Table_3_Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model.docx

ObjectiveTo explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA).
MethodsA 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.
ResultsThe 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.
ConclusionsThe 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.

研究目的：本研究旨在探索肺腺癌伴软脑膜转移（leptomeningeal metastases, LM）的预后影响因素，并构建一款整合分子改变的更新版分级预后评估模型（molecular graded prognostic assessment, molGPA）。
研究方法：本研究从202例肺腺癌伴软脑膜转移患者中纳入162例作为研究队列。将数据随机划分为训练集（80%）与验证集（20%），在训练集中采用Cox回归与随机生存森林方法筛选具有统计学意义的变量并构建预后模型。计算该模型的C指数，并与既往molGPA模型的C指数进行对比。
研究结果：Cox回归与随机生存森林模型均筛选出4项具有统计学意义的预测变量，包括卡氏功能状态评分（Karnofsky Performance Status, KPS）、LANO神经功能评估、酪氨酸激酶抑制剂（Tyrosine Kinase Inhibitor, TKI）治疗线数以及原发肿瘤控制情况。基于上述4项预后因素，本研究构建了一款新型靶向治疗辅助的2022版molGPA模型，用于预测肺腺癌伴软脑膜转移患者的预后。该预后模型在训练集与验证集中的C指数均高于2017版肺molGPA与2019版molGPA模型。
研究结论：2022版molGPA模型作为既往molGPA模型的重大更新，具备更优异的预测性能，可用于临床决策制定以及未来临床试验的患者分层。