文章：

基于知识驱动的机器学习模型预测(y)pN1乳腺癌总体疾病负担

Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model

原文发布日期：13 April 2024

DOI: 10.3390/cancers16081494

类型: Article

开放获取: 是

英文摘要：

Background: We aimed to construct an expert knowledge-based Bayesian network (BN) model for assessing the overall disease burden (ODB) in (y)pN1 breast cancer patients and compare ODB across arms of ongoing trials. Methods: Utilizing institutional data and expert surveys, we developed a BN model for (y)pN1 breast cancer. Expert-derived probabilities and disability weights for radiotherapy-related benefit (e.g., 7-year disease-free survival [DFS]) and toxicities were integrated into the model. ODB was defined as the sum of disability weights multiplied by probabilities. In silico predictions were conducted for Alliance A011202, PORT-N1, RAPCHEM, and RT-CHARM trials, comparing ODB, 7-year DFS, and side effects. Results: In the Alliance A011202 trial, 7-year DFS was 80.1% in both arms. Axillary lymph node dissection led to higher clinical lymphedema and ODB compared to sentinel lymph node biopsy with full regional nodal irradiation (RNI). In the PORT-N1 trial, the control arm (whole-breast irradiation [WBI] with RNI or post-mastectomy radiotherapy [PMRT]) had an ODB of 0.254, while the experimental arm (WBI alone or no PMRT) had an ODB of 0.255. In the RAPCHEM trial, the radiotherapy field did not impact the 7-year DFS in ypN1 patients. However, there was a mild ODB increase with a larger irradiation field. In the RT-CHARM trial, we identified factors associated with the major complication rate, which ranged from 18.3% to 22.1%. Conclusions: The expert knowledge-based BN model predicted ongoing trial outcomes, validating reported results and assumptions. In addition, the model demonstrated the ODB in different arms, with an emphasis on quality of life.

摘要翻译： 

背景：本研究旨在构建一个基于专家知识的贝叶斯网络模型，用于评估(y)pN1乳腺癌患者的总体疾病负担，并比较正在进行的临床试验各组的总体疾病负担。方法：利用机构数据和专家调查，我们为(y)pN1乳腺癌开发了一个贝叶斯网络模型。模型中整合了专家得出的放疗相关获益（如7年无病生存率）和毒副作用的概率及残疾权重。总体疾病负担定义为残疾权重与相应概率乘积的总和。对Alliance A011202、PORT-N1、RAPCHEM和RT-CHARM试验进行了计算机模拟预测，比较了各组的总体疾病负担、7年无病生存率和副作用。结果：在Alliance A011202试验中，两组患者的7年无病生存率均为80.1%。与哨淋巴结活检联合全区域淋巴结照射相比，腋窝淋巴结清扫导致了更高的临床淋巴水肿发生率和总体疾病负担。在PORT-N1试验中，对照组（全乳照射联合全区域淋巴结照射或乳房切除术后放疗）的总体疾病负担为0.254，而实验组（仅全乳照射或无乳房切除术后放疗）为0.255。在RAPCHEM试验中，放疗范围未影响ypN1患者的7年无病生存率。然而，较大照射范围导致了总体疾病负担的轻度增加。在RT-CHARM试验中，我们识别了与主要并发症发生率相关的因素，该发生率在18.3%至22.1%之间。结论：基于专家知识的贝叶斯网络模型预测了正在进行的临床试验结果，验证了已报告的结果和假设。此外，该模型展示了不同试验组的总体疾病负担，并强调了生活质量的重要性。

原文链接：

Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model