AUTHOR=Meng Chunliu , Wang Fang , Tian Jia , Wei Jia , Li Xue , Ren Kai , Xu Liming , Zhao Lujun , Wang Ping 

TITLE=Risk Prediction Model for Synchronous Oligometastatic Non-Small Cell Lung Cancer: Thoracic Radiotherapy May Not Prolong Survival in High-Risk patients

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.897329

DOI=10.3389/fonc.2022.897329

ISSN=2234-943X

ABSTRACT=Background and purpose:
Based on promising clinical studies results, thoracic radiotherapy (TRT) has become an integral part of treatment of oligometastatic non-small cell lung cancer (SOMNSCLC). While, some of them experienced rapid disease progression after TRT and showed no significant survival benefit. How to screen out such patients is a more concerned problem at present. In this study, we developed a risk-prediction model by screening hematological-clinical data of SOM-NSCLC patients, and identified patients who would not benefit from TRT.
Materials and methods: We investigated patients with SOM-NSCLC between 2011 and 2019. A formula named Risk-Total was constructed using factors screened by LASSO-Cox regression analysis. Stabilized inverse probability treatment weight analysis was used to match the clinical-characteristics between TRT and non-TRT groups. The primary endpoint was OS.
Results: We finally included 283 patients divided into two groups, 188 cases for the training cohort and 95 for the validation cohort. Ten prognostic factors included in the Risk-Total formula were: age, N stage, T stage, adrenal metastasis, liver metastasis, sensitive-mutations status, local treatment status to metastatic sites, systemic inflammatory index, CEA and Cyfra211. Patients were divided into low- and high-risk groups based on risk-scores, and TRT was found to have improved the OS of low-risk patients (46.4 vs. 31.7 months, P=0.083; 34.1 vs. 25.9 months, P=0.078), but not that of high-risk patients (14.9 vs. 11.7 months, P=0.663; 19.4 vs. 18.6 months, P=0.811) in the training and validation sets, respectively.
Conclusion: We developed a prediction model to help identify patients with SOMNSCLC who would not benefit from TRT, and TRT could not improve the survival of high-risk patients.