PDF(Pubmed)
Abstract：
Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made under considerable uncertainty; this commonly leads to undertreatment and, consequently, poorer outcomes. This study aimed to develop a decision support tool that can help to identify candidate adjuvant post-surgery treatment schemes for elderly breast cancer patients based on tumor and patient characteristics. Our approach was to generate predictions of patient outcomes for different courses of action; these predictions can, in turn, be used to inform clinical decisions for new patients. We used a cohort of elderly patients (≥ 70 years) who underwent surgery with curative intent for early breast cancer to train the models. We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0.869).
摘要：
老年早期乳腺癌患者的管理指南很少。老年人群早期乳腺癌手术的其他辅助系统治疗受到随着年龄增加的合并症的挑战。在非转移性环境中，治疗决策通常是在相当大的不确定性下做出的；这通常会导致治疗不足，因此,较差的结果。这项研究旨在开发一种决策支持工具，可以帮助根据肿瘤和患者特征确定老年乳腺癌患者的候选术后辅助治疗方案。我们的方法是为不同的行动过程生成患者结果的预测；这些预测可以，反过来,用于为新患者的临床决策提供信息。我们使用了一组老年患者（≥70岁），这些患者接受了早期乳腺癌的治愈性手术，以训练模型。我们使用5倍交叉验证测试了7种分类算法，其中80%的数据被随机选择用于训练，其余20%用于测试。我们使用准确性评估模型性能，精度,召回，F1分数，和AUC评分。我们使用自动编码器在分类之前执行降维。与我们测试的其他模型相比，我们使用逻辑回归和线性判别分析模型观察到了更好的性能。当使用自动编码器时，分类性能通常会提高，除非我们预测需要辅助治疗。我们使用逻辑回归模型获得了总体最佳结果，而无需自动编码来预测是否需要辅助治疗（F1评分=0.869）。
