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Epidemiology and Cancer Control |
Department of Neurological Surgery (M.W., T.R., R.M., K.R.L., M.D.P.) and Comprehensive Cancer Center Biostatistics Core (A.M.), University of California, San Francisco, San Francisco, CA 94143; and Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030 (K.A.); USA
2 Address correspondence to Margaret Wrensch, Department of Neurological Surgery, University of california, San Francisco, 44 Page Street Suite 503, San Francisco, CA 94102-1215 (wrensch{at}itsa.ucsf.edu).
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Abstract |
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Key Words: glioblastoma multiforme • glioma • neuropathology review • population-based study • SEER • survival
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Introduction |
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A multinational survival study from 17 European registries (1985-1989) showed that women had somewhat better survival from primary malignant brain tumors than did men in all except two countries (Sant et al., 1998), with relative five-year survival rates of 20% for women and 17% for men. Ethnicity also seems to be linked with survival, with African Americans having higher five-year survival rates than whites (38% vs. 30%) (Davis et al., 1999). Studies based on population registry data have a disadvantage in that pathologic diagnoses are subject to considerable variability depending on numerous factors such as the pathologist's neuropathology training (Aldape et al., 2000) and the time period and the place in which the diagnosis was made, as diagnostic criteria have varied over time and geographic region (Coons et al., 1997; Karak et al., 2000). Population registries also do not generally have as complete treatment data as are available in clinical trials.
Investigators are currently trying to identify and understand tumor markers or other patient characteristics that might influence survival or response to treatment (e.g., Bhowmick et al., 2004; Cairncross et al., 1998; Freije et al., 2004; Kyritsis et al., 1995; Lin et al., 1998; Nigro et al., 2005; Okcu et al., 2004; Puduvalli et al., 2000; Sano et al., 1999; Sigurdson et al., 1998; Simmons et al., 2001; Tang et al., 2005; Wei et al., 1997).
In this article, we make the following comparisons and estimates. (1) We compare survival of population-based cases from the San Francisco Bay Area in which patients were interviewed and in which central neuropathology review was conducted (the study group) to survival of those who were otherwise eligible but not included in the study group. (2) Within the study group, we compare survival estimates based on the International Classification of Diseases, Oncology, second edition (SEER ICD-O-2), diagnoses and those based on the central neuropathology review diagnosis. (3) For histologic groups with sufficient numbers of cases, we estimate survival in relation to several patient demographic and treatment factors.
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Methods |
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Subjects
Eligibility criteria and ascertainment methods for the glioma cases have been previously described (Wiemels et al., 2002; Wrensch et al., 1997). Briefly, any adult (age >20) newly diagnosed with glioma (ICD-O morphology codes 9380-9481) between August 1991 and April 1994 (series 1) and May 1997 and August 1999 (series 2) who resided in any of six San Francisco Bay Area counties at the time of diagnosis was eligible to participate. Potentially eligible cases were identified by using a rapid case ascertainment (RCA) program available through a SEER participating registry, the Northern California Cancer Center (Fremont) (NCCC). Median time from diagnosis to recruitment was 98 days. The study group included consenting patients or their proxies who were interviewed about a variety of factors, who gave written consent to obtain and review pathology specimens and records, and whose review confirmed an eligible diagnosis. The remaining cases (referred to as SEER eligible only) were not included in the study group either because consent was not obtained, central neuropathology review was not possible or revealed a diagnosis not eligible for the study, or cases were identified after the study closed.
Neuropathology Review
Pathology records and specimens were obtained from diagnosing hospitals, and a neuropathologist reviewed all tumors: Richard Davis (University of California, San Francisco [UCSF]) reviewed cases diagnosed between 1991 and 1994 (series 1), and Kenneth Aldape reviewed cases diagnosed between 1997 and 1999 (series 2). Tumors were classified according to the WHO criteria described by Kleihues et al. (1993a, b) with use of a coding form developed for this study. GM corresponds to WHO grade IV, AA to WHO grade III, and astrocytoma to WHO grade II. The corresponding ICD-O-2 codes are 9440 and 9401 for GM and AA, respectively. For astrocytoma, there is not an exact correspondence between the WHO criteria grade II and the ICD-O-2 code 9400. Details of the review of series 1 were previously published by Aldape et al. (2000), who also developed criteria for grouping ICD-O-2 histologic categories used by SEER to compare to the categories from the neuropathology review.
