A novel prognostic clinical tool for elderly lung cancer patients with bone metastases who have difficulty tolerating or refuse chemotherapy

Zhangheng Huang ¹ Kai Chen ² Binbin Yang ¹ Zhen Zhao ³ Peilin Dai ^4*

• ¹ First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
• ² The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
• ³ West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
• ⁴ Department of Radiotherapy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

The incidence of lung cancer is gradually increasing and is often accompanied by bone metastases at the time of diagnosis. As society continues to develop, the aging of the population is increasing. This study aimed to develop a practical tool to identify the optimal beneficiary population for radiotherapy in elderly patients with bone metastases from lung cancer who have difficulty tolerating or refuse chemotherapy through a multicenter retrospective study.The study included 6228 patients from the Surveillance, Epidemiology, and End Results (SEER) database and 63 patients from our institution. The SEER cohort was randomized into a training cohort versus an internal validation cohort in a 7:3 ratio. Cox regression analysis was used to identify risk factors for patient mortality and to construct an associated predictive model. A risk classification system was developed based on the predictive model, and then subgroup analyses using Kaplan-Meier curves were performed to identify the population that would benefit optimally from radiotherapy in different risk subgroups.The predictive models constructed based on the results of multivariate Cox regression analysis showed relatively excellent performance. The area under the curve (AUC) values were 0.707 and 0.733 in the training cohort, 0.710 and 0.708 in the internal validation cohort, and 0.700 and 0.640 in the external validation cohort. The calibration curves further proved that the model had very high prediction accuracy.Decision curve analysis (DCA) demonstrated that the model could achieve a high net clinical benefit. The risk classification system developed based on the model successfully screened the best beneficiary population for radiotherapy. We recommend radiotherapy for high-risk risk patients, whereas radiotherapy is not recommended for middle and low risk patients.The risk classification system constructed in this study can help clinicians distinguish elderly lung cancer patients with bone metastases who have difficulty tolerating chemotherapy or refuse chemotherapy from those who can benefit optimally from radiotherapy, to achieve personalized and precise treatment for patients, avoiding the waste of medical resources and over-treatment of patients.