ArticleAmerican journal of translational research2023
Construction of an auxiliary scoring model for myelosuppression in patients with lung cancer chemotherapy based on random forest algorithm.
Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning-based prediction and external validation of treatment-related myelosuppression in patients with non-small cell lung cancer receiving PD-1 inhibitors plus platinum-doublet chemotherapy.Journal of thoracic disease · 2026Article
- Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.BMC medical imaging · 2026Article
- Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator.Frontiers in oncology · 2026Article
- Construction and validation of a nomogram prediction model for predicting the risk of chemotherapy-induced myelosuppression after chemotherapy in patients with triple-negative breast cancer: a single-center retrospective case-control study.Translational cancer research · 2025Article
- Article
- Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters.Therapeutic advances in medical oncology · 2025Article
- Radiomics model based on vertebral calcium-suppressed CT images for predicting chemotherapy-induced myelosuppression in nasopharyngeal carcinoma.Frontiers in oncology · 2025Article
- Development and validation of a prediction model for myelosuppression in lung cancer patients after platinum-based doublet chemotherapy: a multifactorial analysis approach.American journal of cancer research · 2025Article
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4 authors.
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Abstract
objectiveTo construct an auxiliary scoring model for myelosuppression in patients with lung cancer undergoing chemotherapy based on a random forest algorithm, and to evaluate the predictive performance of the model.
methodsPatients with lung cancer who received chemotherapy in Shanxi Province Cancer Hospital from January 2019 to January 2022 were retrospectively selected as research subjects, and their general demographic information, disease-related indicators, and laboratory test results before chemotherapy were collected. Patients were divided into a training set (136 cases) and a validation set (68 cases) in a ratio of 2:1. R software was used to establish a scoring model of myelosuppression in lung cancer patients in the training set, and the receiver operating characteristic curve, accuracy, sensitivity, and balanced F-score were used in the two data sets to evaluate the predictive performance of the model.
resultsAmong the 204 lung cancer patients enrolled, 75 patients developed myelosuppression during the follow-up period after chemotherapy, with an incidence of 36.76%. The factors in the constructed random forest model were ranked in order of age (23.233), bone metastasis (21.704), chemotherapy course (19.259), Alb (13.833), and gender (11.471) according to the mean decrease accuracy. The areas under the curve of the model in the training and validation sets were 0.878 and 0.885, respectively (all
conclusionThe risk assessment model for the occurrence of myelosuppression in patients with lung cancer chemotherapy based on a random forest algorithm can provide a reference for the accurate identification of high-risk patients.
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