| Autor: |
Zhang, Milan, Tong, Jiayi, Ma, Weifeng, Luo, Chongliang, Liu, Huiqin, Jiang, Yushu, Qin, Lingzhi, Wang, Xiaojuan, Yuan, Lipin, Zhang, Jiewen, Peng, Fuhua, Chen, Yong, Li, Wei, Jiang, Ying |
| Předmět: |
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| Zdroj: |
Frontiers in Oncology; 6/10/2022, Vol. 12, p1-9, 9p |
| Abstrakt: |
Objective: To explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA). Methods: A cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models. Results: The Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models. Conclusions: The 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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