Wang Zhiyu, Sun Jing, Sun Yi, Gu Yifeng, Xu Yongming, Zhao Bizeng, Yang Mengdi, Yao Guangyu, Zhou Yiyi, Li Yuehua, Du Dongping, Zhao Hui
Department of Internal Oncology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai, People's Republic of China.
Department of Radiation, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai, People's Republic of China.
Pain Ther. 2021 Jun;10(1):619-633. doi: 10.1007/s40122-021-00251-2. Epub 2021 Mar 19.
As life expectancy increases for lung cancer patients with bone metastases, the need for personalized local treatment to reduce pain is expanding.
Patients were treated by a multidisciplinary team (MDT), and local treatment including surgery, percutaneous osteoplasty, or radiation. Visual analog scale (VAS) and quality of life (QoL) scores were analyzed. VAS at 12 weeks after treatment was the main outcome. We developed and tested machine learning models to predict which patients should receive local treatment. Model discrimination was evaluated by the area under curve (AUC), and the best model was used for prospective decision-making accuracy validation.
Under the direction of MDT, 161 patients in the training set, 32 patients in the test set, and 36 patients in the validation set underwent local treatment. VAS in surgery, percutaneous osteoplasty, and radiation groups decreased significantly to 4.78 ± 1.28, 4.37 ± 1.36, and 5.39 ± 1.31 at 12 weeks, respectively (p < 0.05), with no significant differences among the three datasets, and improved QoL was also observed (p < 0.05). A decision tree (DT) model that included VAS, bone metastases character, Frankel classification, Mirels score, age, driver gene, aldehyde dehydrogenase 2, and enolase 1 expression had a best AUC in predicting whether patients would receive local treatment of 0.92 (95% CI 0.89-0.94) in the training set, 0.85 (95% CI 0.77-0.94) in the test set, and 0.88 (95% CI 0.81-0.96) in the validation set.
Local treatment provided significant pain relief and improved QoL. There were no significant differences in reducing pain and improving QoL among training, test, and validation sets. The DT model was best at determining whether patients should receive local treatment. Our machine learning model can help guide clinicians to make local treatment decisions to reduce pain.
Trial registration number ChiCRT-ROC-16009501.
随着肺癌骨转移患者预期寿命的增加，通过个性化局部治疗来减轻疼痛的需求也在不断扩大。
患者由多学科团队（MDT）进行治疗，局部治疗包括手术、经皮骨成形术或放疗。分析视觉模拟量表（VAS）和生活质量（QoL）评分。治疗后12周的VAS是主要观察指标。我们开发并测试了机器学习模型，以预测哪些患者应接受局部治疗。通过曲线下面积（AUC）评估模型的辨别力，并使用最佳模型进行前瞻性决策准确性验证。
在MDT的指导下，训练集有161例患者、测试集有32例患者、验证集有36例患者接受了局部治疗。手术组、经皮骨成形术组和放疗组在12周时的VAS分别显著降至4.78±1.28、4.37±1.36和5.39±1.31（p<0.05），三个数据集之间无显著差异，且生活质量也有所改善（p<0.05）。包含VAS、骨转移特征、Frankel分级、Mirels评分、年龄、驱动基因、醛脱氢酶2和烯醇化酶1表达的决策树（DT）模型在预测患者是否接受局部治疗方面，训练集的AUC最佳为0.92（95%CI 0.89 - 0.94）,测试集为0.85（95%CI 0.77 - 0.94），验证集为0.88（95%CI 0.81 - 0.96）。
局部治疗能显著缓解疼痛并改善生活质量。训练集、测试集和验证集在减轻疼痛和改善生活质量方面无显著差异。DT模型在确定患者是否应接受局部治疗方面表现最佳。我们开发的机器学习模型有助于指导临床医生做出局部治疗决策以减轻疼痛。
试验注册号ChiCRT - ROC - 16009501。
