Age

Patients enrolled in most neurosurgical clinical studies are described by age, but this is particularly essential in brain tumor reports because age is such an important prognostic factor for most types of brain tumor. For malignant and low-grade glioma, for example, older patients have much shorter survival for reasons that are still not entirely clear.¹⁰ Older patients with malignant glioma are less likely than younger patients to have known low-grade tumors preceding their diagnosis of malignancy, more likely to have PTEN (phosphatase and tensin homologue from chromosome 10) abnormalities, and less likely to have p53 mutations.¹¹ Pediatric malignant glioma patients have longer median survival than do young adults.¹² Conversely, in medulloblastoma, patients younger than 2 years have shorter survival, partially because of reluctance to subject such young children to aggressive central nervous system radiotherapy, but also probably because of more biologically aggressive disease.^13,14 For both acoustic neuromas and hemangioblastomas, younger patients with apparently isolated sporadic tumors are much more likely to have occult neurofibromatosis type 2 and von Hippel-Lindau disease, respectively, which potentially affects long-term outcome.^15,16 Age at diagnosis or initiation of treatment and age limitations on eligibility for prospective studies should be carefully documented in all brain tumor reports.

Histology

In 1932, Harvey Cushing announced to a hushed crowd that he would never again attempt to report on the subject of brain tumor outcomes as a whole.¹⁷ Almost all subsequent brain tumor outcome studies have followed Cushing’s example in being limited to a single tumor histology and most to a single point in the course of the disease, such as at diagnosis or first recurrence. This seems straightforward enough, except that histologic definitions have changed over time, some diagnoses are highly inconsistent from pathologist to pathologist, and some are dependent on the amount of tissue sampled at surgery (or when diagnoses are based on imaging alone).

The present standard reference for describing the histology of brain tumors is the 2007 histologic classification of the World Health Organization (WHO).^18–20 In interpreting older studies, temporal changes in grading systems should be kept in mind. For glioblastoma, newer grading systems no longer mandate the presence of tissue necrosis, which was formerly required to make the diagnosis.^18,21,22 This change should have little effect on most studies because the majority of glioblastomas still contain necrosis and prognoses are similar for patients whose glioblastomas do or do not contain necrosis.²³ However, for studies that focus specifically on long-term glioblastoma survivors, the evolution of neuropathologic criteria constantly forces a re-evaluation of earlier research. This is so because some patients in whom glioblastoma was originally diagnosed are now recognized to have other tumors by current criteria, such as some anaplastic oligodendrogliomas that contain vascular proliferation or benign tumors whose histopathologic appearance formerly qualified for a diagnosis of glioblastoma. For example, increased awareness of the relatively new diagnosis of pleomorphic xanthoastrocytoma required removal of some patients from one long-term glioblastoma survivor series,²⁴ and recognition of malignant oligodendroglioma elements caused another group to reclassify a third of their long-term glioblastoma survivors.²⁵ Only 17% of long-term glioblastoma survivors in Sweden from 1958 to 1977 were not reclassified on later re-examination.²⁶

At the other end of the glioma spectrum, the identification of pilomyxoid astrocytomas in 1999 caused some poor-prognosis patients to be reclassified from earlier pilocytic astrocytoma series,^27,28 just as increasing recognition of atypical teratoma-rhabdoid tumors has removed some of the worst-prognosis patients from modern medulloblastoma series.^29,30 Medulloblastoma series are also variably affected by categorization into subdiagnoses, such as desmoplastic, anaplastic, and nodular medulloblastoma.¹⁹

