibility of radiomic features in pulmonary nodules, making it particularly suitable for low-dose CT protocols.
problem_title: Feature robustness tends to decrease at higher deep learning reconstruction strengths, and nodule characteristics significantly influence variability.
trace_subject: Deep learning image reconstruction and CT radiomic feature reproducibility in pulmonary nodules
gain_reading: Deep learning image reconstruction improved the reproducibility of radiomic features in pulmonary nodules, making it particularly suitable for low-dose CT protocols.
gain_evidence: DLIR improves the radiomic feature reproducibility in pulmonary nodules, making it particularly suitable for low-dose CT protocols.
problem_reading: Feature robustness tends to decrease at higher deep learning reconstruction strengths, and nodule characteristics significantly influence variability.
problem_evidence: the robustness tends to decrease with higher DLIR strengths | Nodule characteristics significantly influence variability.
quick_read: Researchers scanned 94 patients with pulmonary nodules between March and June 2023 at standard and low radiation doses. They compared three deep learning reconstruction strengths against filtered back projection and ASIR-V-50%, and assessed 2264 extracted radiomic features for agreement and variability.

The findings suggest reconstruction choice can affect how reliable quantitative nodule assessment is in clinical radiology, especially when doses are reduced. The best setting depended on the comparison, and stronger noise suppression reduced robustness, so protocol tuning matters. The excerpt does not show how this affects patient outcomes.
limitation: The inter-dose reproducibility difference between DLIR-H, ASIR-V-50%, and FBP was not statistically significant, and robustness declines at higher DLIR strengths.
tag: Dual reading
key_points: Mean inter-dose CCC was 0.934 for DLIR-H versus 0.922 for both ASIR-V-50% and FBP, a difference with p = 0.055. | Near-perfect inter-dose agreement covered 29.8% of features with DLIR-H, 27.9% with ASIR-V-50%, and 20.2% with FBP. | In low-dose CT, DLIR-L had the highest inter-reconstruction reproducibility against ASIR-V-50% at 0.950, with 39.4% of features near-perfect. | Overall CV across reconstructions was 9.87%, with 74.0% of features highly stable, and 21 robust features were identified.
rundown: The study evaluated reproducibility using the concordance correlation coefficient for inter-dose and inter-reconstruction comparisons and the coefficient of variation for feature variability. Deep learning reconstruction was tested at low, medium, and high strengths, which represent progressive levels of noise suppression.

Different strengths excelled in different tests: DLIR-H showed high inter-dose reproducibility, while DLIR-L achieved the highest inter-reconstruction reproducibility. The authors note robustness tends to fall as strength rises.

Nodule traits also mattered. Part-solid nodules showed the lowest variability at 7.17%, and variability decreased significantly with increasing nodule size. The authors say this supports protocol optimization and more reliable quantitative assessment.
sources:
- peer_reviewed | Insights into Imaging | https://doi.org/10.1186/s13244-026-02417-y | 2026-10-13
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