Determination of Vital Status
In December 2002, NCCC-SEER initially provided vital status data they routinely obtain through California State mortality tapes and the National Death Index. For those not identified as deceased, we sent a letter to the last known address and followed this with a telephone call. We searched the California Department of Motor Vehicles, UCSF medical records, and Internet search engines for those whose contact information was no longer valid. We also updated mortality from SEER in July 2004. For those contacted and determined to be alive (n = 152), the date of returned postcard or the last phone contact was their last known date alive. For six cases in which we could not locate the patient, we used the date of last contact as determined by NCCC-SEER. To summarize, patient survival time was either date of death or date of last contact. For patients not known to be dead, the patient was censored as of the date of last contact.
Determination of Treatment Information
Three sources of data, NCCC-SEER, medical record abstraction, and clinical trials database from UCSF, were used to assign treatment and other clinical characteristics. NCCC-SEER treatment information includes summary data for surgery, radiation, and chemotherapy. According to SEER guidelines, NCCC collects curative treatment information that is planned and given within the first course of therapy (within the first four months) following diagnosis. If the first plan of treatment fails, and subsequent therapy is planned and given, that treatment is not recorded by NCCC. Primary treatment data as summarized by NCCC-SEER was categorized as follows for this study: surgery—biopsy only or resection (the method of coding data by SEER precluding reliable classification into subtotal versus total resection); radiation therapy—none noted, external beam, implants, isotopes, or not otherwise specified; and chemotherapy—none noted, single or double agent, or not otherwise specified.
Medical record abstraction variables included KPS at time of diagnosis, tumor location, surgical resection (total, partial, or biopsy), radiation (types: external beam, radiosurgery, or brachytherapy, and total dose), chemotherapy (type and whether given before radiation [neoadjuvant] or during or after radiation [adjuvant]), and date of first relapse. KPS was not available for many subjects and is not included in most analyses in this article. The UCSF Neuro-Oncology Service database contained these data for patients entered in clinical trials. If any of the three data sources (SEER-NCCC, medical records, and UCSF Neuro-Oncology Service) indicated a resection, surgery was classified as resection; cases not classified as having resection were classified as having biopsy only. Radiation treatment was coded as given if that was reported in any of the three sources of information. If any of the three sources indicated chemotherapy being given, it was coded as given, with the following exceptions. Chemotherapy treatment status as recorded in SEER was recoded from "given" to "not given" for patients for whom the only chemotherapy agent listed in the hard-copy abstracts provided by NCCC was hydroxyurea or 5-bromo-2'-deoxyuridine (BUDR), which are radiation sensitizers, or the date chemotherapy was first given came on or after the date of relapse noted in the medical record or clinical trial database.
Data Management and Statistical Methods
We estimated median or lowest quartile months of survival and 95% confidence limits (CLs) and percent of subjects alive at two years by histologic diagnosis from the two coding systems (SEER ICD-O-2 and central review) with Kaplan-Meier life-table methods (Kalbfleisch and Prentice, 1980) using SAS PROC LIFETEST (SAS Institute, 1990). Lowest quartile is presented for histologies in which median months of survival have not yet been reached. We compared distributions of time to death for the study group with time-to-death distributions for SEER-eligible-only diagnostic subgroups that included at least 30 cases, using Cox proportional hazards regression models (Cox, 1972) estimated with SAS PROC PHREG (Stokes et al., 1995), adjusting for age at diagnosis, gender, and white versus nonwhite ethnicity. For the study group, we also computed Cox regressions to compare distributions of time to death as a function of age and other treatment and demographic factors of interest. Cox proportional hazards regression estimates a hazard ratio (HR) for each factor included in the model. For example, the HR associated with having radiation therapy is the probability of death at any point in time for a person alive at that point in time who has had radiation therapy divided by the probability of death at that time for a patient, also alive at that point, who did not have radiation therapy. In the multivariable model, which includes multiple independent variables, the probability is adjusted for the other factors in the model. Initially, age was evaluated as a prognostic factor. All other variables were analyzed by using age-adjusted models. Age-adjusted models were run for diagnostic categories with a minimum of 30 subjects, and multivariable models were used for homogeneous diagnostic categories with a minimum of at least 100 subjects. The associations of marital status with treatment variables were evaluated with age-, gender-, and ethnicity-adjusted odds ratios by using SAS PROC LOGISTIC and with KPS using age- gender-, and ethnicity-adjusted means by using PROC GLM. Results were considered statistically significant if P 
