{"title":"Predictive nomogram for risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy: development and performance evaluation.","authors":"Yujie Huang, Guang Han","doi":"10.62347/MQQB5184","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To develop an accurate predictive model for identifying patients at high risk of pulmonary infection during radiochemotherapy.</p><p><strong>Methods: </strong>We retrospectively analyzed data from 544 lung cancer patients treated at Hubei Cancer Hospital between May 2019 and October 2022. The patients were divided into training and validation groups (7:3 ratio). An external validation cohort of 100 patients treated from November 2022 to January 2024 was also included. Feature selection and model development were performed using machine learning algorithms, including Lasso regression, Random Forest, XGBoost, and Support Vector Machine (SVM). Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis.</p><p><strong>Results: </strong>Key predictive factors for pulmonary infection risk were identified, including diabetes, chronic obstructive pulmonary disease, chemotherapy intensity, chemotherapy cycles, antibiotic use, age, Karnofsky Performance Status score, systemic inflammation index, prognostic nutritional index, and C-reactive protein. A nomogram-based prediction model was constructed, achieving ROC curve Area Under the Curve values of 0.889 in the training set, 0.897 in the validation set, and 0.875 in the external validation set, demonstrating strong classification ability and stability.</p><p><strong>Conclusion: </strong>We developed a robust nomogram-based model incorporating eight key factors to predict the risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy. This model can assist clinicians in early identification of high-risk patients, enabling timely interventions to improve patient outcomes and quality of life.</p>","PeriodicalId":7437,"journal":{"name":"American journal of cancer research","volume":"15 2","pages":"781-796"},"PeriodicalIF":3.6000,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897617/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/MQQB5184","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To develop an accurate predictive model for identifying patients at high risk of pulmonary infection during radiochemotherapy.
Methods: We retrospectively analyzed data from 544 lung cancer patients treated at Hubei Cancer Hospital between May 2019 and October 2022. The patients were divided into training and validation groups (7:3 ratio). An external validation cohort of 100 patients treated from November 2022 to January 2024 was also included. Feature selection and model development were performed using machine learning algorithms, including Lasso regression, Random Forest, XGBoost, and Support Vector Machine (SVM). Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis.
Results: Key predictive factors for pulmonary infection risk were identified, including diabetes, chronic obstructive pulmonary disease, chemotherapy intensity, chemotherapy cycles, antibiotic use, age, Karnofsky Performance Status score, systemic inflammation index, prognostic nutritional index, and C-reactive protein. A nomogram-based prediction model was constructed, achieving ROC curve Area Under the Curve values of 0.889 in the training set, 0.897 in the validation set, and 0.875 in the external validation set, demonstrating strong classification ability and stability.
Conclusion: We developed a robust nomogram-based model incorporating eight key factors to predict the risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy. This model can assist clinicians in early identification of high-risk patients, enabling timely interventions to improve patient outcomes and quality of life.
目的:建立一种准确的放化疗中肺部感染高危患者的预测模型。方法:回顾性分析2019年5月至2022年10月在湖北省肿瘤医院治疗的544例肺癌患者的资料。将患者分为训练组和验证组,比例为7:3。外部验证队列纳入了2022年11月至2024年1月期间接受治疗的100名患者。使用机器学习算法进行特征选择和模型开发,包括Lasso回归、随机森林、XGBoost和支持向量机(SVM)。采用受试者工作特征(ROC)曲线、校正曲线和决策曲线分析评估模型的性能。结果:确定了肺部感染风险的关键预测因素,包括糖尿病、慢性阻塞性肺疾病、化疗强度、化疗周期、抗生素使用、年龄、Karnofsky Performance Status评分、全身炎症指数、预后营养指数、c反应蛋白。构建基于模态图的预测模型,训练集的ROC曲线Area Under the curve值为0.889,验证集为0.897,外部验证集为0.875,具有较强的分类能力和稳定性。结论:我们建立了一个强大的基于nomogram模型,包含八个关键因素来预测肺癌放化疗患者肺部感染的风险。该模型可以帮助临床医生早期识别高危患者,及时干预,改善患者预后和生活质量。
期刊介绍:
The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.