A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.

OBJECTIVE: This study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction. METHODS: Data from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross-validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model-Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score. RESULTS: The deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780-0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non-standard antiemetic regimen. CONCLUSION: The deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC.

Comparative studies on antioxidant activities of extracts and fractions from the leaves and stem of Epimedium koreanum Nakai.

The antioxidant potency of various extracts and fractions from the leaves and stem of Epimedium koreanum Nakai was evaluated using three esteblished methods, specifically the 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radica-scavenging activity assay, the inhibitory effect on lipid peroxidation induced by Fe(2+)/ascorbate (MDA) assay and the ferric reducing power (FRP) assay. The amounts of total phenolics and total flavonoids in the extracts and fractions were determined by spectrophotometric methods and the content of icariin was determined by HPLC. The results showed that all the extracts and fractions exhibited antioxidant activities at different magnitudes of potency. The leaf extract and fractions demonstrated superior antioxidant activity in most of the assays. The decreasing order of antioxidant activities among the extracts/fractions assayed through the three methods were found to be n-BuOH fraction>ethyl acetate fraction>ethanol extract>petroleum ether fraction>water fraction. A positive correlation was found between the amounts of total phenolics, total flavonoids and icariin and DPPH radical scavenging activity (R(2) = 0.9935, 0.9944 and 0.9997, respectively) and inhibitory activity on lipid peroxidation (R(2) = 0.9987, 0.9830 and 0.9886, respectively). The results suggested that Icariin was one of the main constituents contribute to the antioxidant activity of Epimedium koreanum Nakai and the n-BuOH fractions of leaf extract might be valuable antioxidant natural sources.