{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T20:10:47Z","timestamp":1781813447623,"version":"3.54.5"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Chengde Medical University Project","award":["202423"],"award-info":[{"award-number":["202423"]}]},{"name":"Chengde Medical University Project","award":["202423"],"award-info":[{"award-number":["202423"]}]},{"name":"Hebei Province Introduced Returned Overseas Chinese Scholars Funding Project","award":["C20220107"],"award-info":[{"award-number":["C20220107"]}]},{"name":"Chengde Biomedicine Industry Research Institute Funding project","award":["202205B086"],"award-info":[{"award-number":["202205B086"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P\u02c30.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model\u2019s classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat\u2019s impact on radiomic features in medical imaging.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-024-01306-4","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T15:02:12Z","timestamp":1717686132000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers"],"prefix":"10.1186","volume":"24","author":[{"given":"Xiaolei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Iqbal bin Saripan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongxiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhendong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingzhen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Hamiruce","family":"Marhaban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianling","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"issue":"5","key":"1306_CR1","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.canrad.2020.01.011","volume":"24","author":"JE Bibault","year":"2020","unstructured":"Bibault JE, Xing L, Giraud P, El Ayachy R, Giraud N, Decazes P, Burgun A, Giraud P. Radiomics: a primer for the radiation oncologist. Cancer Radiother. 2020;24(5):403\u201310.","journal-title":"Cancer Radiother"},{"issue":"4","key":"1306_CR2","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441\u20136.","journal-title":"Eur J Cancer"},{"issue":"3","key":"1306_CR3","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1002\/mp.12123","volume":"44","author":"M Shafiq-ul\u2010Hassan","year":"2017","unstructured":"Shafiq-ul\u2010Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44(3):1050\u201362.","journal-title":"Med Phys"},{"key":"1306_CR4","doi-asserted-by":"publisher","first-page":"104400","DOI":"10.1016\/j.compbiomed.2021.104400","volume":"133","author":"R Reiazi","year":"2021","unstructured":"Reiazi R, Abbas E, Famiyeh P, Rezaie A, Kwan JY, Patel T, Bratman SV, Tadic T, Liu F-F, Haibe-Kains B. The impact of the variation of imaging parameters on the robustness of computed Tomography Radiomic features: a review. Comput Biol Med. 2021;133:104400.","journal-title":"Comput Biol Med"},{"issue":"11","key":"1306_CR5","doi-asserted-by":"publisher","first-page":"4498","DOI":"10.1007\/s00330-017-4859-z","volume":"27","author":"I Shiri","year":"2017","unstructured":"Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A. The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol. 2017;27(11):4498\u2013509.","journal-title":"Eur Radiol"},{"issue":"3","key":"1306_CR6","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1002\/mp.14686","volume":"48","author":"OL Wong","year":"2021","unstructured":"Wong OL, Yuan J, Zhou Y, Yu SK, Cheung KY. Longitudinal acquisition repeatability of MRI radiomics features: an ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence. Med Phys. 2021;48(3):1239\u201349.","journal-title":"Med Phys"},{"issue":"5","key":"1306_CR7","doi-asserted-by":"publisher","first-page":"e0251147","DOI":"10.1371\/journal.pone.0251147","volume":"16","author":"A Ibrahim","year":"2021","unstructured":"Ibrahim A, Refaee T, Leijenaar RT, Primakov S, Hustinx R, Mottaghy FM, Woodruff HC, Maidment AD, Lambin P. The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset. PLoS ONE. 2021;16(5):e0251147.","journal-title":"PLoS ONE"},{"issue":"2","key":"1306_CR8","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/TRPMS.2018.2878934","volume":"3","author":"T Upadhaya","year":"2018","unstructured":"Upadhaya T, Valli\u00e8res M, Chatterjee A, Lucia F, Bonaffini PA, Masson I, Mervoyer A, Reinhold