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Feature stability was quantified with ICC and used to build Full-feature and stability-stratified models, then assessed with fivefold cross-validation and independent tests across differing CT parameters.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/impact-of-ct-acquisition-settings-on-the-stability-of-radiomic-features-and-the-performance-of-pulmonary-nodule-classification-models/443417/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/impact-of-ct-acquisition-settings-on-the-stability-of-radiomic-features-and-the-performance-of-pulmonary-nodule-classification-models/443417.png","ImageObject",300,407,{"name":42,"@type":43},"Miles","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-01","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",13,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Which CT settings most strongly affect radiomic feature stability?","Question",{"text":63,"@type":64},"Slice thickness showed the greatest impact on feature stability, while image transmission methods had the least impact, based on the reported proportions of stable features.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is radiomic feature stability measured in the study?",{"text":68,"@type":64},"Stability is evaluated using the intraclass correlation coefficient (ICC), with stable features defined as ICC ≥ 0.8, intermediate stable as 0.4 \u003C ICC \u003C 0.8, and unstable features as ICC ≤ 0.4.",{"name":70,"@type":61,"acceptedAnswer":71},"Do models built from stable features generalize better across different CT parameters?",{"text":72,"@type":64},"Yes. 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Insights into Imaging (2026)17:4 [https://doi.org/10.1186/s13244-025-02179-z](https://doi.org/10.1186/s13244-025-02179-z)  \nORIGINAL ARTICLE Open Access  \nImpact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models  \nQian Zhou 1, Chengting Lin2, Jinyi Jiang 1, Yuwei Li2, Yue Yu2, Shiyang Huang2, Chaokang Han3, Liting Shi2* and Lei Shi 1,2*   \nObjectives To evaluate the stability of radiomic features under different CT acquisition settings and investigate its impact on diagnostic model performance and generalizability.  \nMaterials and methods 198 patients with 1227 pulmonary nodules underwent chest CT scans using varied settings (three slice thicknesses, two reconstruction matrices, six convolution kernels, two transmission methods) . 1394 radiomic features were extracted per nodule. Feature stability was evaluated using the Intraclass Correlation Coefficient (ICC, stable: ICC ≥ 0 . 8, intermediate stable: 0 .4 \u003C ICC \u003C 0 . 8, unstable: ICC ≤ 0 .4) . Four diagnostic models (Full-feature, Stable, Unstable, Intermediate stable) were developed using two datasets (lung cancer screening, n = 184; clinical scenarios, n = 1192) . In addition, three combination models were constructed for ablation analysis. Model performance and generalizability were assessed via fivefold cross-validation and independent test sets with different CT parameters. Results Slice thickness and image transmission methods had the greatest and least impacts on feature stability (7 . 0% and 83 . 0% stable features, respectively) . In training and validation sets, the Full-feature and Intermediate stable models showed higher AUCs than the Stable and Unstable models (p \u003C 0 . 05) . However, in test sets with varying CT parameters, the Stable model maintained consistent performance (AUC: 0.693–0.728), while the Unstable model exhibited the greatest variability (AUC: 0 .523–0. 800) . Notably, the Full-feature and Intermediate stable models largely predicted nodules as benign, exhibiting limited ability to discriminate malignant cases.  \nConclusion Radiomic feature stability is significantly affected by CT reconstruction parameters, especially slice thickness. Models based on stable features demonstrate better generalizability across varying CT settings, underscoring the importance of assessing feature stability in radiomic-based diagnostics.  \nCritical relevance statement Radiomic feature stability is significantly affected by CT acquisition parameters. Stable radiomic features enhance diagnostic model consistency and reliability across diverse CT settings. Therefore, feature stability analysis and selection of stable features are crucial to enhance model generalizability and stability.  \nKey Points  \n● How do CT settings affect radiomic feature stability and model performance?  \n● Feature stability varies with CT parameters, but stable features enhance model generalizability.  \n● Stable feature models boost diagnostic reliability and clinical applicability.  \n*Correspondence: Liting Shi  \n[shilt@zjcc.org.cn](shilt@zjcc.org.cn)  \nLei Shi  \n[shilei@zjcc.org.cn](shilei@zjcc.org.cn)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025 . Open Access This article is licensed under a Creative Commons Attribution 4 .0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain perm","cbCaivTR5Lp2m5xa","https://ap.wps.com/l/cbCaivTR5Lp2m5xa","pdf",2311212,12,"English","# Objectives\n# Materials and methods\n## Stability assessment with ICC\n## Diagnostic and combination models\n# Results\n# Conclusion\n# Key points","[{\"question\":\"Which CT settings most strongly affect radiomic feature stability?\",\"answer\":\"Slice thickness showed the greatest impact on feature stability, while image transmission methods had the least impact, based on the reported proportions of stable features.\"},{\"question\":\"How is radiomic feature stability measured in the study?\",\"answer\":\"Stability is evaluated using the intraclass correlation coefficient (ICC), with stable features defined as ICC ≥ 0.8, intermediate stable as 0.4 \\u003c ICC \\u003c 0.8, and unstable features as ICC ≤ 0.4.\"},{\"question\":\"Do models built from stable features generalize better across different CT parameters?\",\"answer\":\"Yes. In test sets with varying CT parameters, the Stable-feature model maintained relatively consistent AUC values, whereas the Unstable-feature model showed the greatest variability.\"}]","Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models | PDF",1790703961]