0.05. No adjustment was made for multiple comparisons.
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Results |
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Compared to subjects in SEER-eligible-only cases, the 873 subjects in the study group were, on average, younger at diagnosis (Table 1) and were somewhat more likely to be white. Among patients with SEER-eligible diagnoses of GM or astrocytoma, not otherwise specified (NOS), those in the study group had significantly better survival than did those not in the study group (age-, gender-, ethnicity-adjusted HR = 0.83 [0.71-0.97] for GM; HR = 0.53 [0.32-0.87] for astrocytoma, NOS [Table 1]). There were no significant survival differences between the two groups for AA, oligodendroglial tumors, or other glioma. However, HR estimates for some of these tumors were of the same order of magnitude as observed for GM, but with the study group having poorer survival as compared to the SEER-eligible group (HRs between 1.2 and 1.3 [Table 1]).
Study Group Survival Estimates Based on Central Neuropathology Review Versus SEER ICD-O-2 Diagnosis Coding Systems
With central review diagnoses, a progressive increase can be observed in the median months of survival, from GM to AA to astrocytoma, with nonoverlapping 95% CLs (Table 2). On the other hand, while there is a comparable difference in median months of survival between GM and AA based on SEER diagnoses, the median months of survival for AA and astrocytoma, NOS, are nearly the same, with widely overlapping CLs (Table 1; see the column "Included in SFBGS"). These comparisons by diagnostic coding system also are shown in Fig. 1. Also note that the largest differences in numbers of cases categorized by the two diagnostic coding systems were for the review diagnosis category astrocytoma and the SEER ICD-O-2 category astrocytoma, NOS; specifically, for cases in the study group, review diagnosis coded 43 for astrocytoma and 1 for astrocytoma, NOS (Table 2), whereas SEER ICD-O-2 coded 2 for astrocytoma and 62 for astrocytoma, NOS (Table 1).
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Study Group Age-Adjusted and Multivariable Survival Analyses
Study Group GM. Two GM patients could not be included in multivariable analyses because they lacked data on education. In univariate (age) or age-adjusted analyses of 517 patients, younger age, resection, radiation therapy, chemotherapy, being married at the time of diagnosis, and participation in a UCSF treatment protocol were significantly associated with improved survival (Table 3). In multivariable analyses including gender and ethnicity and all these variables (except participation in a UCSF treatment protocol), younger age, resection, and radiation therapy were highly statistically significant (P < 0.001). Being married at the time of diagnosis was significant (P = 0.05), and diagnosis at an academic hospital was nearly significantly associated with improved survival (P = 0.07) (Table 3). For chemotherapy, the HR approached the null in multivariable analysis. In a separate multivariable analysis including the same variables except diagnosis at an academic hospital, participation in a UCSF treatment protocol was not statistically significantly associated with survival (HR = 0.85; P = 0.21) (Table 3, note 4). Marital status was not related to either chemotherapy or extent of surgery, but married subjects were about twice as likely to have received radiation treatment (age-, gender-, ethnicity-adjusted odds ratio for receiving radiation therapy for married versus unmarried cases was 2.01 [95% CI, 1.23-3.29]). The multivariable analysis in Table 3 shows, however, that the association of marital status with survival exists (with a similar HR) even when adjusted for radiation treatment. In a subset of 428 GM cases with KPS scores, married patients had a mean KPS of 71 as compared to 64 for unmarried patients (P < 0.003). In a model including variables shown in Table 3 plus KPS, the HR (95% CI) for marital status moved toward the null (0.94 [0.76-1.16]), and higher KPS was significantly associated with better survival HR (0.73 [0.69-0.78]).
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Study Group AA. Among 105 patients in the study group with AA, younger age, resection versus biopsy, chemotherapy, and radiation therapy were significantly associated with improved survival in univariate or age-adjusted analyses (Table 4). In multivariable analyses, younger age (P < 0.001), chemotherapy (P = 0.01), and resection (P = 0.002) were the only variables significantly associated with improved survival (Table 4), although having radiation therapy was nearly significant with an estimated twofold improved survival (P = 0.06).
