Anemia Risk Prediction Model for Osteosarcoma Patients Post‐Chemotherapy Using Artificial Intelligence - Zhiping Su; Zhiwei Nong; Feihong Huang; Chengxing Zhou; Chaojie Yu (Cancer Medicine)
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.