For lower grade gliomas, changes in formal grading systems rather than the development of new diagnoses have been more significant. Although the classic histologic criteria for defining oligodendroglial portions of a tumor have generally been established, the subjective nature of their application (in the absence of an immunohistochemical marker), in the context of intense clinical interest in oligodendroglial sensitivity to chemotherapy,³¹ has resulted in important epidemiologic shifts in the frequency of diagnosing oligodendroglioma in the past 2 decades, with a remarkable increase in the proportion of low-grade tumors now thought to contain oligodendroglial elements.³² The most recent WHO revision has introduced a new grade IV diagnosis of “glioblastoma with an oligodendroglioma component” for many tumors that were previously diagnosed as grade III anaplastic oligodendrogliomas.^18,19 The impact of these changes on outcome studies is complex and difficult to predict. Revisions in the WHO definition of atypical meningioma have also been shown to have a concrete impact on the recommended treatment of many patients at certain centers.^33–35

Interpathologist disagreement on brain tumor diagnoses is sufficiently common to require central review for many studies. Discordant results on central review are more common with diagnoses originating at community hospitals, especially those without a neuropathologist, and affect some diagnoses more than others.^36–38 For example, in the San Francisco Bay area, Aldape and coauthors reported more than 50% discordant results on central review of tumors diagnosed as containing an oligodendroglial component, thus indicating that the distinction between oligodendroglial and astrocytic histology is an especially subjective one.³⁸ The diagnosis of glioblastoma in this study was much more reliable. For studies using data sources that include many community hospital diagnoses, such as population-based case registries, it has been recommended that all diagnoses except glioblastoma and meningioma be subjected to central pathology review whenever possible.³⁶ Most multicenter randomized studies of brain tumor treatment now require central pathology review before registration.

An important consideration in studies on glioma surgery is that the pathologic diagnosis may vary with the extent of tumor resection because of the combination of sampling bias and the high degree of spatial pathologic heterogeneity in many gliomas. Glioma grading systems are based on counting individual malignant features that may be seen in successive microscopic fields examined, so the idea that more tissue examined leads to a more malignant diagnosis makes intrinsic sense. This has been demonstrated in clinical studies comparing rates of high-grade diagnoses in patient cohorts that differ in the extent of resection,³⁹ as well as in studies comparing needle biopsy diagnoses with later resections, especially in larger tumors.^40–42 Similarly, within glioblastoma, detection of necrosis is more common with more extensive resection.²³ Less widely recognized is the tendency for more extensive resections to produce more histologic diagnoses that include an oligodendroglial component. Perry and colleagues described a cohort of grade 3 gliomas in which the percentage containing an oligodendroglial component increased from 3% in biopsied tumors to 29% in subtotal resection specimens and 43% in gross total resections.⁴³ Similar results have been described for grade 2 gliomas, both in a single-institution study⁴⁴ (46% oligodendroglial-containing tumors in biopsy specimens, 89% in gross total resections) and in a population-based study⁴⁵ (32% oligodendroglial-containing tumors in biopsy specimens, 62% in resection specimens). Because of sampling bias, when one group in a treatment comparison selectively contains patients with less extensive surgical resection, the pathologic diagnoses in that group will be biased toward lower grade histology and less common oligodendroglial diagnoses than will the true pathology.

Studies in which some or all patients are enrolled on the basis of imaging diagnoses alone, without histologic confirmation, face special problems in reliably defining diagnostic categories. Radiosurgery series and series of tumors undergoing observation without other treatment are particularly dependent on accurate imaging diagnoses. Randomized clinical trials (RCTs) in which patients are registered before surgery provide some insight into the error rates of imaging diagnoses. For example, in three RCTs of patients undergoing resection for single metastases, all of whom had single brain lesions and established diagnoses of systemic cancer, 7 of 127 presumed metastases (6%) proved to be other lesions (gliomas, abscesses, or inflammatory changes)^46–48; in a phase II trial involving implantation of carmustine (BCNU)-impregnated wafers for single metastases with presurgical enrollment that did not require a previous history of cancer, 6 of 35 patients (17%) had pathology other than metastasis.⁴⁹ These results are relevant to radiosurgical series for single brain metastases. Nonenhancing cerebral masses are usually thought to represent low-grade gliomas, but up to a third will be malignant gliomas, especially in older patients,⁵⁰ and many low-grade gliomas enhance on magnetic resonance imaging (MRI), a feature that characteristically portends a poorer prognosis.^51,52 An observational study of “low-grade gliomas” that is limited to nonenhancing tumors will therefore include some malignant gliomas and exclude some poor-prognosis low-grade tumors, thus making the combined effect of the two biases difficult to predict. Treatment of malignant gliomas without a histologic diagnosis is less common, but knowing the rate of misdiagnosis based on imaging can assist in power calculations for trials. Of 30 patients enrolled in an RCT of glioblastoma resection based on characteristic images,⁵³ 7 (23%) had nonglioma lesions and another 4 had grade 3 gliomas. In a larger study that required an MRI diagnosis of contrast-enhancing glioma amenable to complete resection for enrollment, 34 of 322 patients (11%) had nonglioma pathology; 97% of the remaining tumors were grade 4 astrocytomas.⁵⁴

