Research on personalized therapy recommendation of liposome doxorubicin and epirubicin for breast cancer patients after surgery based on deep learning
10.3969/j.issn.1672-8467.2024.04.001
- VernacularTitle:基于深度学习的乳腺癌术后脂质体多柔比星与表柔比星个性化治疗推荐研究
- Author:
Xu-Chun SONG
1
;
Ji-Chun ZHOU
;
Xu-Dong LYU
Author Information
1. 浙江大学生物医学工程与仪器科学学院 杭州 310027
- Keywords:
breast cancer;
epirubicin(EPI);
pegylated liposomal doxorubicin(PLD);
personalized treatment;
deep learning
- From:
Fudan University Journal of Medical Sciences
2024;51(4):443-454
- CountryChina
- Language:Chinese
-
Abstract:
Objective To compare the performance of machine learning(ML)and individualized treatment effect(ITE)models based on deep learning in providing personalized treatment recommendations using real-world clinical datasets,and construct personalized drug treatment recommendation models for pegylated liposomal doxorubicin(PLD)and epirubicin(EPI)in postoperative breast cancer patients,and assist clinical decision-making by evaluating the treatment effects of these drugs.Methods Clinical data of 904 breast cancer patients admitted at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine was collected retrospectively,including 387 cases treated with PLD and 517 cases treated with EPI.The two groups were compared using propensity score matching to assess the 5-year disease free survival(DFS)outcome.Six ITE models,including CFR_WASS,were used to predict the 5-year DFS probability of patients under two drug treatments.Six machine learning(ML)models,including Random Forest,were used as baselines for performance analysis and comparison.Model's Predictive performance was evaluated based on the AUROC.The effectiveness of treatment recommendations was assessed by calculating the difference of 5-year rates between the group where the actual treatment used was consistent with the treatment recommended by the model and the control group.Results Among the 153 matched cases,there was no statistically significant difference in 5-year DFS outcomes between the two groups.In 16 pairs of cases,the PLD group showed better clinical outcomes than the EPI group,and in 12 pairs of cases,the EPI group had better clinical outcomes than the PLD group,confirming individual differences in treatment benefit between the two drugs.The CFR_WASS model achieved the optimal predictive performance(AUROC value was 0.736 8),and there was no significant difference in 5-year DFS rates between most ML groups and the control group;The 5-year DFS rate in the ITE group was lower than that in the control group(P<0.01),showing significant differences.Among them,the 5-year DFS rate in the CFR_WASS group was 2.13%lower than that in the control group.Conclusion The ITE model is more accurate in estimating the individualized treatment effects of two drugs compared to the ordinary ML model,providing effective individualized treatment recommendations,and has certain clinical application value.
