Su Zhiping, Nong Zhiwei, Huang Feihong, Zhou Chengxing, Yu Chaojie
Department of Bone and Soft Tissue Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region, China.
Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Cancer Med. 2024 Dec;13(23):e70427. doi: 10.1002/cam4.70427.
This study aimed to develop a machine learning model for predicting anemia post-chemotherapy in osteosarcoma patients.
Clinical data from 631 osteosarcoma patients were collected, and after data filtering, a training set and validation set were created. Various statistical tests were conducted on the data, and single-factor and multiple-factor logistic regression analysis, random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) were used to construct risk prediction models. A new model was created by intersecting the above models to identify common risk factors, and a nomogram was developed to display the new model. The model's performance was validated using the validation set.
Twenty-five risk factors were identified in the anemia group compared to the non-anemia group (p < 0.05). Single-factor logistic regression analysis identified 22 risk factors (AUC 0.895), whereas multiple-factor logistic regression analysis identified 8 risk factors (AUC 0.872), RF identified 7 risk factors (AUC 0.851), SVM identified 16 risk factors (AUC 0.851), and LASSO identified 19 risk factors (AUC 0.902). Five common risk factors (ALB, Ca, CREA, D-dimer, and ESR) were identified through model intersection, yielding a new model with an AUC of 0.85. Internal validation of the new model showed an AUC of 0.802, indicating high predictive ability. A web model application was created (https://anemic-prediction-of-osteosarcoma.shinyapps.io/DynNomapp/).
The developed risk prediction model based on clinical and laboratory data can aid in individualized diagnosis and treatment of anemia in osteosarcoma patients post-chemotherapy.
本研究旨在开发一种机器学习模型，用于预测骨肉瘤患者化疗后的贫血情况。
收集631例骨肉瘤患者的临床数据，经过数据筛选后，创建训练集和验证集。对数据进行了各种统计测试，并使用单因素和多因素逻辑回归分析、随机森林（RF）、支持向量机（SVM）和最小绝对收缩和选择算子（LASSO）构建风险预测模型。通过对上述模型进行交叉分析创建一个新模型，以识别常见风险因素，并开发了一个列线图来展示新模型。使用验证集对模型的性能进行验证。
与非贫血组相比，贫血组共识别出25个风险因素（p < 0.05）。单因素逻辑回归分析识别出22个风险因素（AUC 0.895），而多因素逻辑回归分析识别出8个风险因素（AUC 0.872），随机森林识别出7个风险因素（AUC 0.851），支持向量机识别出16个风险因素（AUC 0.851），LASSO识别出19个风险因素（AUC 0.902）。通过模型交叉分析确定了5个常见风险因素（白蛋白、钙、肌酐、D-二聚体和血沉），得到一个AUC为0.85的新模型。新模型的内部验证显示AUC为0.802，表明具有较高的预测能力。创建了一个网络模型应用程序（https://anemic-prediction-of-osteosarcoma.shinyapps.io/DynNomapp/）。
基于临床和实验室数据开发的风险预测模型有助于骨肉瘤患者化疗后贫血的个体化诊断和治疗。
