Anemia Risk Prediction Model for Osteosarcoma Patients Post-Chemotherapy Using Artificial Intelligence

Abstract

Objective: This study aimed to develop a machine learning model for predicting anemia post-chemotherapy in osteosarcoma patients. Methods: 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. Results: 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/). Conclusion: 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.