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Keywords:

  • glioblastoma;
  • elderly;
  • surgery;
  • prognosis;
  • aging

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

BACKGROUND:

The most-used prognostic scheme for malignant gliomas included only patients aged 18 to 70 years. The purpose of this study was to develop a prognostic model for patients ≥70 years of age with newly diagnosed glioblastoma.

METHODS:

A total of 437 patients ≥70 years of age with newly diagnosed glioblastoma, pooled from 2 tertiary academic institutions, was identified for recursive partitioning analysis (RPA). The resulting prognostic model, based on the final pruned RPA tree, was validated using 265 glioblastoma patients ≥70 years of age from a data set independently compiled by a French consortium.

RESULTS:

RPA produced 9 terminal nodes, which were pruned to 4 prognostic subgroups with markedly different median survivals: subgroup I = patients <75.5 years of age who underwent surgical resection (9.3 months); subgroup II = patients ≥75.5 years of age who underwent surgical resection (6.4 months); subgroup III = patients with Karnofsky performance status of 70 to 100 who underwent biopsy only (4.6 months); and subgroup IV = patients with Karnofsky performance status <70 who underwent biopsy only (2.3 months). Application of this prognostic model to the French cohort also resulted in significantly different (P < .0001) median survivals for subgroups I (8.5 months), II (7.7 months), III (4.3 months), and IV (3.1 months).

CONCLUSIONS:

This model divides elderly glioblastoma patients into prognostic subgroups that can be easily implemented in both the patient care and the clinical trial settings. This purely clinical prognostic model serves as a backbone for the future incorporation of the increasing number of potential molecular prognostic markers. Cancer 2012. © 2012 American Cancer Society.

Malignant gliomas represent approximately 70% of the 22,500 cases of malignant primary brain tumors diagnosed in adults in the United States each year.1 Given the median age of 65 years at glioblastoma (World Health Organization grade IV glioma) diagnosis, a sizable proportion of cases occurs in the elderly population.2 Advancing age is one of the strongest negative prognostic factors in glioblastoma,3 which may be attributable to age-related molecular differences;4 for example, the significantly smaller percentage of patients in this age group with mutations in the IDH1 (isocitrate dehydrogenase 1) gene, which seems to confer a survival advantage.5 Unfortunately, previous studies examining the effects of multiple prognostic factors in the glioblastoma population have excluded patients aged 70 years or older6 or have included relatively small numbers of patients in this age range.7 For example, the most-used prognostic scheme for malignant gliomas, derived from 4 trials conducted by the Radiation Therapy Oncology Group, included only patients aged 18 to 70 years.6 Consequently, clinicians have struggled with management decisions for elderly glioblastoma patients.

In light of this uncertainty, we sought to generate a prognostic model for glioblastoma patients aged 70 years and older based on both pretreatment factors and extent of surgery. Recursive partitioning analysis (RPA) enables classification of patients into successively more homogeneous prognostic groups based on multiple input variables.8 This statistical tool has been used successfully in several retrospective studies of the glioblastoma patient population.6, 7, 9 In this study, we employ RPA to divide glioblastoma patients aged 70 years or older into clinically useful prognostic groups, using data pooled from 2 previous retrospective studies conducted by Memorial Sloan-Kettering Cancer Center (MSKCC), New York, New York,10 and the Cleveland Clinic (CC), Cleveland, Ohio11; in addition, we use data published by a French consortium12 to validate the resulting prognostic model.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

Study Design

We identified patients ≥70 years of age with pathologically confirmed, newly diagnosed glioblastoma by acquiring data sets from clinical studies previously published by authors from MSKCC10 (394 patients), CC11 (206 patients), and a French consortium12 (952 patients). We excluded 10 patients from the CC data set who had glioblastoma diagnoses prior to 1990, for a total of 196 patients. We also excluded 687 patients from the French data set and 153 patients from the MSKCC data set whose initial diagnoses of glioblastoma diagnoses were made prior to 70 years of age, for totals of 265 patients and 241 patients, respectively. No other inclusion or exclusion criteria were used, beyond those already described for these published data sets.10-12

Detailed descriptions of the acquisition methods for the 3 constituent studies included in this work have already been published.10-12 From these data sets, we collected various patient, tumor, and treatment characteristics (Table 1) for all patients meeting the aforementioned criteria. The 3 individual studies represented in this work were approved by the institutional review boards (IRBs) of the MSKCC and the CC, as well as the French government (INCa), Association des Neuro-Oncologues d'Expression Française, Société Française de NeuroChirurgie, and Société Française de Neuropathologie. An official data sharing agreement among the institutions' IRBs was signed, and identifying information was removed from the data before final processing.

