乳腺癌（BC）给治疗决策带来了重大挑战。目前有多种一线治疗方案可供选择，具体取决于决策过程中需要考虑的几个患者特定因素。提出 CureMate，一种临床决策支持系统，用于预测 BC 患者最有效的初始治疗方案。研究了基于人口统计、解剖病理学和磁共振成像变量的不同人工智能模型。 CureMate 的网络应用程序可以轻松使用最佳模型。建立了包含 232 名 BCE 患者的数据库，每名患者均由 29 个变量描述。在四种机器学习算法中，特别是决策树分类器 (DTC)、高斯朴素贝叶斯 (GNB)、k 最近邻 (K-NN) 和支持向量机 (SVM)，确定了最适合该任务的模型，优化并独立测试。SVM 被确定为 BC 治疗计划的最佳模型，测试精度为 0.933。 CureMate 的网络应用程序（包括 SVM 模型）允许引入相关患者变量并显示建议的第一个治疗步骤以及后续步骤的图表。结果证明了 CureMate 在临床环境中的高精度和有效性，表明其潜力帮助从业者做出明智的治疗决定。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process.To present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate's web application allows for easy use of the best model.A database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested.SVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate's web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps.The results demonstrate CureMate's high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions.Copyright © 2024 Elsevier B.V. All rights reserved.