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Benign and malignant nodules were segmented for feature extraction using PyRadiomics. Principal component analysis reduced feature dimensionality to twenty components. Logistic regression models using ComBat correction, fixed effects or random effects to mitigate for disparate data across study centres were compared. Models were fit to the whole dataset and evaluated using 10-fold cross-validation. Internal-external validation assessed between-centre heterogeneity. Models were benchmarked against volume, Brock score and scrambled voxels. Calibration was assessed using calibration curves. Decision-curve analysis was used to assess net benefit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Between-centre discrimination variability was observed with an area under the curve (AUC) ranging 0.75\u20130.91. The mixed-effects model provided better discrimination over ComBat or fixed effects alone (AUC 0.91 (95% confidence interval [CI]: 0.90\u20130.93)\n                      <jats:italic>versus<\/jats:italic>\n                      0.75 (95% CI: 0.72\u20130.78) and 0.88 (95% CI: 0.86\u20130.90), respectively (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001 and 0.003, respectively) in cross-validation. Radiomics features retained significance in multivariable models including established clinical features. The final model was superior to models developed using volume alone, Brock score, and scrambled-voxels. The model was well calibrated (intercept -0.036, slope 0.89 with net benefit compared to volume or Brock score alone.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Radiomics models show between-centre heterogeneity, with resilience best achieved with mixed-effects models. The final model had high performance with net benefit over established risk models.<\/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                      Can heterogeneous data from multiple centres be used to predict lung nodule malignancy risk?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      Radiomics cancer prediction models must account for heterogeneity to maintain performance between different centres. The mixed-effects model achieved AUC 0.91 (cross-validation) for cancer prediction.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Relevance statement<\/jats:italic>\n                      <\/jats:bold>\n                      The model provided net benefit for nodule investigation over Brock score and volume alone and could assist in decision-making following prospective validation.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00791-2","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:45:53Z","timestamp":1791189953000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics machine learning models for lung cancer early diagnosis in heterogenous multicentre chest CT data: LIBRA study results"],"prefix":"10.1186","volume":"10","author":[{"given":"Benjamin","family":"Hunter","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashanthi","family":"Ratnakumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nishanthi","family":"Senthivel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amyn","family":"Bhamani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abigail","family":"Coe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susannah","family":"Bloch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel V.","family":"Kemp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Doran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neal","family":"Navani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arjun","family":"Nair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Ratoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claire E.","family":"Wells","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Garner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Nicolson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catey","family":"Bunce","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Blackledge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"Devaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric O.","family":"Aboagye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8368-3406","authenticated-orcid":false,"given":"Richard W.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"791_CR1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1097\/PTS.0b013e3182948b1a","volume":"10","author":"EP Hess","year":"2014","unstructured":"Hess EP, Haas LR, Shah ND, Stroebel RJ, Denham CR, Swensen SJ (2014) Trends in computed tomography utilization rates: a longitudinal practice-based study. 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NHS Health Regulatory Authority (HRA) and research ethics committee (REC) approval were obtained for this retrospective cohort study (IRAS ID: 274775, REC reference 20\/NI\/0088, ClinicalTrials.gov: NCT04270799). Written informed consent was not required, as approved by the Institutional Review Board and subsequent NHS research approvals.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors of this manuscript declare relationships with the following companies: Dr Lee is funded by the Royal Marsden NIHR BRC, and Royal Marsden Cancer Charity. RWL\u2019s institution receives compensation for time spent in a secondment role at the NHS England lung cancer screening programme and as National Speciality Lead for the National Institute of Health and Care Research. He has received research funding from CRUK, Innovate UK (co-funded by GE Healthcare, Roche Diagnostics, Optellum, Elliptica, and RNA Guardian) and SBRI (including as a co-applicant with QURE.AI), RM Partners Cancer Alliance, and NIHR (including co-applicant in grants with Optellum). He has received honoraria, speaker\/advisory fees, and\/or hospitality\/travel expenses from CRUK, Roche Diagnostics, Johnson & Johnson, Guardant, AstraZeneca, and King Faisal Hospital, Saudi Arabia. He also undertakes medical private practice. AD reports consulting fees from Boehringer Ingelheim, Roche, AZ and Brainomix. Professor Navani is supported by a Medical Research Council Clinical Academic Research Partnership (MR\/T02481X\/1). This work was partly undertaken at University College London Hospitals\/University College London, which received a proportion of funding from the Department of Health\u2019s National Institute for Health Research (NIHR) Biomedical Research Centre\u2019s funding scheme. He has received research funding from NIHR, CRUK, SBRI, UKRI and the Ruth Strauss Foundation. Professor Navani reports honoraria or institutional funding for non-promotional educational talks or advisory boards from Amgen, AstraZeneca, AXANA, Boehringer Ingelheim, Bristol Myers Squibb, EQRx, Fujifilm, Guardant Health, Intuitive, Janssen, Lilly, Merck Sharp & Dohme, Olympus, Roche and Sanofi. Where these companies, or entities have products or services that may be related to the subject matter of the article, these products were neither used directly in this work nor was there any influence on this study design or results. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"149"}}