Defining the stage of disease (i.e., at diagnosis versus recurrent) is generally straightforward. However, care should be exercised in defining the date of diagnosis in glioblastoma studies that include (or exclude⁵⁵) patients with previous known low-grade tumors (“secondary glioblastomas”). Defining the date of recurrence in treated malignant gliomas is discussed later in the chapter.

Composite Baseline Risk Scores—Recursive Partitioning Analysis and Others

Several specially developed composite baseline risk classifications have found widespread use in reporting treatment of glioma, metastasis, and medulloblastoma. Baseline prognostic classifications or scores are useful in constructing historical control groups for case series without internal controls and for stratifying randomization in RCTs so that both treatment groups will contain patients with comparable prognoses given standard treatment.

For malignant glioma patients, the 1993 Radiation Therapy and Oncology Group (RTOG) recursive partitioning analysis (RPA)⁵⁶ is the most commonly used prognostic classification. This scheme uses patient age, functional status (Karnofsky performance scale [KPS] and work ability), mental status and neurological function, length of symptoms, type of surgery, radiation dose delivered, and tumor histology to define six patient groups with distinctly different survival prognoses. The U.K. Medical Research Council developed and validated a similar six-group classification of malignant glioma patients that has been less used widely,^57,58 and a glioblastoma-specific RPA has also been published.⁵⁹ For patients with low-grade glioma, Pignatti and coworkers identified five baseline prognostic factors (age >40, astrocytoma histology, tumor diameter >6 cm, tumor crossing the midline, and presence of a neurological deficit before surgery) and split patients into two prognostic groups, low risk (two or fewer factors) and high risk (three or more factors).⁶⁰ Brain metastasis patients are classified with another RTOG RPA scheme that uses patient age, presence of metastases outside the brain, and state of control of the primary tumor to define three prognostic groups.^61,62 A 2008 revision of RPA, called graded prognostic assessment (GPA), was developed by the RTOG to be easier to use and less subjective than RPA.⁶³ GPA uses patient age, KPS, number of brain metastases, and the presence of known extracranial metastases to define four prognostic classes, as opposed to three for RPA. The authors found that GPA was as prognostic as RPA and superior to other published prognostic indexes for brain metastasis patients, but GPA has not yet been validated on other data sets. For medulloblastoma, various means of combining baseline risk factors, including age, dissemination, and residual disease after surgery, have been used to construct “average-risk” and “poor-risk” groups.¹⁴

Prognostic indexes are only valid to construct historical control patient sets that share diagnostic, prognostic, and treatment characteristics with the original cohort from which the index was developed, and they should not be used as comparisons for patient cohorts that differ substantially from the original cohort. For example, the RTOG conducted a phase II trial of interferon beta-1a after adjuvant radiation therapy for malignant glioma patients in which eligible patients were those who had stable disease at the completion of radiation treatment.⁶⁴ Initial analyses showed superiority over expected survival with RPA, but the comparison was invalid because patients whose tumors progressed during radiation therapy (a marker for poor prognosis)⁶⁵ were included in the RPA but excluded from the trial (about a third of the original cohort). A matched-pair analysis that accounted for disease stability during radiation therapy showed no difference between survival of interferon-treated patients in the trial and appropriate historical controls. Similarly, malignant glioma trials that stipulate gross total resection for entry (such as many immunotherapy trials) require a special prognostic index constructed from similar patients.⁶⁶ When the standard of care shifts for a disease, as it has recently for malignant glioma because of the introduction of adjuvant temozolomide,⁶⁷ new indexes must be developed.

