A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data

Purpose Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions' diagnostic efficiency. Methods After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set. Results Selecting features with around 800 genes for training, the R-2-score of a 10-fold CV of training data can reach 96.38%, and the R-2-score of test data can reach 83.3%. Conclusion These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors' location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

Automated summary

The study aimed to establish a machine learning model to identify primary lesions for primary metastatic tumors in an integrated learning approach, aiming to improve diagnostic efficiency. The results showed that combining tumor data with machine learning methods can predict the location of primary metastatic tumors accurately.