PDF(Pubmed)
Abstract：
UNASSIGNED: Potential uncertainties and overtreatment exist in adjuvant chemotherapy for triple-negative breast cancer (TNBC) patients.
UNASSIGNED: This study aims to explore the performance of deep learning (DL) models in personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy.
UNASSIGNED: Patients who received treatment recommended by models were compared to those who did not. Overall survival for treatment according to model recommendations was the primary outcome. To mitigate bias, inverse probability treatment weighting (IPTW) was employed. A mixed-effect multivariate linear regression was employed to visualize the influence of certain baseline features of patients on chemotherapy selection.
UNASSIGNED: A total of 10,070 female TNBC patients met the inclusion criteria. Treatment according to Self-Normalizing Balanced (SNB) individual treatment effect for survival data model recommendations was associated with a survival benefit (IPTW-adjusted hazard ratio: 0.53, 95% CI, 0.32-8.60; IPTW-adjusted risk difference: 12.90, 95% CI, 6.99-19.01; IPTW-adjusted the difference in restricted mean survival time: 5.54, 95% CI, 1.36-8.61), which surpassed other models and the National Comprehensive Cancer Network guidelines. No survival benefit for chemotherapy was seen for patients not recommended to receive this treatment. SNB predicted older patients with larger tumors and more positive lymph nodes are the optimal candidates for chemotherapy.
UNASSIGNED: These findings suggest that the SNB model may identify patients with TNBC who could benefit from chemotherapy. This novel analytical approach may provide debiased individual survival information and treatment recommendations. Further research is required to validate these models in clinical settings with more features and outcome measurements.
UNASSIGNED: This study aims to explore the performance of deep learning (DL) models in personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy.
UNASSIGNED: Patients who received treatment recommended by models were compared to those who did not. Overall survival for treatment according to model recommendations was the primary outcome. To mitigate bias, inverse probability treatment weighting (IPTW) was employed. A mixed-effect multivariate linear regression was employed to visualize the influence of certain baseline features of patients on chemotherapy selection.
UNASSIGNED: A total of 10,070 female TNBC patients met the inclusion criteria. Treatment according to Self-Normalizing Balanced (SNB) individual treatment effect for survival data model recommendations was associated with a survival benefit (IPTW-adjusted hazard ratio: 0.53, 95% CI, 0.32-8.60; IPTW-adjusted risk difference: 12.90, 95% CI, 6.99-19.01; IPTW-adjusted the difference in restricted mean survival time: 5.54, 95% CI, 1.36-8.61), which surpassed other models and the National Comprehensive Cancer Network guidelines. No survival benefit for chemotherapy was seen for patients not recommended to receive this treatment. SNB predicted older patients with larger tumors and more positive lymph nodes are the optimal candidates for chemotherapy.
UNASSIGNED: These findings suggest that the SNB model may identify patients with TNBC who could benefit from chemotherapy. This novel analytical approach may provide debiased individual survival information and treatment recommendations. Further research is required to validate these models in clinical settings with more features and outcome measurements.
摘要：
■三阴性乳腺癌（TNBC）患者的辅助化疗存在潜在的不确定性和过度治疗。
■本研究旨在探讨深度学习（DL）模型在个性化化疗选择中的表现，并量化基线特征对治疗疗效的影响。
■将接受模型推荐治疗的患者与未接受治疗的患者进行比较。根据模型推荐的治疗总生存期是主要结果。为了减轻偏见，采用逆概率治疗加权(IPTW)。采用混合效应多元线性回归来可视化患者某些基线特征对化疗选择的影响。
■共有10070名女性TNBC患者符合纳入标准。根据生存数据模型推荐的自我正常化平衡（SNB）个体治疗效果进行治疗与生存获益相关（IPTW调整后的风险比：0.53，95％CI，0.32-8.60；IPTW调整后的风险差异：12.90，95％CI，6.99-19.01；IPTW调整后的受限平均生存时间差异：5.54，95％CI，1.36-8.61），这超过了其他模型和国家综合癌症网络指南。对于不推荐接受这种治疗的患者，没有观察到化疗的生存益处。SNB预测具有较大肿瘤和更多阳性淋巴结的老年患者是化疗的最佳候选者。
■这些研究结果表明，SNB模型可以识别TNBC患者，他们可以从化疗中获益。这种新颖的分析方法可以提供偏见的个体生存信息和治疗建议。需要进一步的研究以在具有更多特征和结果测量的临床环境中验证这些模型。
■本研究旨在探讨深度学习（DL）模型在个性化化疗选择中的表现，并量化基线特征对治疗疗效的影响。
■将接受模型推荐治疗的患者与未接受治疗的患者进行比较。根据模型推荐的治疗总生存期是主要结果。为了减轻偏见，采用逆概率治疗加权(IPTW)。采用混合效应多元线性回归来可视化患者某些基线特征对化疗选择的影响。
■共有10070名女性TNBC患者符合纳入标准。根据生存数据模型推荐的自我正常化平衡（SNB）个体治疗效果进行治疗与生存获益相关（IPTW调整后的风险比：0.53，95％CI，0.32-8.60；IPTW调整后的风险差异：12.90，95％CI，6.99-19.01；IPTW调整后的受限平均生存时间差异：5.54，95％CI，1.36-8.61），这超过了其他模型和国家综合癌症网络指南。对于不推荐接受这种治疗的患者，没有观察到化疗的生存益处。SNB预测具有较大肿瘤和更多阳性淋巴结的老年患者是化疗的最佳候选者。
■这些研究结果表明，SNB模型可以识别TNBC患者，他们可以从化疗中获益。这种新颖的分析方法可以提供偏见的个体生存信息和治疗建议。需要进一步的研究以在具有更多特征和结果测量的临床环境中验证这些模型。