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Study Group Oligodendroglial Tumors. The proportion of subjects diagnosed with oligodendroglioma versus oligoastrocytoma changed between series, likely reflecting somewhat different diagnostic criteria of the two review pathologists. Specifically, in series 1, 34 patients (7%) were centrally reviewed as oligodendroglioma (including anaplastic oligodendroglioma) and 47 (10%) as oligoastrocytoma (including anaplastic). In series 2, 58 patients (14%) were centrally reviewed as oligodendroglioma, and only 10 (2%) had review diagnosis of oligoastrocytoma. Because the total percentage of gliomas with oligodendroglial component was similar for the two series (17% of tumors in series 1 and 16% of tumors in series 2) and because there were too few tumors in the oligoastrocytoma category for separate analysis, we estimated univariate and age-adjusted HRs for tumors diagnosed as oligodendroglioma and for tumors with any oligodendroglial component (Table 5 for those not considered to be anaplastic and Table 6 for those considered to be anaplastic oligodendroglial tumors).
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In analyses of 66 patients with nonanaplastic oligodendroglioma, younger age and resection were significantly associated with improved survival (Table 5). In analyses of 106 patients with tumors with any nonanaplastic oligodendroglial tumors (40 with oligoastrocytoma plus the 66 with oligodendroglioma described above), younger age, resection, and having graduated from college were significantly associated with improved survival in univariate or age-adjusted analyses (Table 5). For patients with anaplastic oligodendroglial tumors, younger age, resection, and not having chemotherapy were significantly associated with improved survival in univariate or age-adjusted analyses (Table 6). We did not conduct separate survival analyses of other histologic categories because the numbers of subjects were too small for meaningful analysis.
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Discussion |
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Comparison of the SEER and Centrally Reviewed Diagnostic Categories Among the Study Group
Comparison of the study group survival estimates by using the central review diagnostic categories versus the SEER ICD-O-2 diagnoses could indicate the extent to which differential classification of tumors might influence survival estimates. In this study, for astrocytic tumors, the review diagnosis codes performed better than the SEER ICD-O-2 codes at discriminating survival. In particular, median survival for the SEER category astrocytoma, NOS, was virtually indistinguishable from that for AA, whereas with the central review diagnoses, astrocytoma had substantially better survival than AA. These findings highlight the often expressed need (Aldape et al., 2000; Bruner et al., 1997; Coons et al., 1997; Karak et al., 2000; McCarthy et al., 2002) for specialist neuropathology review to accurately classify astrocytic tumors (especially those not classified as GM) in order to provide meaningful survival information through population resources. Mechanisms for instituting population-wide neuropathology review and/or revising ICD-O coding criteria for certain subgroups of glioma should be explored and evaluated.
Treatment and Demographic Factors Associated with Survival in the Study Group
As expected, survival of GM patients was significantly improved by younger age at diagnosis, surgical resection versus biopsy only, and radiation therapy in multivariable analysis (CBTRUS, 2002-2003; Curran et al., 1993; Davis et al., 1998, 1999; Horn et al., 1999; Lamborn et al., 2004; Levin et al., 2001; Scott et al., 1998a). In the study group, although chemotherapy and treatment on a UCSF protocol were favorable prognostic indicators in age-adjusted analyses, they moved toward the null in multivariable analysis, suggesting that patients with a more favorable prognosis might have been preferentially provided chemotherapy or placed on a protocol. Interestingly, being married at diagnosis was a favorable prognostic indicator in multivariable analysis, although in the subset of patients with KPS scores, the association moved toward the null when KPS was included in the model. Also, since the association of marital status with GM was of moderate statistical significance (P = 0.05) and was not apparent for AA or nonanaplastic oligodendroglial tumors, the possibility remains that this association could be due to chance, given the number of comparisons made in this report. If not due to chance, the fact that the association of marital status and GM survival appeared to be confounded with KPS at time of diagnosis suggests that the improved survival for married GM patients might result from earlier diagnosis while their KPS was higher. However, other aspects of marriage such as social support might influence KPS and subsequent survival; in this circumstance, KPS could be an intermediate causal factor (not a confounder) by which marital status might influence survival. Although the impact of social factors has been extensively studied in relation to survival for other cancer sites (Banerjee et al., 2004; Charalampopoulou et al., 2004; De Boer et al., 1999; Rosengren and Wilhelmsen, 2004; Smedslund and Ringdal, 2004; Soler-Vila et al., 2003; Spiegel et al., 1989; Ward et al., 2004), little information is available about the association of social factors and glioma survival. Thus, our results also point to the need for further exploration and evaluation of these factors in clinical trials and epidemiologic investigations of brain tumors.