Table 1. Patient, Tumor, and Treatment Characteristicsa
CharacteristicPercent of Assessable Patients: All Cohorts (Absolute Number)Percent of Assessable Patients: MSKCC + CC Only (Absolute Number)Percent of Assessable Patients: French Only (Absolute Number)
  • CC indicates Cleveland Clinic; GTR, gross total resection; ICP, intracranial pressure; KPS, Karnofsky performance status; MSKCC, Memorial Sloan-Kettering Cancer Center; PR, partial resection; RT, radiation therapy.

  • a

    Globally, 144 patients (54%) presented with neurologic deficits in the French cohort.

  • b

    Tumor locations were noted but rarely detailed in the French study.

Site   
 MSKCC34 (241)55 (241)0.0 (0)
 CC28 (196)45 (196)0.0 (0)
 French38 (265)0.0 (0)100.0 (265)
Sex   
 Men58 (404)60 (246)56 (158)
 Women42 (298)40 (191)44 (107)
Age, y   
 ≤73.538 (265)36 (157)41 (108)
 73.6-75.517 (122)16 (72)19 (50)
 75.6-76.510 (68)10 (44)9 (24)
 76.6-78.513 (89)11 (50)15 (39)
 78.6-83.518 (130)21 (90)15 (40)
 ≥83.64 (28)6 (24)1 (4)
KPS   
 <7031 (175)33 (135)27 (40)
 70-10069 (387)67 (279)73 (108)
Symptoms   
 Headache18 (125)19 (80)17 (45)
 Seizure19 (130)17 (73)22 (57)
 Hemiparesis17 (117)26 (112)2 (5)
 Language18 (123)27 (117)2 (6)
 Mental status47 (326)46 (199)48 (127)
 Visual7 (51)11 (45)2 (6)
 General sensory3 (23)5 (23)0 (0)
 Cranial nerves3 (19)4 (17)<1 (2)
 Increased ICP3 (24)1 (5)7 (19)
 Gait12 (82)18 (79)1 (3)
Lesion number12 (82)  
 Single88 (381)88 (381)Not recorded
 Multiple12 (54)12 (54)Not recorded
Lesion location   
 Frontal38 (165)38 (165)Not detailedb
 Temporal38 (166)38 (166) 
 Parietal36 (154)36 (154)
 Occipital11 (48)11 (48)
 Corpus callosum6 (24)6 (24)
 Cerebellum<1 (1)<1 (1)
 Brainstem<1 (2)<1 (2)
 Gliomatosis<1 (2)<1 (2)
 Other5 (22)5 (22)
Surgery   
 Biopsy47 (324)36 (154)64 (170)
 PR33 (231)43 (186)17 (45)
 GTR20 (141)21 (91)19 (50)
RT   
 Yes78 (419)72 (304)45 (119)
 No22 (118)28 (118)55 (146)
RT dose (cGy)   
 <600054 (202)60 (156)40 (48)
 ≥600046 (172)40 (104)60 (71)
Chemotherapy   
 Yes35 (234)35 (143)34 (91)
 No65 (442)65 (268)66 (174)

Statistical Analysis

The date of initial diagnosis was considered the date of the surgical procedure that established the histopathologic diagnosis. Overall survival was defined as the interval between the initial diagnosis and the date of death; patients with unknown survival status were censored at the date of last follow-up. Survival curves were generated using Kaplan-Meier methods, and survival differences were evaluated via the log-rank test. RPA divides patients into successively more homogeneous groups based on chosen input variables, with respect to a predetermined outcome parameter. We employed rpart routines in R (http://cran.r-project.org/) to generate divisions, with respect to overall survival, based on patient and tumor characteristics (Table 1), as well as the extent of surgery leading to a histopathologic diagnosis. All patient and tumor characteristics served as input elements for the RPA, including age as a continuous variable. Beyond the extent of surgery, however, we excluded all treatment characteristics (eg, radiation therapy, radiation therapy dose, and chemotherapy) when selecting input variables for the RPA, thereby ensuring that any prognostic model generated could be used for all glioblastoma patients ≥70 years of age at the time of diagnosis.

All of these input variables were candidates for defining each successive split in the RPA tree, and only patients with recorded entries for a given input variable were used in defining the corresponding split. This process continued until all subgroups generated could no longer be made more homogeneous via additional splits. In order to reduce overfitting, we used rpart to implement tree pruning with a 10-fold cross-validation, thereby estimating the error for each RPA tree size and allowing a tree with minimal error to be selected. Because the estimated error from pruning also involves a degree of randomness, the pruning process was repeated 100 times. A final RPA tree was selected on the basis of the most common number of terminal nodes generated by pruning. The MSKCC + CC data set was considered the “training” data set, and we used the French data set for external validation of the resulting prognostic model.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