C, Schick U, Seuntjens J. Comparison of radiomics models built through machine learning in a multicentric context with independent testing: identical data, similar algorithms, different methodologies. IEEE Trans Radiation Plasma Med Sci. 2018;3(2):192\u2013200.","journal-title":"IEEE Trans Radiation Plasma Med Sci"},{"issue":"11","key":"1306_CR9","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1097\/RLI.0000000000000180","volume":"50","author":"D Mackin","year":"2015","unstructured":"Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, Rodriguez-Rivera E, Dodge C, Jones AK, Court L. Measuring computed tomography scanner variability of radiomics features. Invest Radiol. 2015;50(11):757\u201365.","journal-title":"Invest Radiol"},{"issue":"1","key":"1306_CR10","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","volume":"8","author":"WE Johnson","year":"2007","unstructured":"Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118\u201327.","journal-title":"Biostatistics"},{"issue":"1","key":"1306_CR11","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1148\/radiol.2019182023","volume":"291","author":"F Orlhac","year":"2019","unstructured":"Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of a method to compensate multicenter effects affecting ct radiomics. Radiology. 2019;291(1):53\u20139.","journal-title":"Radiology"},{"key":"1306_CR12","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.neuroimage.2017.08.047","volume":"161","author":"JP Fortin","year":"2017","unstructured":"Fortin JP, Parker D, Tunc B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, et al. Harmonization of multi-site diffusion tensor imaging data. NeuroImage. 2017;161:149\u201370.","journal-title":"NeuroImage"},{"key":"1306_CR13","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","volume":"167","author":"JP Fortin","year":"2018","unstructured":"Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage. 2018;167:104\u201320.","journal-title":"NeuroImage"},{"issue":"1","key":"1306_CR14","doi-asserted-by":"publisher","first-page":"10248","DOI":"10.1038\/s41598-020-66110-w","volume":"10","author":"R Da-Ano","year":"2020","unstructured":"Da-Ano R, Masson I, Lucia F, Dore M, Robin P, Alfieri J, Rousseau C, Mervoyer A, Reinhold C, Castelli J, et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep. 2020;10(1):10248.","journal-title":"Sci Rep"},{"key":"1306_CR15","unstructured":"Latifi K, Ullah G, Gillies R, Moros E. Credence cartridge radiomics phantom CT scans with controlled scanning approach. In.: TCIA; 2018."},{"issue":"9","key":"1306_CR16","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F. Sonka MJMri: 3D slicer as an image computing platform for the quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323\u201341.","journal-title":"Magn Reson Imaging"},{"key":"1306_CR17","doi-asserted-by":"crossref","unstructured":"Mackin D, Fave X, Zhang L, Yang J, Jones AK, Ng CS. Court LJPo: correction: harmonizing the pixel size in retrospective computed tomography radiomics studies. 2018, 13(1):e0191597.","DOI":"10.1371\/journal.pone.0191597"},{"key":"1306_CR18","doi-asserted-by":"crossref","unstructured":"Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, Rodriguez-Rivera E, Dodge C, Jones AK. Court LJIr: measuring CT scanner variability of radiomics features. 2015, 50(11):757.","DOI":"10.1097\/RLI.0000000000000180"},{"issue":"21","key":"1306_CR19","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin J-C, Pieper S, Aerts HJWL. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104\u20137.","journal-title":"Cancer Res"},{"key":"1306_CR20","unstructured":"Hatt M, Vallieres M, Visvikis D, Zwanenburg A. IBSI: an international community radiomics standardization initiative. In.: Soc Nuclear Med; 2018."},{"issue":"1","key":"1306_CR21","first-page":"21","volume":"3","author":"TA Bature, Bature","year":"2022","unstructured":"Bature, Bature TA, Lawal AY, Aji DA, Danjuma H, Sulu AB. Selection of best methods of estimation under multicollinearity. Int J Res Publication Reviews. 