For the approximately 100 participants in the study group with AA, only surgical resection versus biopsy, chemotherapy, and younger age remained as significantly favorable prognostic indicators after multivariable analysis. However, statistical power for some of the other variables may have been weak, and it is worth noting that there was an estimated twofold benefit of radiation therapy after adjustment for other factors. No notable benefits for education, marital status, or diagnosis at an academic hospital or treatment on a UCSF protocol were noted in the multivariable analysis.
For the 106 patients with nonanaplastic oligodendroglial tumors, younger age and surgical resection versus biopsy were significantly associated with better survival. Interestingly, those patients who were college graduates also showed significantly better survival in age-adjusted comparisons. For the 42 patients with anaplastic oligodendroglial tumors, only younger age and surgical resection were statistically significant, but the statistical power for other variables was low, given the small numbers of cases. Clearly, multi-institutional studies will be needed to clarify other factors associated with this relatively rare form of glioma. We cannot assess the impact of tumor loss of 1p or 19q on survival (such tumors are known to be associated with more favorable survival [van den Bent, 2004]), as these data are not routinely available, and we also cannot assess the extent to which loss of 1p or 19q might be confounded with other variables considered. Given that loss of 1p or 19q influences response to chemotherapy, lack of information on these tumor markers precludes further discussion of our finding that those not given chemotherapy had better survival.
As noted in the introduction and above, survival estimates from population registry and clinical trials have different strengths and limitations: Registry data lack consistent and predictive pathology diagnoses for some histologies and have limited treatment data, whereas clinical trials, having stringent pathology criteria and detailed treatment data, are not readily generalizable because many glioma patients are not treated in these trials. Given that this article reports results from a cohort and not a clinical trial, a general limitation of the cohort approach is noteworthy: Patients with particularly good or poor prognoses may be given different treatments, and those with especially poor survival may not live long enough to institute the recommended treatments. This would tend to inflate the observed benefit of certain treatments in this cohort design in comparsion to the corresponding benefits observed in clinical trials.
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Summary and Conclusions |
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Acknowledgments |
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Footnotes |
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3 Abbreviations used are as follows: AA, anaplastic astrocytoma; BUDR, 5-bromo-2'-deoxyuridine; GM, glioblastoma; HR, hazard ratio; ICD-O-2, International Classification of Diseases, Oncology, second edition; NCCC, Northern California Cancer Center; NOS, not otherwise specified; RCA, rapid case ascertainment; RPA, recursive partitioning analysis; SEER, Surveillance, Epidemiology, and End Results; SFBGS, San Francisco Bay Area Adult Glioma Study; UCSF, University of California, San Francisco.
Received for publication March 9, 2005. Accepted for publication July 7, 2005.
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References |
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Aldape, K., Simmons, M.L., Davis, R.L., Miike, R., Wiencke, J., Barger, G., Lee, M., Chen, P., and Wrensch, M. (2000) Discrepancies in diagnoses of neuroepithelial neoplasms: The San Francisco Bay Area Adult Glioma Study. Cancer 88, 2342-2349.
Banerjee, M., George, J., Song, E.Y., Roy, A., and Hryniuk, W. (2004) Tree-based model for breast cancer prognostication. J. Clin. Oncol. 22, 2567-2575.
Berleur, M.P., and Cordier, S. (1995) The role of chemical, physical, or viral exposures and health factors in neurocarcinogenesis: Implications for epidemiologic studies of brain tumors. Cancer Causes Control 6, 240-256.
Bhowmick, D.A., Zhuang, Z., Wait, S.D., and Weil, R.J. (2004) A functional polymorphism in the eGF gene is found with increased frequency in glioblastoma multiforme patients and is associated with more aggressive disease. Cancer Res. 64, 1220-1223.
Bondy, M., Wiencke, J., Wrensch, M., and Kyritsis, A.P. (1994) Genetics of primary brain tumors: A review. J. Neurooncol. 18, 69-81.