A basic characteristic of all such prognostic indexes in current use is that they were developed from patients enrolled in prospective clinical trials. In the United States, only 2% to 3% of patients with malignant glioma enter clinical trials (Barker FG, Curry WT, unpublished data). It is well known that trial patients have decidedly favorable prognostic factors in comparison to population-based cohorts because of trial eligibility requirements, self-selection, and social factors.^68–70 Valid historical controls for population-based patient cohorts cannot be generated with the standard trial-based prognostic indexes. Conversely, single-center trial patients usually have even better baseline prognostic factors than do patients in multicenter trials, which partially accounts for the tendency of many therapies that appear promising in early single-center trials to fail in RCTs^70–72; this renders the use of multicenter trial–based prognostic indexes suspect as historical controls for single-center trial reports that use survival end points.

Other Factors

All brain tumor studies should include functional status in baseline descriptions because it is a very strong predictor of both outcome and survival.¹⁰ Commonly used scales include the KPS⁷³ and European Cooperative Oncology Group (ECOG) scale.⁷⁴ For special tumor types, other baseline neurological status needs to be specified. An example is preoperative hearing status for acoustic neuromas, which is usually described in terms of the Gardner-Robertson classification,⁷⁵ a strong predictor of hearing preservation results. This same scale is used for studies of radiosurgical treatment. Many specific neurological measures are more often used to describe complications of surgery (such as facial nerve weakness)⁷⁶ and are described later. Tumor size, location, focality and multiplicity, and proximity to or involvement of eloquent structures all contribute to the concept of “resectability,” an important prognostic factor described subsequently.

In metastasis studies, additional important baseline variables include histology and time since diagnosis of the primary tumor (longer intervals suggest a better outcome),⁷⁷ “expected survival” at enrollment (most surgical trials require a minimum expected survival, although physicians are notoriously bad at such predictions^78–80), and whether the systemic cancer is stable or progressive (a very subjective determination with no known guidelines). Despite their subjective and imprecise nature, all these factors are strong predictors of actual patient survival.

End Points for Cancer Studies

A bewildering variety of end points have been used in cancer outcome studies, many of which are useful in brain tumor reports.^5,100 Careful selection of end points for both efficacy and toxicity is one of the most important aspects of planning a brain tumor trial. Appropriate end points for reports of disease treatment typically shift over time as those treatments improve. For example, in the treatment of acoustic neuroma, operative mortality has been supplemented in turn by complete resection, anatomic facial nerve preservation, good facial function, and hearing preservation as additional important end points, and with the introduction of radiosurgery, tumor stability has been added.¹⁰¹ Because many cancer innovations eventually involve approval of a drug or product by regulatory authorities, the perspective of the U.S. Food and Drug Administration (FDA) on appropriate end points for cancer product approval will be touched on briefly in this chapter as an example.^102–104

Survival, Operative Mortality, and Disease-Specific Mortality

The original criterion for success of brain tumor surgery was operative mortality, and overall patient survival is still the “gold standard” for success of therapy for malignant brain tumors, including low-grade gliomas. A survey of neurosurgeons showed that mortality was considered the most important end point far more often than morbidity or quality of life.¹⁰⁵ Survival (or overall survival) is usually defined as the time from initial surgery, date of diagnosis, or date of trial registration until death. Events (such as loss to follow-up) that will be considered censoring events in survival analysis should be carefully defined.¹⁰⁶ The obvious advantages of overall survival as an end point are its undeniable importance to the patient (within reasonable limits imposed by quality of life) and lack of ambiguity in definition. Governments typically maintain death indices that can be used to obtain follow-up even for patients who have gone missing during the study.^107,108 Survival is an ideal end point for diseases with high acute mortality, such as glioblastoma. In diseases with longer expected survival, trials with survival end points must plan for lengthy follow-up (with an increasing amount of patient dropout over time), and when effective salvage therapies are available, the survival benefit of the initial treatment becomes difficult to distinguish. In these situations, progression-free survival (see later) may be a more attractive end point.

