Recursive Partitioning Analysis Classification and Graded Prognostic Assessment for Non-Small Cell Lung Cancer Patients with Brain Metastasis:A Retrospective Cohort Study

Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients with brain metastasis （BM）.Methods：From Jan 2008 to Dec 2009,the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation,systemic chemotherapy and tyrosine kinase inhibitors （TKIs） in two institutes were analyzed.Survival was estimated by Kaplan-Meier method.The differences of survival rates in subgroups were assayed using log-rank test.Multivariate Cox＇s regression method was used to analyze the impact of prognostic factors on survival.Two prognostic indexes models （RPA and GPA） were validated respectively.Results：All patients were followed up for 1-44 months,the median survival time after brain irradiation and its corresponding 95% confidence interval （95% CI） was 14 （12.3-15.8） months.1-,2-and 3-year survival rates in the whole group were 56.0%,28.3%,and 12.0%,respectively.The survival curves of subgroups,stratified by both RPA and GPA,were significantly different （P0.001）.In the multivariate analysis as RPA and GPA entered Cox＇s regression model,Karnofsky performance status （KPS） ≥ 70,adenocarcinoma subtype,longer administration of TKIs remained their prognostic significance,RPA classes and GPA also appeared in the prognostic model.Conclusion：KPS ≥70,adenocarcinoma subtype,longer treatment of molecular targeted drug,and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.

Role of Recursive Partitioning Analysis and Graded Prognostic Assessment on Identifying Non-Small Cell Lung Cancer Patients with Brain Metastases Who May Benefit from Postradiation Systemic Therapy

Background：The role ofpostradiation systemic therapy in non-small cell lung cancer （NSCLC） patients with brain metastasis （BM） was controversial.Thus,we explored the role of Radiation Therapy Oncology Group recursive partitioning analysis （RTOG-RPA） and graded prognostic assessment （GPA） in identifying population who may benefit from postradiation systemic therapy.Methods：The clinical data of NSCLC patients with documented BM from August 2007 to April 2015 of two hospitals were studied retrospectively.Cox regression was used for multivariate analysis.Survival of patients with or without postradiation systemic therapy was compared in subgroups stratified according to RTOG-RPA or GPA.Results：Of 216 included patients,67.1% received stereotactic radiosurgery （SRS）,24.1% received whole-brain radiation therapy （WBRT）,and 8.8% received both.After radiotherapy,systemic therapy was administered in 58.3% of patients.Multivariate analysis found that postradiation systemic therapy （yes vs.no） （hazard ratio [HR] =0.36 l,95% confidence interval [CI] =0.202-0.648,P =0.001）,radiation technique （SRS vs.WBRT） （HR =0.462,95% CI =0.238-0.849,P =0.022）,extracranial metastasis （yes vs.no） （HR =3.970,95% CI =1.757-8.970,P =0.001）,and Kamofsky performance status （〈70 vs.≥70） （HR =5.338,95% CI =2.829-10.072,P 〈 0.001） were independent factors for survival.Further analysis found that subsequent tyrosine kinase inhibitor （TKI） therapy could significantly reduce the risk of mortality of patients in RTOG-RPA Class IⅡ （HR =0.411,95% CI =0.183-）.923,P =0.031） or with a GPA score of 1.5-2.5 （HR =0.420,95% CI =0.182-0.968,P =0.042）.However,none of the subgroups stratified according to RTOG-RPA or GPA benefited from the additional conventional chemotherapy.Conclusion：RTOG-RPA and GPA may be useful to identify beneficial populations in NSCLC patients with BM ifTKIs were chosen as postradiation systemic therapy.

Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes

Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recursive MBKPCA is proposed for monitoring large scale processes. In this paper, we present a new recursive MBKPCA (RMBKPCA) algorithm, where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost, and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.