Institute of Clinical Pharmacology, Goethe - University, Theodor - Stern - Kai 7, 60590, Frankfurt am Main, Germany.
Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Project Group Translational Medicine and Pharmacology TMP, Theodor - Stern - Kai 7, 60596, Frankfurt am Main, Germany.
Breast Cancer Res Treat. 2018 Sep;171(2):399-411. doi: 10.1007/s10549-018-4841-8. Epub 2018 Jun 6.
Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain.
Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28-75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either "persisting pain" or "non-persisting pain" groups. Unsupervised machine learning was applied to map the parameters to these diagnoses.
A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with "yes/no" items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%.
The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.
通过早期识别高风险患者，预防乳腺癌手术后持续性疼痛是临床需求。本研究采用有监督机器学习来识别预测显著疼痛持续存在的参数。
从 1000 名（年龄 28-75 岁）接受乳腺癌治疗的女性中，在术后 6 个月内采集了 500 多个人口统计学、临床和心理参数。使用 11 点数字评分量表在术前和术后 1、6、12、24 和 36 个月评估疼痛。将 12、24 和 36 个月的评分用于将患者分配到“持续性疼痛”或“非持续性疼痛”组。采用无监督机器学习将参数映射到这些诊断中。
创建了一个基于符号规则的分类器工具，该工具由 21 个单一或聚合参数组成，包括人口统计学特征、心理和疼痛相关参数，形成一个具有“是/否”项（决策规则）的问卷。如果至少应用了 21 条规则中的 10 条，则在交叉验证时预测持续性疼痛的准确率为 86%，阴性预测值约为 95%。
本研究通过机器学习分析表明，即使使用来自大样本队列的大量参数，早期识别这些患者也只是部分成功。这表明需要更多的参数来准确预测持续性疼痛。但是，使用当前参数，可以几乎 95%的确定性排除接受乳腺癌治疗的女性发展为持续性疼痛的可能性。