Patient, Tumor, and Treatment Characteristics

A complete listing of patient, tumor, and treatment characteristics for all 702 patients ≥70 years of age diagnosed with glioblastoma can be found in Table 1. Characteristics are also reported according to MSKCC + CC (437 patients) versus French (265 patients) cohorts. Percentages displayed reflect only those patients for whom information about a given parameter was available. For the entire cohort, the median age at diagnosis was 75.0 years, and 58% of patients were men. Median Karnofsky performance status (KPS) at diagnosis was 70. For the MSKCC + CC cohort, 36%, 43%, and 21% of histopathologic diagnoses were made by biopsy, partial resection (PR), and gross total resection (GTR), respectively; for the French cohort, the corresponding proportions were 64%, 17%, and 19%, respectively. Overall, the most common symptom at diagnosis was mental status changes (47%). For the MSKCC + CC data set, lesion number was most commonly single (88%); lesion location was most commonly temporal (38%) or frontal (38%), followed closely by parietal (36%). The French data set did not include information pertaining to lesion location and number.

Recursive Partitioning Analysis

For the MSKCC + CC cohort, RPA resulted in a tree with 9 terminal nodes (Fig. 1). To minimize overfitting, we repeated 10-fold cross-validated pruning 100 times; we identified a primary split corresponding to extent of surgery and secondary splits corresponding to age and KPS. Median survival was markedly different for these 4 prognostic subgroups (Table 2), as the corresponding survival curves indicate (Fig. 2A).

thumbnail image

Figure 1. Recursive partitioning analysis (RPA) trees for the 437 patients in the Memorial Sloan-Kettering Cancer Center (MSKCC) + Cleveland Clinic (CC) data set. All patient and tumor characteristics, as well as extent of surgery, (Table 1) were evaluated as potential split points. Nine terminal nodes were pruned to generate 4 prognostic subgroups using the endpoint of overall survival. Abbreviations: KPS=Karnofsky performance status; PR=partial resection; GTR=gross total resection; N=number of patients in subgroup.

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Table 2. Survival by RPA-derived Subgroups for MSKCC + CC Data Set and French Data Set
SiteSubgroupNMedian Survival (mo)95% CI
  1. CC indicates Cleveland Clinic; CI, confidence interval; GTR, gross total resection; KPS, Karnofsky performance status; MSKCC, Memorial Sloan-Kettering Cancer Center; N, number of patients in subgroup; PR, partial resection; RPA, recursive partitioning analysis.

MSKCC + CCI – GTR/PR; Age <75.5 y1619.38.4-11.2
 II – GTR/PR; Age ≥75.5 y1226.45.8-7.6
 III – Biopsy; KPS 70-100834.63.7-5.3
 IV – Biopsy; KPS <70702.32.1-3.1
FrenchI – GTR/PR; Age <75.5 y688.57.1-10.5
 II – GTR/PR; Age ≥75.5 y277.74.3-15.1
 III – Biopsy; KPS 70-100684.33.2-6.3
 IV – Biopsy; KPS <70323.11.4-4.6
thumbnail image

Figure 2. Kaplan-Meier curves showing overall survival for (A) the Memorial Sloan-Kettering Cancer Center (MSKCC) + Cleveland Clinic (CC) data set split according to subgroups derived from its RPA; (B) the French data set split according to subgroups derived from MSKCC + CC RPA. Abbreviations: KPS=Karnofsky performance status; PR=partial resection; GTR=gross total resection.

Download figure to PowerPoint

We applied the 4-subgroup model to the French cohort to validate its generalizability. The resulting French subgroups had median survivals similar to those observed for the MSKCC + CC subgroups (Table 2). In addition, there were significant differences (P < .0001) in the survival curves for the French cohort when they were divided according to the MSKCC + CC subgroups (Fig. 2B).

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

Our retrospective RPA study identified 4 prognostic subgroups with markedly different median survivals: subgroup I included patients <75.5 years of age who underwent GTR/PR; subgroup II included patients ≥75.5 years of age who underwent GTR/PR; subgroup III included patients with KPS of 70 to 100 who underwent biopsy only; and subgroup IV included patients with KPS <70 who underwent biopsy only. The 95% confidence intervals for the median survivals of these 4 subgroups were entirely nonoverlapping for the MSKCC + CC cohort from which they were derived, indicating the magnitude of these prognostic differences. When this 4-subgroup model was applied to an independent cohort of glioblastoma patients aged 70 years and older, we observed significant differences (P < .0001) in median survival for these subgroups. The number of patients in the French cohort was smaller, especially with respect to the subgroups that underwent GTR/PR (95 patients for the French cohort versus 283 patients for the MSKCC + CC cohort). When the MSKCC + CC model was applied to the French cohort, subgroups I and II (i.e., those derived from splitting the 95 patients who underwent GTR/PR by age) were not significantly different, which may be attributable to this small sample size.