Bruner, J.M., Inouye, L., Fuller, G.N., and Langford, L.A. (1997) Diagnostic discrepancies and their clinical impact in a neuropathology referral practice [see comments]. Cancer 79, 796-803.
Bunin, G. (2000) What causes childhood brain tumors? Limited knowledge, many clues. Pediatr. Neurosurg. 32, 321-326.
Cairncross, J.G., Ueki, K., Zlatescu, M.C., Lisle, D.K., Finkelstein, D.M., Hammond, R.R., Silver, J.S., Stark, P.C., MacDonald, D.R., Ino, Y., Ramsay, D.A., and Louis, D.N. (1998) Specific genetic predictors of chemotherapeutic response and survival in patients with anaplastic oligodendrogliomas. J. Natl. Cancer Inst. 90, 1473-1439.
CBTRUS, Central Brain Tumor Registry of the United States (2002-2003) Primary Brain Tumors in the United States: Statistical Report, 1995-1999. Chicago: CBTRUS.
Charalampopoulou, A., Petridou, E., Spyridopoulos, T., Dessypris, N., Oikonomou, A., Athanasiadou-Piperopoulou, F., Baka, M., Kalmanti, M., Polychronopoulou, S., and Trichopoulos, D. (2004) An integrated evaluation of socioeconomic and clinical factors in the survival from childhood acute lymphoblastic leukaemia: A study in Greece. Eur. J. Cancer Prev. 13, 397-401.
Coons, S.W., Johnson, P.C., Scheithauer, B.W., Yates, A.J., and Pearl, D.K. (1997) Improving diagnostic accuracy and interobserver concordance in the classification and grading of primary gliomas. Cancer 79, 1381-1393.
Cox, D.R. (1972) Regression models and life-tables. J. R. Stat. Soc. 34, 187-220.
Curran, W.J., Jr., Scott, C.B., Horton, J., Nelson, J.S., Weinstein, A.S., Fischbach, A.J., Chang, C.H., Rotman, M., Asbell, S.O., and Krisch, R.E. (1993) Recursive partitioning analysis of prognostic factors in three radiation therapy oncology Group malignant glioma trials. J. Natl. Cancer Inst. 85, 704-710.
Davis, F.G., and McCarthy, B.J. (2000) Epidemiology of brain tumors. Curr. Opin. Neurol. 13, 635-640.
Davis, F.G., Freels, S., Grutsch, J., Barlas, S., and Brem, S. (1998) Survival rates in patients with primary malignant brain tumors stratified by patient age and tumor histological type: An analysis based on Surveillance, Epidemiology, and End Results (SEER) data, 1973-1991. J. Neurosurg. 88, 1-10.
Davis, F.G., McCarthy, B., and Jukich, P. (1999) The descriptive epidemiology of brain tumors. Neuroimaging Clin. N. Am. 9, 581-594.
De Boer, M.F., Ryckman, R.M., Pruyn, J.F., and Van den borne, H.W. (1999) Psychosocial correlates of cancer relapse and survival: A literature review. Patient Educ. Couns. 37, 215-230.
Freije, W.A., Castro-Vargas, F.E., Fang, Z., Horvath, S., Cloughesy, T., Liau, L.M., Mischel, P.S., and Nelson, S.F. (2004) Gene expression profiling of gliomas strongly predicts survival. Cancer Res. 64, 6503-6510.
Horn, B., Heideman, R., Geyer, R., Pollack, I., Packer, R., Goldwein, J., Tomita, T., Schomberg, P., Ater, J., Luchtman-Jones, L., Rivlin, K., Lamborn, K., Prados, M., Bollen, A., Berger, M., Dahl, G., McNeil, E., Patterson, K., Shaw, D., Kubalik, M., and Russo, C. (1999) A multi-institutional retrospective study of intracranial ependymoma in children: Identification of risk factors. J. Pediatr. Hematol. Oncol. 21, 203-211.
Inskip, P.D., Linet, M.S., and Heineman, E.F. (1995) Etiology of brain tumors in adults. Epidemiol. Rev. 17, 382-414.
Kalbfleisch, J., and Prentice, R. (1980) The Statistical Analysis of Failure Time Data. New York: John Wiley and Sons.
Karak, A.K., Singh, R., Tandon, P.N., and Sarkar, C. (2000) A comparative survival evaluation and assessment of interclassification concordance in adult supratentorial astrocytic tumors. Pathol. Oncol. Res. 6, 46-52.
