Recursive partitioning analysis classification and graded prognostic assessment for non-small cell lung cancer patients with brain metastasis: a retrospective cohort study

Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model

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.

Predicting prognosis in resected esophageal squamous cell carcinoma using a clinical nomogram and recursive partitioning analysis

BACKGROUND

Development demand of precise medicine in resectable esophageal squamous cell carcinoma (ESCC) require to recognize patients at high risk treated by surgery alone. Thus, our aim was to construct a clinical nomogram and recursive partitioning analysis (RPA) to predict long-term survival in ESCC treated by surgery alone.

METHODS

Based on the patients with ESCC who treated by three-incisional esophagectomy and two-field lymphadenectomy alone, we identified and integrated significant prognostic factors for survival to build a nomogram. The nomogram was calibrated for overall survival (OS) and the predictive accuracy and discriminative ability was measured by concordance index (c-index) and Akaike information criterion (AIC). Based on the nomogram, the RPA was performed for risk stratification.

RESULTS

A total of 747 patients were included for analysis. Five independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year OS showed optimal agreement between nomogram prediction and actual observation. The AIC value of the nomogram was lower than that of the 7th edition staging system, whereas the c-index of the nomogram was higher than that of the 7th edition staging system. The risk groups stratified by RPA allowed significant distinction between survival curves within respective TNM categories.

CONCLUSION

The RPA based on a clinical nomogram appears to be suitable for risk stratification in OS for resected ESCC. This practical system may help clinicians in decision making and design of clinical studies.

Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model

Using random survival forest, this study was intended to evaluate the prognostic value of serum markers for lung adenocarcinoma patients with brain metastasis (BM), and tried to integrate them into a prognostic model. During 2010 to 2015, the patients were retrieved from two medical centers. Besides the Cox proportional hazards regression, the random survival forest (RSF) were also used to develop prognostic model from the group A (n = 142). In RSF of the group A, the factors, whose minimal depth were greater than the depth threshold or had a negative variable importance (VIMP), were firstly excluded. Subsequently, C-index and Akaike information criterion (AIC) were used to guide us finding models with higher prognostic ability and lower overfitting possibility. These RSF models, together with the Cox, modified-RPA and lung-GPA index were validated and compared, especially in the group B (CAMS, n = 53). Our data indicated that the KSE125 model (KPS, smoking, EGFR-20 (exon 18, 19 and 21) and Ca125) was the best in survival prediction, and performed well in internal and external validation. In conclusions, for lung adenocarcinoma patients with brain metastasis, a validated prognostic nomogram (KPS, smoking, EGFR-20 and Ca125) can more accurately predict 1-year and 2-year survival of the patients.

[Advances in diagnosis and treatment of brain metastases from the primary lung cancer]

肺癌脑转移的发生率为23%-65%，是脑转移性肿瘤中最常见的类型，且预后较差。目前肺癌脑转移的诊治和分子机理已成为众多研究的热点之一。本文就肺癌脑转移的临床特征、诊断和治疗方面的进展以及最新脑转移的机制学研究做了系统的综述。