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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean CCC for inter-dose reproducibility using DLIR-H, ASIR-V-50%, and FBP was 0.934\u2009\u00b1\u20090.104, 0.922\u2009\u00b1\u20090.128, and 0.922\u2009\u00b1\u20090.113, respectively (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.055), 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\u2009\u00b1\u20090.094), with 39.4% of features showing near-perfect agreement. The overall CV across reconstructions was 9.87\u2009\u00b1\u200920.32%, with 74.0% of features highly stable. Part-solid nodules showed the lowest variability (7.17\u2009\u00b1\u200913.48%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Reproducibility improved significantly with increasing nodule size (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      The impact of DLIR on radiomic feature reproducibility across radiation doses and reconstruction methods in pulmonary nodules remains unknown.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      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                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Critical relevance statement<\/jats:italic>\n                      <\/jats:bold>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13244-026-02417-y","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T19:54:08Z","timestamp":1791921248000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules"],"prefix":"10.1186","volume":"17","author":[{"given":"Huiyuan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingle","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luna","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qunhui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyu","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zike","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1094-4334","authenticated-orcid":false,"given":"Hong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"2417_CR1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222904","volume":"309","author":"AE Prosper","year":"2023","unstructured":"Prosper AE, Kammer MN, Maldonado F et al (2023) Expanding role of advanced image analysis in CT-detected indeterminate pulmonary nodules and early lung cancer characterization. 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