Kleihues, P., and Cavenee, W.K. (Eds.) (1997) Pathology and Genetics of Tumours of the Nervous System. Lyon, France: International Agency for Research on Cancer.
Kleihues, P., Burger, P.C., and Scheithauer, B.W. (1993a) Histological Typing of Tumours of the Central Nervous System, 2nd ed. (WHO International Histological Classification of Tumours series). Berlin: Springer-Verlag.
Kleihues, P., Burger, P.C., and Scheithauer, B.W. (1993b) The new WHO classification of brain tumours. Brain Pathol. 3, 255-268.
Kyritsis, A.P., Bondy, M.L., Hess, K.R., Cunningham, J.E., Zhu, D., Amos, C.J., Yung, W.K., Levin, V.A., and Bruner, J.M. (1995) Prognostic significance of p53 immunoreactivity in patients with glioma. Clin. Cancer Res. 1, 1617-1622.
Lamborn, K.R., Chang, S.M., and Prados, M.D. (2004) Prognostic factors for survival of patients with glioblastoma: Recursive partitioning analysis. Neuro-Oncology 6, 227-235.
Levin, V.A., Leibel, S.A., and Gutin, P.H. (2001) Neoplasms of the central nervous system. In: DeVita, V.T., Jr., Hellman, S., and Rosenberg, S.A. (Eds.), Cancer: Principles and Practice of Oncology, 6th ed., Vol. 2. Philadelphia: Lippincott, Williams and Wilkins, pp. 2100-2160.
Lin, H., Bondy, M.L., Langford, L.A., Hess, K.R., Delclos, G.L., Wu, X., Chan, W., Pershouse, M.A., Yung, W.K., and Steck, P.A. (1998) Allelic deletion analyses of MMAC/PTEN and DMBT1 loci in gliomas: Relationship to prognostic significance. Clin. Cancer Res. 4, 2447-2454.
Louis, D.N., and Stemmer-Rachamimov, A.O. (2000) Pathology and classification. In: Bernstein, M., and Berger, M. (Eds.), Neuro-Oncology: The Essentials. New York: Thieme Medical Publishers, pp. 18-29.
McCarthy, B.J., Surawicz, T., Bruner, J.M., Kruchko, C., and Davis, F. (2002) Consensus Conference on Brain Tumor Definition for registration. Neuro-Oncology 4, 134-145.
Nigro, J.M., Misra, A., Zhang, L., Smirnov, I., Colman, H., Griffin, C., Ozburn, N., Chen, M., Pan, E., Koul, D., Yung, W.K., Feuerstein, B.G., and Aldape, K.D. (2005) Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. Cancer Res. 65, 1678-1686.
Okcu, M.F., Selvan, M., Wang, L.E., Stout, L., Erana, R., Airewele, G., Adatto, P., Hess, K., Ali-Osman, F., Groves, M., Yung, A.W., Levin, V.A., Wei, Q., and Bondy, M. (2004) Glutathione S-transferase polymorphisms and survival in primary malignant glioma. Clin. Cancer Res. 10, 2618-2625.
Preston-Martin, S. (1996) Epidemiology of primary CNS neoplasms. Neurol. Clin. 14, 273-290.
Puduvalli, V.K., Kyritsis, A.P., Hess, K.R., Bondy, M.L., Fuller, G.N., Kouraklis, G.P., Levin, V.A., and Bruner, J.M. (2000) Patterns of expression of RB and p16 in astrocytic gliomas, and correlation with survival. Int. J. Oncol. 17, 963-969.
Rosengren, A., and Wilhelmsen, L. (2004) Cancer incidence, mortality from cancer and survival in men of different occupational classes. Eur. J. Epidemiol. 19, 533-540.
Sano, T., Lin, H., Chen, X., Langford, L.A., Koul, D., Bondy, M.L., Hess, K.R., Myers, J.N., Hong, Y.K., Yung, W.K., and Steck, P.A. (1999) Differential expression of MMAC/PTEN in glioblastoma multiforme: Relationship to localization and prognosis. Cancer Res. 59, 1820-1824.
Sant, M., van der Sanden, G., and Capocaccia, R. (1998) Survival rates for primary malignant brain tumours in Europe. EUROCARE Working Group. Eur. J. Cancer 34, 2241-2247.
