The Use of Recursive Partitioning Analysis Grouping in Patients with Brain Metastases from Non-Small-Cell Lung Cancer
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This study evaluates the use of recursive partitioning analysis (RPA) grouping in an attempt to predict the survival probabilities in patients with brain metastases from non-small-cell lung cancer (NSCLC).Seventy-two patients with brain metastases from NSCLC treated with radiation therapy were included in the study. Sixty-three patients were male and nine patients were female. Their median age was 57 years and their median Karnofsky performance status was 70. At the time of brain metastases, there was no evidence of the intrathoracic disease in 27 patients and the extrathoracic disease was limited to the intracranial disease in 42 patients. In accordance with RPA grouping, 12 patients were in Group 1, 24 patients were in Group 2, and 36 patients were in Group 3. Radiation therapy was delivered to the whole brain at a dose of 30 Gy in 10 fractions in most of the patients.The median survival time was 7 months for Group 1, 5 months for Group 2 and 3 months for Group 3. The survival probability at 1 year was 50% for Group 1, 26% for Group 2 and 14% for Group 3.This study presents evidence supporting the use of RPA grouping in an attempt to predict the survival probabilities in patients with brain metastases from NSCLC.Keywords:
Recursive partitioning
Performance status
Our group has previously published the Graded Prognostic Assessment (GPA), a prognostic index for patients with brain metastases. Updates have been published with refinements to create diagnosis-specific Graded Prognostic Assessment indices. The purpose of this report is to present the updated diagnosis-specific GPA indices in a single, unified, user-friendly report to allow ease of access and use by treating physicians.A multi-institutional retrospective (1985 to 2007) database of 3,940 patients with newly diagnosed brain metastases underwent univariate and multivariate analyses of prognostic factors associated with outcomes by primary site and treatment. Significant prognostic factors were used to define the diagnosis-specific GPA prognostic indices. A GPA of 4.0 correlates with the best prognosis, whereas a GPA of 0.0 corresponds with the worst prognosis.Significant prognostic factors varied by diagnosis. For lung cancer, prognostic factors were Karnofsky performance score, age, presence of extracranial metastases, and number of brain metastases, confirming the original Lung-GPA. For melanoma and renal cell cancer, prognostic factors were Karnofsky performance score and the number of brain metastases. For breast cancer, prognostic factors were tumor subtype, Karnofsky performance score, and age. For GI cancer, the only prognostic factor was the Karnofsky performance score. The median survival times by GPA score and diagnosis were determined.Prognostic factors for patients with brain metastases vary by diagnosis, and for each diagnosis, a robust separation into different GPA scores was discerned, implying considerable heterogeneity in outcome, even within a single tumor type. In summary, these indices and related worksheet provide an accurate and facile diagnosis-specific tool to estimate survival, potentially select appropriate treatment, and stratify clinical trials for patients with brain metastases.
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Recursive Partitioning Analysis (RPA) is a very flexible non parametric algorithm that allows classification of individuals according to certain criteria, particularly in clinical trials, the method is used to predict response to treatment or classify individuals according to prognostic factors.In this paper we examine how often RPA is used in clinical trials and in meta-analysis.We reviewed abstracts published between 1990 and 2016, and extracted data regarding clinical trial phase, year of publication, type of treatment, medical indication and main evaluated endpoints.One hundred and eighty three studies were identified; of these 43 were meta-analyses and 23 were clinical trials. Most of the studies were published between 2011 and 2016, for both clinical trials and meta-analyses of randomized clinical trials. The prediction of overall survival and progression free survival were the outcomes most evaluated, at 43.5% and 51.2% respectively. Regarding the use of RPA in clinical trials, the brain was the most common site studied, while for meta-analytic studies, other cancer sites were also studied. The combination of chemotherapy and radiation was seen frequently in clinical trials.Recursive partitioning analysis is a very easy technique to use, and it could be a very powerful tool to predict response in different subgroups of patients, although it is not widely used in clinical trials.
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[Purpose] To investigate the prognostic factors in 288 non-small cell lung cancer(NSCLC) cases with brain metastasis(BM) after whole-brain radiotherapy(WBRT),to compare the significance of two prognostic index models of the RPA and GPA to clinical and to research.[Methods] From Jan 2006 to Aug 2008,the clinical date of 288 non-small cell lung cancer cases with BM after WBRT were retrospective analysis.Survival rate was estimated by Kaplan-Meier method.Multi-factor Cox regression method was used to analyze the impact of prognostic factors on survival.Based on two prognostic index models(RPA and GPA),the prognosis models were established respectively.The differences of survival rate in sub-group were used log-rank test.[Results] All patients were followed up for 1～33 months,the median survival time after WBRT was 8 months(95% CI:7.07 ～8.92 months).Multi-factor analysis showed that the pre-radiotherapy KPS score,control of primary tumor,age,number of brain metastases,extracranial metastasis,molecular targeted drug treatment were independent prognostic factors affected the survival rate.According to RPA and GPA prognosis models,the survival curve differences in every sub-group were statistically significant(P0.001).[Conclusion] KPS score,primary tumor control,age,number of brain metastases,extracranial metastasis,molecular targeted drug treatment are the independent prognostic factors impacting the survival rate of non-small cell lung cancer with brain metastasis after whole brain radiotherapy.Both RPA and GPA model prognostic indexes could better reflect the prognosis.
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