In surgical studies, operative mortality is a short-term end point of importance to many investigators. Cushing, in an era when many patients had prolonged hospitalization after brain tumor surgery, used mortality before hospital discharge as his definition of operative mortality.¹⁷ Later investigators substituted 30-day mortality as the standard measure to capture events occurring soon after hospital discharge (such as death from pulmonary embolism). With the increasing use of administrative databases for brain tumor outcome studies,^109–111 mortality before hospital discharge is again frequently reported by investigators. Use of this end point can introduce bias into analyses. For example, studies showed a substantial decrease in in-hospital mortality for patients undergoing brain tumor craniotomy in the United States from 1988 to 2000.^109–111 However, a Medicare analysis showed that although in-hospital mortality after craniotomy (all types) fell 8.5% between 1998 and 2003, 30-day mortality remained almost unchanged. This strongly suggests that the observed decrease in in-hospital mortality is largely an artifact of decreasing length of hospital stay over the study period.¹¹² In general, when patients with acute conditions remain in the hospital longer (even for administrative reasons), their observed in-hospital mortality will be higher.

In malignant glioma patients, studies show that virtually all deaths result from the primary tumor, so the difference between overall mortality (death from any cause) and disease-specific mortality (death from the brain tumor) is negligible. Although the same holds true for death in patients with neurofibromatosis type 2,¹¹³ for patients who undergo operations for sporadic benign tumors such as acoustic neuromas, meningiomas, or pituitary adenomas, most eventual deaths are not directly related to the brain tumor or its treatment. In fact, there is a bias toward better survival in such patients than in the general population because of preferential selection of healthy patients for surgery, as seen in patients with other benign neurosurgical conditions.^114–116 This bias is related to the “healthy worker” effect in epidemiology—when cases are selected only from a working population, a spurious increase in survival over general population controls is seen.¹¹⁷ In these situations, analysis of overall survival can produce confusing or misleading results. Not surprisingly, patient age at diagnosis is the strongest prognostic factor for survival in studies where few deaths result from the disease under study, such as meningioma.¹¹⁸ Disease-specific survival is the appropriate end point for determining relevant prognostic survival factors in the treatment of benign tumors: in one study of radiation therapy after subtotal resection of benign skull base meningiomas, the 15-year cause-specific survival rate was 92%, but the overall survival rate was just 62%.¹¹⁹

In detailing results, all time-dependent end points, such as survival, completeness of follow-up, and extent of patient dropout, should be reported. Most reports simply state the number of patients lost to follow-up, but when the loss occurred is also important. Better available methods include calculating the maximal theoretically possible follow-up (i.e., all patients monitored from enrollment to the time of analysis) and reporting the actual follow-up achieved as a percentage of the former.¹²⁰ Another method is the “reverse Kaplan-Meier” method in which the indicator for events and censoring is reversed.¹²⁰ Loss to follow-up in survival studies is usually nonrandom. Younger patients are more likely to be lost, as are those who have experienced a complication of therapy or other poor outcome,^114,121,122 so including only patients with complete follow-up can bias a study toward better results.

