Predicting chemotherapy-induced myelosuppression in colorectal cancer: An interpretable, machine learning-based nomogram

Figure 1 Flowchart of the study protocol. CRC: Colorectal cancer; T: Tumor; N: Node; M: Metastasis; HM: Hepatic metastasis; LM: Lung metastasis; PM: Peritoneal metastasis; BSA: Body surface area; BMI: Body mass index; ALB: Albumin; CEA: Carcinoembryonic antigen; CA: Carbohydrate antigen; LASSO: Least absolute shrinkage and selection operator; ML: Machine learning; LR: Logistic regression; DT: Decision trees; RF: Random forest; XGBoost: Extreme gradient boosting; SVM: Support vector machines; GBM: Gradient boosting machines; KNN: K-Nearest neighbors; ANN: Artificial neural network; ET: Extreme trees; ROC: Receiver operating characteristic; AUC: Area under the curve; PR: Precision-recall; AUPRC: Area under the precision-recall curve; PPV: Positive predictive value; NPV: Negative predictive value.