2022;3(1):21\u20138.","journal-title":"Int J Res Publication Reviews"},{"issue":"9","key":"1306_CR22","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/jpm11090842","volume":"11","author":"SA Mali","year":"2021","unstructured":"Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Muller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods. J Pers Med. 2021;11(9):842.","journal-title":"J Pers Med"},{"issue":"3","key":"1306_CR23","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10334-020-00892-y","volume":"34","author":"M-J Saint Martin","year":"2021","unstructured":"Saint Martin M-J, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to breast MRI: validation on a multi-scanner phantom study. Magn Reson Mater Phys Biol Med. 2021;34(3):355\u201366.","journal-title":"Magn Reson Mater Phys Biol Med"},{"issue":"3","key":"1306_CR24","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s00330-021-08249-2","volume":"32","author":"S Lennartz","year":"2022","unstructured":"Lennartz S, O\u2019Shea A, Parakh A, Persigehl T, Baessler B, Kambadakone A. Robustness of dual-energy CT-derived radiomic features across three different scanner types. Eur Radiol. 2022;32(3):1959\u201370.","journal-title":"Eur Radiol"},{"issue":"3","key":"1306_CR25","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1007\/s00330-020-07174-0","volume":"31","author":"M Ligero","year":"2021","unstructured":"Ligero M, Jordi-Ollero O, Bernatowicz K, Garcia-Ruiz A, Delgado-Mu\u00f1oz E, Leiva D, Mast R, Suarez C, Sala-Llonch R, Calvo N, et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. Eur Radiol. 2021;31(3):1460\u201370.","journal-title":"Eur Radiol"},{"key":"1306_CR26","doi-asserted-by":"crossref","unstructured":"Zhao B. Understanding sources of variation to improve the reproducibility of radiomics. Front Oncol 2021, 11.","DOI":"10.3389\/fonc.2021.633176"},{"issue":"3","key":"1306_CR27","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1007\/s00330-021-08274-1","volume":"32","author":"Y Xu","year":"2022","unstructured":"Xu Y, Lu L, Sun SH, L-n E, Lian W, Yang H, Schwartz LH, Yang Z-h, Zhao B. Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes. Eur Radiol. 2022;32(3):1517\u201327.","journal-title":"Eur Radiol"},{"issue":"9","key":"1306_CR28","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3390\/jpm11090842","volume":"11","author":"SA Mali","year":"2021","unstructured":"Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, M\u00fcller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods. J Pers Med. 2021;11(9):842.","journal-title":"J Pers Med"},{"issue":"13","key":"1306_CR29","doi-asserted-by":"publisher","first-page":"R150","DOI":"10.1088\/0031-9155\/61\/13\/R150","volume":"61","author":"SSF Yip","year":"2016","unstructured":"Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150\u201366.","journal-title":"Phys Med Biol"},{"issue":"476","key":"1306_CR30","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1198\/016214506000000735","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou H. The adaptive Lasso and its oracle properties. J Am Stat Assoc. 2006;101(476):1418\u201329.","journal-title":"J Am Stat Assoc"},{"issue":"7","key":"1306_CR31","doi-asserted-by":"publisher","first-page":"e0253653","DOI":"10.1371\/journal.pone.0253653","volume":"16","author":"R Da-ano","year":"2021","unstructured":"Da-ano R, Lucia F, Masson I, Abgral R, Alfieri J, Rousseau C, Mervoyer A, Reinhold C, Pradier O, Schick U, et al. A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets. PLoS ONE. 2021;16(7):e0253653.","journal-title":"PLoS ONE"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01306-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01306-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01306-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T15:04:10Z","timestamp":1717686250000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01306-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,6]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1306"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01306-4","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,6]]},"assertion":[{"value":"2 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Because the data set used in this study is a public phantom data set, this study does not involve ethical review and informed consent issues.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The data for this study were collected from public datasets. https:\/\/wiki.cancerimagingarchive.net\/pages\/viewpage.action? pageId=39,879,218.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}],"article-number":"137"}}