Progression-Free Survival, Time to Progression, and Time to Treatment Failure

In many brain tumor treatment studies, progression-free survival is an important end point. It typically reflects a period of relative freedom from symptoms for the patient, in addition to being an excellent surrogate for overall survival.^123,124 However, defining tumor progression in brain tumors (especially malignant gliomas) is complex and subjective; definitions of tumor progression and response are discussed later in this chapter. Progression-free survival starts at diagnosis, surgery, or trial enrollment and ends at either tumor progression or death. In contrast, time to progression ends only when tumor progression is actually observed (typically with an imaging study). The difference is important because many patients fail to keep clinical or imaging appointments when their tumors recur, so tumor progression is never formally demonstrated before death. Such patients are censored observations in time-to-progression analyses, which limits the usefulness of this end point. Similarly, “time to treatment failure” ends either at death, at tumor progression, or when treatment is stopped for any other reason, such as toxicity or patient choice. This is only rarely a useful end point in brain tumor trials because efficacy and toxicity are generally better assessed separately.

Other Time-Dependent End Points

Besides the survival measures already discussed, the concepts of independent or functional survival, time to neurological progression, and neurological death are important in some brain tumor trials. Definitions of functional or independent survival and time to neurological progression are highly dependent on the functional status scale and definition of neurological symptoms used; these scales are discussed later. Neurological death is an end point used in some brain metastasis trials.^46,125 In surgical or radiosurgical trials, it reflects cause-specific mortality for the treated lesion or lesions or from other, new brain metastases.

Quality of Life, Functional Status, and Measures of Symptoms

In recent years there has been increasing emphasis on patient-reported outcomes (PROs) in cancer therapy in addition to length of survival.^{105,126–133} PROs include, in order of increasing specificity, health-related quality of life (HRQoL), functional status, and measures of specific neurological deficits. In the WHO framework for discussing health-related problems, decreases in overall HRQoL correspond roughly to “handicap,” deficits in functional status to “disability,” and specific symptoms to “impairment.” In the United States, the increasing value placed on PROs is reflected by their acceptance by the FDA as supporting evidence in the approval process for a growing number of anticancer drugs.¹²⁸

Methods for measuring HRQoL are still in the course of development, and specialized texts are available.^5,100,134 HRQoL is a measure of an individual’s satisfaction with life insofar as it is affected by health. It excludes many domains of “quality of life” defined generally, such as financial security, political and religious freedom, quality of interpersonal relationships, and so on, except as they are affected by the person’s health status. Generic HRQoL measures are applicable to all individuals regardless of health state; such measures include familiar instruments such as the short form health survey SF-36 (www.sf-36.org). Other measures are designed to capture HRQoL in cancer patients generally, such as the Functional Assessment of Cancer Therapy—General version (FACT-G; www.facit.org) and the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ) C30 (www.eortc.be/home/qol/ExplQLQ-C30_1.htm). The highest level of specificity is achieved by HRQoL measures designed specifically for brain tumor patients, such as the brain tumor subscale of the FACT-G, called the FACT-BR,¹³⁵ and the EORTC QLQ Brain Cancer Module (BN 20).^136,137 HRQoL and cognitive functioning are strong predictors of survival in glioma patients, both at diagnosis^136–139 and at recurrence.¹⁴⁰ Most large trials of brain metastasis treatment now report HRQoL or cognitive function measures, or both,¹²⁶ and HRQoL reports in other tumor types such as meningioma,^141–143 acoustic neuroma,^131,133,144 and some types of pituitary adenoma^145,146 are becoming increasingly common. Such studies sometimes use disease-specific HRQoL scales that have not been developed or validated with rigorous psychometric methods.

Despite their obvious merits, HRQoL and cognitive function have been tricky end points in brain tumor therapy trials for several reasons. The main obstacle has been the expense and difficulty of administering the instruments themselves. Combined with the declining functional status seen in the late stages of many brain tumor patients’ lives, this causes missing data to be a frequent problem in HRQoL analyses. HRQoL data tend not to be missing at random, but selectively for patients who are doing poorly—thereby falsely inflating the HRQoL of the remaining compliant population. Caregivers can be asked to rate HRQoL for patients under these circumstances (proxy rating). The reliability of proxy ratings as a surrogate for PROs is poor, especially in patients with cognitive deficits, but they are preferable to missing data.^147,148 Other problems have included difficulty defining the minimum clinically significant change in HRQoL measures (as opposed to the smallest statistically significant change) and the possibility that some supportive medications commonly used in brain tumor patients (such as antiseizure medications and corticosteroids) can interfere with test validity.¹⁰² To date, HRQoL has not been used by the FDA as the primary evidence for approving any anticancer product, although HRQoL data are often included in applications.^127,128

