Siqi Tan, Ziyan Chen, Xuefei Hua, Suhan Zhang, Yanshan Zhu, Ruifang Wu, Yuwen Su, Peng Zhang, Yu Liu
{"title":"基于机器学习算法的朗格汉斯细胞组织细胞增生症再激活预测模型。","authors":"Siqi Tan, Ziyan Chen, Xuefei Hua, Suhan Zhang, Yanshan Zhu, Ruifang Wu, Yuwen Su, Peng Zhang, Yu Liu","doi":"10.1684/ejd.2024.4645","DOIUrl":null,"url":null,"abstract":"<p><p>Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The reactivation rate of LCH exceeds 30%. However, an effective prediction model to predict reactivation is lacking. To select potential prognostic factors of LCH and construct an easy-to-use predictive model based on machine-learning algorithms. Clinical records of LCH inpatients in the Second Xiangya Hospital of Central South University, from 2008 to 2022, were retrospectively studied. Seventy-six patients were classified into a reactivated/progressive group or a stable group. Clinical features and laboratory outcomes were compared, and machine-learning algorithms were used for building prognostic prediction models. Clinical classification (single-system LCH, multiple-system LCH, and central nervous system/lung LCH), level of anemia, bone involvement, skin involvement, and elevated monocyte count were the best performing factors and were finally chosen for the construction of the prediction models. Our results show that the above-mentioned five factors can be used together in a prediction model for the prognosis of LCH patients. The major limitations of this study include its retrospective nature and the relatively small sample size.</p>","PeriodicalId":11968,"journal":{"name":"European Journal of Dermatology","volume":"34 2","pages":"109-118"},"PeriodicalIF":2.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.\",\"authors\":\"Siqi Tan, Ziyan Chen, Xuefei Hua, Suhan Zhang, Yanshan Zhu, Ruifang Wu, Yuwen Su, Peng Zhang, Yu Liu\",\"doi\":\"10.1684/ejd.2024.4645\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The reactivation rate of LCH exceeds 30%. However, an effective prediction model to predict reactivation is lacking. To select potential prognostic factors of LCH and construct an easy-to-use predictive model based on machine-learning algorithms. Clinical records of LCH inpatients in the Second Xiangya Hospital of Central South University, from 2008 to 2022, were retrospectively studied. Seventy-six patients were classified into a reactivated/progressive group or a stable group. Clinical features and laboratory outcomes were compared, and machine-learning algorithms were used for building prognostic prediction models. Clinical classification (single-system LCH, multiple-system LCH, and central nervous system/lung LCH), level of anemia, bone involvement, skin involvement, and elevated monocyte count were the best performing factors and were finally chosen for the construction of the prediction models. Our results show that the above-mentioned five factors can be used together in a prediction model for the prognosis of LCH patients. The major limitations of this study include its retrospective nature and the relatively small sample size.</p>\",\"PeriodicalId\":11968,\"journal\":{\"name\":\"European Journal of Dermatology\",\"volume\":\"34 2\",\"pages\":\"109-118\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Dermatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1684/ejd.2024.4645\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"DERMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Dermatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1684/ejd.2024.4645","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"DERMATOLOGY","Score":null,"Total":0}
A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.
Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The reactivation rate of LCH exceeds 30%. However, an effective prediction model to predict reactivation is lacking. To select potential prognostic factors of LCH and construct an easy-to-use predictive model based on machine-learning algorithms. Clinical records of LCH inpatients in the Second Xiangya Hospital of Central South University, from 2008 to 2022, were retrospectively studied. Seventy-six patients were classified into a reactivated/progressive group or a stable group. Clinical features and laboratory outcomes were compared, and machine-learning algorithms were used for building prognostic prediction models. Clinical classification (single-system LCH, multiple-system LCH, and central nervous system/lung LCH), level of anemia, bone involvement, skin involvement, and elevated monocyte count were the best performing factors and were finally chosen for the construction of the prediction models. Our results show that the above-mentioned five factors can be used together in a prediction model for the prognosis of LCH patients. The major limitations of this study include its retrospective nature and the relatively small sample size.
期刊介绍:
The European Journal of Dermatology is an internationally renowned journal for dermatologists and scientists involved in clinical dermatology and skin biology.
Original articles on clinical dermatology, skin biology, immunology and cell biology are published, along with review articles, which offer readers a broader view of the available literature. Each issue also has an important correspondence section, which contains brief clinical and investigative reports and letters concerning articles previously published in the EJD.
The policy of the EJD is to bring together a large network of specialists from all over the world through a series of editorial offices in France, Germany, Italy, Spain and the USA.