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Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning

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dc.contributor.author윤홍인-
dc.contributor.author최서희-
dc.date.accessioned2024-10-04T01:55:21Z-
dc.date.available2024-10-04T01:55:21Z-
dc.date.issued2024-09-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/200341-
dc.description.abstractPurpose: We aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-β1 pathway with RP. Methods: We prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFβ pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. Results: Severe RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). Conclusion: We successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherElsevier Ireland Ltd-
dc.relation.isPartOfCLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleIntegrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiation Oncology (방사선종양학교실)-
dc.contributor.googleauthorSeo Hee Choi-
dc.contributor.googleauthorEuidam Kim-
dc.contributor.googleauthorSeok-Jae Heo-
dc.contributor.googleauthorMi Youn Seol-
dc.contributor.googleauthorYoonsun Chung-
dc.contributor.googleauthorHong In Yoon-
dc.identifier.doi39161733-
dc.contributor.localIdA04777-
dc.contributor.localIdA04867-
dc.relation.journalcodeJ04423-
dc.identifier.eissn2405-6308-
dc.identifier.pmid10.1016/j.ctro.2024.100819-
dc.subject.keywordLogistic model-
dc.subject.keywordMachine learning-
dc.subject.keywordPrediction model-
dc.subject.keywordRadiation pneumonitis-
dc.subject.keywordSingle nucleotide polymorphism-
dc.contributor.alternativeNameYoon, Hong In-
dc.contributor.affiliatedAuthor윤홍인-
dc.contributor.affiliatedAuthor최서희-
dc.citation.volume48-
dc.citation.startPage100819-
dc.identifier.bibliographicCitationCLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY, Vol.48 : 100819, 2024-09-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers

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