{
  "abstract": "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.\n\nMaterials and methods: We included 94 patients between March and June 2023. Standard- and low-dose CT images were scanned to assess the impact of dose reduction on feature reproducibility. DLIR strengths at low (DLIR-L), medium (DLIR-M), and high (DLIR-H), representing progressive levels of noise suppression, were compared against filtered back projection (FBP) and adaptive statistical iterative reconstruction-V at 50% strength (ASIR-V-50%) reconstructions. 2264 features were extracted from pulmonary nodules. The concordance correlation coefficient (CCC) and the coefficient of variation (CV) were used to evaluate inter-dose reproducibility, inter-reconstruction reproducibility, and feature variability.\n\nResults: The mean CCC for inter-dose reproducibility using DLIR-H, ASIR-V-50%, and FBP was 0.934 ± 0.104, 0.922 ± 0.128, and 0.922 ± 0.113, respectively ( p = 0.055), with 29.8%, 27.9%, and 20.2% of features showing near-perfect agreement. In low-dose CT, DLIR-L achieved the highest inter-reconstruction reproducibility against ASIR-V-50% (0.950 ± 0.094), with 39.4% of features showing near-perfect agreement. The overall CV across reconstructions was 9.87 ± 20.32%, with 74.0% of features highly stable. Part-solid nodules showed the lowest variability (7.17 ± 13.48%, p < 0.001). Reproducibility improved significantly with increasing nodule size ( p < 0.001).\n\nConclusion: DLIR improves the radiomic feature reproducibility in pulmonary nodules, making it particularly suitable for low-dose CT protocols. However, the robustness tends to decrease with higher DLIR strengths. Nodule characteristics significantly influence variability.\n\nKey Points: Question The impact of DLIR on radiomic feature reproducibility across radiation doses and reconstruction methods in pulmonary nodules remains unknown.\n\nFindings DLIR-H demonstrated high inter-dose reproducibility, while DLIR-L achieved the highest inter-reconstruction reproducibility; feature variability decreased significantly with increasing nodule size, with 21 robust features identified.\n\nCritical relevance statement This study critically assesses reconstruction-related instability in CT radiomics and identifies DLIR settings and nodule characteristics that influence feature robustness, supporting protocol optimization and more reliable quantitative pulmonary nodule assessment in clinical radiology.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Huiyuan Zhu"
    },
    {
      "name": "Tingle Wei"
    },
    {
      "name": "Luna Wang"
    },
    {
      "name": "Lin Zhu"
    },
    {
      "name": "Qunhui Chen"
    },
    {
      "name": "Guangyu Tao"
    },
    {
      "name": "Linlin Sun"
    },
    {
      "name": "Xiwen Sun"
    },
    {
      "name": "Zike Huang"
    },
    {
      "name": "Hong Yu",
      "orcid": "0000-0002-1094-4334"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9dbq8ckgvzz53.bib",
  "cited_by": 0,
  "doi": "10.1186/s13244-026-02417-y",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661886/?tool=EBI",
  "id": "9dbq8ckgvzz53",
  "issns": [
    "1869-4101"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13661886",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7221707086",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661886/pdf/?tool=EBI",
  "pmcid": "PMC13661886",
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/9dbq8ckgvzz53.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:29Z",
      "native_id": "PMC:PMC13661886",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13661886"
    },
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "10.1186/s13244-026-02417-y",
      "source": "crossref",
      "url": "https://link.springer.com/10.1186/s13244-026-02417-y"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Impact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules",
  "title_text": "Impact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/9dbq8ckgvzz53",
  "venue": "Insights into imaging",
  "versions": []
}