Previously published reports pertaining to our study's constituent data sets also demonstrate a survival benefit for GTR/PR versus biopsy only, more specifically, for glioblastoma patients aged 65 years or older,10 aged 70 years or older,11 and aged 10 years or older.12 Another small retrospective study, which compared 88 patients aged 65 years or older who had undergone biopsy to 40 patients who had undergone GTR/PR instead, similarly demonstrated a moderate improvement in survival for surgical resection.13 Although all these studies were retrospective, a single prospective study of 23 patients aged 65 years or older, in which subjects were randomized to undergo either GTR/PR or biopsy, also found a survival advantage for surgical resection.14

Despite the common belief that elderly patients require longer recoveries after extensive neurosurgical procedures and exhibit higher postoperative complication rates,15 the survival benefit of GTR/PR versus biopsy seems to hold regardless of age. Thus, maximal surgical resection preceding RT and chemotherapy, which has become the standard of care for younger glioblastoma patients,16 should also be considered a viable therapeutic course for appropriate elderly glioblastoma patients.

In addition, we also found age and KPS to be important prognostic factors for the GTR/PR and biopsy only subgroups. Age at diagnosis is one of the most important prognostic factors within the general adult glioblastoma population,3, 7 and based on the results of our study and others,10, 11 it continues to exert a significant effect within the elderly glioblastoma population. The same holds true for KPS.

Patient and tumor characteristics can influence clinical management and thus survival, so our study did not include treatment variables other than the extent of surgery in the RPA. Our goal was to derive a prognostic model that could apply to all glioblastoma patients aged 70 years or older at diagnosis. Thus, we included extent of surgery as an RPA variable, because all patients must undergo some surgical procedure to establish a glioblastoma diagnosis. In contrast, we excluded variables related to radiotherapy or chemotherapy from the RPA, because these treatments are not initiated until later in the disease course and are employed much less consistently in this specific population. Elderly glioblastoma patients are often less likely to receive radiotherapy or chemotherapy than their younger counterparts;17, 18 this trend was observed in our data, particularly throughout the earlier years included in this study, because evidence that these modalities improve overall survival in this population is fairly recent.19, 20 Because other RPA studies of glioblastoma have included postsurgical treatment as input variables,6, 7 the resulting models are less useful at glioblastoma diagnosis than the model in this study. In addition, because radiation and chemotherapy are usually given sequentially after diagnosis and older glioblastoma patients have a short survival, inclusion of such variables would require multiple landmark survival analyses to exclude patients who died before the time they were eligible to receive such treatments.

Our current study has several limitations, most notably its retrospective design and the inherent associated biases. Although patients in the French cohort were identified via the French Brain Tumor Database, which draws records of glioblastoma patients from multiple medical settings across the country, the patients in the MSKCC and CC cohorts may differ from the larger glioblastoma population in the United States due to referral and practice biases at these 2 tertiary medical centers. In addition, our study lacked several potentially important input variables, including quality-of-life data, tumor molecular studies such as MGMT (O(6)-methylguanine-DNA methyltransferase) methylation status, and potential imaging biomarkers.21 Finally, therapy was not standardized in any of the 3 data sets. Nonetheless, our study represents a large-scale RPA in the elderly glioblastoma population and complements data from previous RPA studies that either included the entire adult glioblastoma population7 or excluded patients aged 70 years or older.6

Overall, our study provides an RPA-derived prognostic model specifically tailored to glioblastoma patients aged 70 years or older, which we have validated using separate American (MSKCC + CC) and French data sets. Hopefully, the 4 subgroups identified by the present study will be further validated by prospective studies in elderly glioblastoma patients. This prognostic scheme can be implemented easily in both the patient care and the clinical trial settings. This clinical prognostic model also serves as a backbone for the future incorporation of currently preliminary molecular prognostic markers.

FUNDING SOURCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES

This study has been supported by the US National Institutes of Health (NIH) Intramural Research Program (1ZIDBC011098-02). Dr. Fraum is a Fellow in the Clinical Research Training Program, a public-private partnership supported jointly by NIH and Pfizer Inc. (via a grant to the Foundation for NIH from Pfizer Inc.). Dr. Iwamoto is supported by the National Cancer Institute Clinical Investigator Development Program and the NIH Intramural Program (1ZIABC011347-01 and 1ZIABC011348-01). Drs. Zouaoui, Mathieu-Daudé, Fabbro-Peray, Rigau, Taillandier, and Bauchet acknowledge the support of the French government (INCa), Association des Neuro-Oncologues d'Expression Française (ANOCEF), Société Française de NeuroChirurgie (SFNC), and Société Française de Neuropathologie (SFNP).

CONFLICT OF INTEREST DISCLOSURE

Dr. Suh has been a consultant for Abbott Oncology and Dr. Peereboom has been a consultant for Schering Plough (Merck). All other authors made no disclosures.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. FUNDING SOURCES
  7. REFERENCES