Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study

Frontiers in Oncology

Fei Gao¹  Jinyuan Zhang¹  Xin Hao²  Hongyue Liu³  Huan Li³  Qing Zhai³  Xuan Ye³  Zeyuan Wang⁵  Ze Yu⁶  Hai Wei⁶  Fang Kou⁶ 

[1] Beijing Medicinovo Technology Co., Ltd., Beijing, China;Dalian Medicinovo Technology Co., Ltd., Dalian, China;Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China;Department of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai, China;Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, NSW, Australia;Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China;

关键词: lapatinib;    machine learning;    deep learning;    TabNet;    breast cancer;    real-world study;   

DOI  :  10.3389/fonc.2022.893966

来源: DOAJ

【 摘 要 】

Lapatinib is used for the treatment of metastatic HER2(+) breast cancer. We aim to establish a prediction model for lapatinib dose using machine learning and deep learning techniques based on a real-world study. There were 149 breast cancer patients enrolled from July 2016 to June 2017 at Fudan University Shanghai Cancer Center. The sequential forward selection algorithm based on random forest was applied for variable selection. Twelve machine learning and deep learning algorithms were compared in terms of their predictive abilities (logistic regression, SVM, random forest, Adaboost, XGBoost, GBDT, LightGBM, CatBoost, TabNet, ANN, Super TML, and Wide&Deep). As a result, TabNet was chosen to construct the prediction model with the best performance (accuracy = 0.82 and AUC = 0.83). Afterward, four variables that strongly correlated with lapatinib dose were ranked via importance score as follows: treatment protocols, weight, number of chemotherapy treatments, and number of metastases. Finally, the confusion matrix was used to validate the model for a dose regimen of 1,250 mg lapatinib (precision = 81% and recall = 95%), and for a dose regimen of 1,000 mg lapatinib (precision = 87% and recall = 64%). To conclude, we established a deep learning model to predict lapatinib dose based on important influencing variables selected from real-world evidence, to achieve an optimal individualized dose regimen with good predictive performance.

【 授权许可】

Unknown