Functional status is an individual’s capacity to perform the normal activities of daily living; for adults this includes self-care and work. The oldest and most commonly used measure of functional status in cancer studies is the KPS (Table 113-2);⁷³ a closely allied scale is the ECOG performance scale, which is very similar to the WHO and Zubrod scales (Table 113-3).⁷⁴ Performance status is an important prognostic factor for survival in brain tumor patients, and most prospective studies include performance status as a baseline variable, usually with a minimum score required as an entry criterion for the study (e.g., KPS score of 70). Examining the KPS itself, it is clear that the difference between some levels is subjective and can be difficult to assign reliably, especially the highest levels of the scale. For example, a history of a single seizure curtails driving in most jurisdictions even if the seizures are controlled; whether such a patient can be assigned a KPS score of 100 (normal) or even 90 (capable of normal activity) is ambiguous. In a general cancer practice setting, interrater agreement on the KPS is generally good; in one study, raters agreed on KPS scores 59% of the time and disagreed by one level (10 points) 32% of the time, with 7% of ratings disagreeing by 20 or 30 points.¹⁴⁹ In brain tumor patients the KPS score is highly correlated with age and is sensitive to depression; it is most sensitive to changes in HRQoL in glioma patients for KPS scores in the 50 to 80 range and performs less well for higher scores.¹⁵⁰ KPS scores can be difficult to assign in retrospective studies. They are also highly confounded by the extent of resection in glioma studies, probably reflecting a combination of relief of mass effect from extensive resections with deficits caused primarily by unresectable tumors involving eloquent areas. Despite these drawbacks, KPS and other functional status scores are widely used in reporting baseline status in patient cohorts and in expressing immediate improvement or deterioration after brain tumor surgery by comparing presurgical and postsurgical scores.¹⁵¹ In longer term outcome studies, the KPS is used for defining “independent” or “functional” survival, and a 10-point decline in KPS score has been shown to correlate with increased risk for death in glioma patients.¹⁰

TABLE 113-2 Karnofsky Performance Scale

From Karnofsky DA, Burchenal JH. The clinical evaluation of chemotherapeutic agents in cancer. In: MacLeod CM, ed. Evaluation of Chemotherapeutic Agents. New York: Columbia University Press; 1949:196.

TABLE 113-3 Eastern Cooperative Oncology Group Performance Scale

From Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5:649-655.

Cognitive function has already been mentioned in the context of HRQoL^140,152 and is most often measured with the Mini-Mental Status Examination (MMSE).^153,154 A multitude of other measures are available to express changes in neurological function generally or in specific functions.⁴ For example, after acoustic neuroma surgery, the Glasgow Benefit Inventory can be used to measure otolaryngologically related health status.¹⁵⁵ More specifically, facial nerve function can be graded by using the House-Brackmann scale,⁷⁶ hearing by using the Gardner-Robertson classification,⁷⁵ tinnitus by using the Tinnitus Handicap Inventory,¹⁵⁶ and vertigo by using the Dizziness Handicap Inventory.¹⁵⁷ After glioma surgery, the National Institutes of Health (NIH) Stroke Scale,^158–161 which measures 15 aspects of neurological function, has been used to detect new neurological deficits.⁵⁴ Other investigators have used less formal criteria, such as improved/stable/worsened motor function¹⁶² or detailed listings of adverse events,¹⁶³ to express neurological damage from glioma surgery quantitatively. Although the NIH Common Toxicity Criteria (www.fda.gov/cder/cancer/toxicityframe.htm