Machine Learning to Predict the Occurrence of Distant Organ Involvement in Primary Lymphoma of Bone: A Mixed Cohort Study Based on the SEER Database and Chinese single-center Data

Machine Learning to Predict the Occurrence of Distant Organ Involvement in Primary Lymphoma of Bone: A Mixed Cohort Study Based on the SEER Database and Chinese single-center Data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 April 2025 V1 Latest version Share on Machine Learning to Predict the Occurrence of Distant Organ Involvement in Primary Lymphoma of Bone: A Mixed Cohort Study Based on the SEER Database and Chinese single-center Data Authors : Jia-ji Ren , Peng Lu , Pan-feng Yu , Bin Cai , Wang Jing 0000-0002-5337-5370 , MD Chao-qun You , Cheng Peng , Lin-fei Cheng , and Tielong Liu 0000-0002-1450-6104 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174497043.36787561/v1 146 views 58 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Primary lymphoma of bone (PLB) significantly worsens in prognosis with distant organ involvement, leading to decreased survival rates. Early detection and appropriate intervention are critical, yet systematic treatment strategies and predictive models are lacking. This study aims to develop and validate a machine learning model to predict the risk of distant metastasis in PLB and identify relevant risk factors. Utilizing the SEER database from the National Institutes of Health, 690 PLB patients diagnosed between 2000 and 2021 were analyzed to construct machine learning models. The models’ performance was evaluated using ROC AUC, with the best-performing model being further validated on an external cohort of 142 PLB patients from Changzheng Hospital, demonstrating model generalizability. SHAP values were used to visualize disease-related risk factors. A web-based calculator employing the optimal model was developed to predict PLB distant organ involvement risk. In total, 832 patients were included, with 666 experiencing distant metastasis. The Random Forest model showed the best predictive capability, achieving an internal accuracy of 0.852 and AUC of 0.907. External validation confirmed its performance, with an accuracy of 0.929 and AUC of 0.977.This study presents an RF algorithm-based model to assist clinicians in making informed clinical predictions for PLB patients. Supplementary Material File (figure_1.doc) Download 477.50 KB File (figure_2.doc) Download 32.00 KB File (figure_3.doc) Download 1.26 MB File (figure_4.doc) Download 306.86 KB File (manuscript.doc) Download 2.42 MB File (table_1.doc) Download 116.50 KB File (table_2.doc) Download 121.50 KB File (table_3.doc) Download 74.50 KB Information & Authors Information Version history V1 Version 1 18 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Jia-ji Ren Shandong Second Medical University View all articles by this author Peng Lu University of Shanghai for Science and Technology School of Health Science and Engineering View all articles by this author Pan-feng Yu Peking University People's Hospital View all articles by this author Bin Cai Shanghai 6th Peoples Hospital Affiliated to Shanghai Jiao Tong University View all articles by this author Wang Jing 0000-0002-5337-5370 Shanghai Changzheng Hospital View all articles by this author MD Chao-qun You Shandong Second Medical University View all articles by this author Cheng Peng Shanghai Changzheng Hospital View all articles by this author Lin-fei Cheng Anhui University of Science and Technology Medical School View all articles by this author Tielong Liu 0000-0002-1450-6104 [email protected] Shandong Second Medical University View all articles by this author Metrics & Citations Metrics Article Usage 146 views 58 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jia-ji Ren, Peng Lu, Pan-feng Yu, et al. Machine Learning to Predict the Occurrence of Distant Organ Involvement in Primary Lymphoma of Bone: A Mixed Cohort Study Based on the SEER Database and Chinese single-center Data. Authorea . 18 April 2025. DOI: https://doi.org/10.22541/au.174497043.36787561/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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