SAS Institute (1990) SAS Procedures Guide: Version 6. Cary, NC: SAS Institute, Inc.
Scott, C.B., Scarantino, C., Urtasun, R., Movsas, B., Jones, C.U., Simpson, J.R., Fischbach, A.J., and Curran, W.J., Jr. (1998a) Validation and predictive power of Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis classes for malignant glioma patients: A report using RTOG 90-06. Int. J. Radiat. Oncol. Biol. Phys. 40, 51-55.
Scott, J.N., Rewcastle, N.B., Brasher, P.M., Fulton, D., Hagen, N.A., MacKinnon, J.A., Sutherland, G., Cairncross, J.G., and Forsyth, P. (1998b) Long-term glioblastoma multiforme survivors: A population-based study. Can. J. Neurol. Sci. 25, 197-201.
Sigurdson, A.J., Bondy, M.L., Hess, K.R., Toms, S.A., Kyritsis, A.P., Gu, J., Wang, L.E., Wang, X., Adatto, P., Bruner, J.L., Yung, W.K., Levin, V.A., and Wei, Q. (1998) Gamma-ray mutagen sensitivity and survival in patients with glioma. Clin. Cancer Res. 4, 3031-3035.
Simmons, M.L., Lamborn, K.R., Takahashi, M., Chen, P., Israel, M.A., Berger, M.S., Godfrey, T., Nigro, J., Prados, M., Chang, S., Barker, F.G., 2nd, and Aldape, K. (2001) Analysis of complex relationships between age, p53, epidermal growth factor receptor, and survival in glioblastoma patients. Cancer Res. 61, 1122-1128.
Smedslund, G., and Ringdal, G.I. (2004) Meta-analysis of the effects of psychosocial interventions on survival time in cancer patients. J. Psychosom. Res. 57, 123-131.
Soler-Vila, H., Kasl, S.V., and Jones, B.A. (2003) Prognostic significance of psychosocial factors in African-American and white breast cancer patients: A population-based study. Cancer 98, 1299-1308.
Spiegel, D., Bloom, J.R., Kraemer, H.C., and Gottheil, E. (1989) Effect of psychosocial treatment on survival of patients with metastatic breast cancer. Lancet 2, 888-891.
Stokes, M.E., Davis, C.S., and Koch, G.G. (1995) Categorical Data Analysis Using the SAS System. Cary, NC: SAS Institute, Inc.
Tang, J., Shao, W., Tevfik Dorak, M., Li, Y., Miike, R., Lobashevsky, E., Wiencke, J.K., Wrensch, M., Kaslow, R.A., and Cobbs, C.S. (2005) Positive and negative associations of human leukocyte antigen variants with the onset and prognosis of adult glioblastoma multiforme. Cancer Epidemiol. Biomarkers Prev. 14, 2040-2044.
van den Bent, M.J. (2004) Advances in the biology and treatment of oligodendrogliomas. Curr. Opin. Neurol. 17, 675-680.
Ward, E., Jemal, A., Cokkinides, V., Singh, G.K., Cardinez, C., Ghafoor, A., and Thun, M. (2004) Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J. Clin. 54, 78-93.
Wei, Q., Bondy, M.L., Mao, L., Gaun, Y., Cheng, L., Cunningham, J., Fan, Y., Bruner, J.M., Yung, W.K., Levin, V.A., and Kyritsis, A.P. (1997) Reduced expression of mismatch repair genes measured by multiplex reverse transcription-polymerase chain reaction in human gliomas. Cancer Res. 57, 1673-1677.
Wiemels, J.L., Wiencke, J.K., Sison, J.D., Miike, R., McMillan, A., and Wrensch, M. (2002) History of allergies among adults with glioma and controls. Int. J. Cancer 98, 609-615.
Wrensch, M., Lee, M., Miike, R., Newman, B., Barger, G., Davis, R., Wiencke, J., and Neuhaus, J. (1997) Familial and personal medical history of cancer and nervous system conditions among adults with glioma and controls. Am. J. Epidemiol. 145, 581-593.
Wrensch, M., Minn, Y., Chew, T., Bondy, M., and Berger, M.S. (2002) Epidemiology of primary brain tumors: Current concepts and review of the literature. Neuro-Oncology 4, 278-299.
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