Machine Learning Algorithm Guiding Local Treatment Decisions For Lung Cancer Patients With Bone Metastases

Background: As life expectancy increases for lung cancer patients who develop bone metastases, the need for personalized local treatment for bone metastases is expanding. Methods: Lung cancer patients with bone metastases were treated by a multidisciplinary team via surgery, percutaneous osteoplasty, or radiation. The pre- and post-treatment visual analog scale (VAS) and Quality of Life (QoL) scores were analyzed. QoL at 12 weeks was the main outcome. Treatment-related costs and overall survival time (OS) were collected. We used machine learning to develop and test models to predict which patients should receive local treatment. Models discrimination were evaluated by the area under curve (AUC), and the best one was used for validation in clinical use. Results: Under the direction of a multidisciplinary team, 161 patients in the training set, and 32 patients in the test set underwent local treatment. A decision tree model included VAS scale, bone metastases character, Frankel classification, Mirels score, age, driver gene, aldehyde dehydrogenase 2, and enolase 1 expression had a best AUC of 0.92 (95%CI 0.89 to 0.94), and 36 patients in a validation set underwent local treatment guided by the model. Improved QoL and VAS scores were observed at 12 weeks after local treatment in training, test, and validation sets ( p < 0.05), with no significant differences among the three datasets. There were no significant differences in mean costs among the three datasets in the four treatment groups. OS was 18.03±0.45 months and did not significantly differ among treatment groups or the three datasets. Conclusions: Local treatment not only had no negative influence on OS but also provided significant pain relief and improved QoL. QoL, OS or costs did not significantly differ between patients whose treatment was guided by a multidisciplinary team or machine learning model. Our machine learning model using clinical data can help guide clinicians to make local treatment decisions to improve patients’ QoL.Trial registration: No. ChiCRT-ROC-16009501