Clinical Oncology (2004) 16: 332e338

doi:10.1016/j.clon.2004.03.006

Original Article

Prognostic Factors in Brain Metastases: Can WeDetermine Patients Who Do Not Benefit from

Whole-brain Radiotherapy?

M. Lock*, E. Chowy, G. R. Pondz, V. Dox, C. Danjouxy, R. Dinniwell{, J. Leak, A. Bezjak{)

*Department of Radiation Oncology, London Regional Cancer Centre, University of Western Ontario,

London, Ontario, Canada; yDepartment of Radiation Oncology, TorontoeSunnybrook Regional Cancer Centre,

University of Toronto, Toronto, Ontario, Canada; zDepartment of Biostatistics, Princess Margaret Hospital,

University of Toronto, Toronto, Ontario, Canada; xDepartment of Radiation Oncology, Radiation Oncology Network,

Westmead Hospital, Sydney, Australia; {Department of Radiation Oncology, Princess Margaret Hospital,

University of Toronto, Toronto, Ontario, Canada; kFaculty of Medicine, University of Toronto,

Toronto, Ontario, Canada

ABSTRACT:Aims: Whole-brain radiotherapy (WBRT) is a standard treatment recommendation for patients with brain metastases. The goal of treatmentis symptom control, which in the short run can be often achieved by steroids. Patients with a short life expectancy may not derive benefitfrom the addition of radiation. The ability to identify this group would aid the decision of whether to recommend WBRT.Materials and methods: Data on all patients referred for WBRT to palliative radiotherapy teams at two comprehensive cancer centres wereprospectively collected over a 2-year period. The most frequent radiation dose and fractionation was 2000 cGy in 5 fractions over 1 week.Multivariate logistic regression analysis using forward stepwise selection process was used to develop a prediction model for early death(before 8 weeks). The variables considered were sex, performance status, primary disease, weight loss, age, time from primary diagnosis tobrain metastases diagnosis, number of metastatic sites and Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis(RPA) status.Results: Two hundred and seventy-five patients with brain metastases were assessed. The median follow-up was 19 weeks, and estimatedmedian overall survival was 5.3 months. Multivariate regression revealed Eastern Cooperative Oncology Group (ECOG) performancestatus and number of metastatic sites to be significant predictors of early death. The odds ratios were 2.38 (95% confidence interval [CI]1.77e3.19) and 1.39 (95% CI 1.07e1.81), respectively. Sixty-eight per cent of patients could be correctly classified; however, 55% wouldhave been incorrectly predicted to die early.Conclusions: Poor performance status and number of metastatic sites are useful predictors of early death. A regression model was highlypredictive overall, but this was primarily due to a high negative predictive value of 86%. The ability to predict patients who would die early( positive predictive value) was only 45%. Thus, despite the understanding that we are over-treating a subset of patients, further research isrequired to identify patients who do not require radiotherapy. Lock, M. et al. (2004). Clinical Oncology 16, 332e338

� 2004 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Key words: Brain metastases, prognostic factors, radiotherapy

Received: 6 June 2003 Revised: 22 February 2004 Accepted: 12 March 2004

Introduction

Brain metastases occur in about 25% of people with cancer[1]. The incidence may be increasing because of improveddetection by imaging and longer survival resulting fromimproved therapies for the primary disease [1e3]. Whole-brain radiotherapy (WBRT) is the standard treatmentrecommended to patients with brain metastases [4,5]. More

Author for correspondence: Andrea Bezjak, Department of Radiation

Oncology, Princess Margaret Hospital, 5th Floor, 610 University Avenue,

Toronto, Ontario, M5G 2M9 Canada. Tel: D416-946-2132; Fax: D416-

946-4586. E-mail: andrea.bezjak@rmp.uhn.on.ca

0936-6555/04/000000C07 $35.00/0 � 2004 The Roy

aggressive interventions (surgical resection, stereotacticradiation) benefit only a subset of this population (e.g.patients with a single small metastasis and good per-formance status). For most patients who present withmultiple brain metastases, the goal of treatment is pallia-tion. Specifically, the main expectations of therapy are animprovement in presenting neurological symptoms andprevention of neurological deterioration. A rapid improve-ment in neurological symptoms is usually seen when cor-ticosteroid therapy is instituted. In this setting, the furtheradditive palliative benefit of WBRT cannot be appreciatedimmediately. Additive benefits are discernible only after 1or 2 months when steroids have been tapered and stopped.

al College of Radiologists. Published by Elsevier Ltd. All rights reserved.

333PROGNOSTIC FACTORS IN BRAIN METASTASES

Radiation is considered to have been beneficial if at thattime the patient continues to maintain the neurologicalbenefit. This is particularly desirable in patients whose lifeexpectancy is beyond a few months, as continued de-pendence on high-dose steroids could have led to poten-tially debilitating side-effects, such as proximal myopathy.In patients with shorter life expectancies, steroids withoutradiation may provide equivalent or even better palliation,by avoiding the side-effects and treatment time of radiation.

Determining meaningful end points in this patient popu-lation is difficult. Improvement or maintenance of the qualityof life end point represents the primary goal. However, moststudies do not include this end point, probably because of theinherent difficulty in defining and collecting these data frompalliative and symptomatic patients. Ongoing developmentof tools such as the EORTC QLQ C30 brain module, FACT-Br and proxy quality-of-life measures represents importantsteps in the investigation of brain metastases treatment ifthey can be validated and implemented uniformly [6e8]. Inpractice, data collection in this patient population is in-complete, and other surrogate clinical end points are used.Objective end points, such as reduction in size of metastases,improvement in neurologic signs and reduction in steroiddose, have shown the greatest use and success [9e12]. Theissue of steroids is of particular importance, as the responseto steroids has often not been distinguished from the re-sponse to radiation [13]. In a randomised study comparingthe use of steroids alone with combination plus WBRT,WBRT offered only slightly better survival and duration ofremission than steroids alone [14]. This supports the viewthat some patients would have an improved quality of lifewithout WBRT. However, use of diverse measures canresult in difficulty in comparing studies and interpretingresults. A more detailed exploration of this issue is reportedelsewhere by this study’s senior author [13].

The proportion of patients with multiple-brain metasta-ses and a short life expectancy is remarkably high. In areview of the Radiation Therapy Oncology Group (RTOG)database, Gaspar et al. [15] showed that as many as 40% ofhigh-risk patients do not survive more than 2 months. Datafrom a multicentre randomised-control study carried out bythe Royal College of Radiologists revealed that one-third ofpatients died within a month of starting treatment for brainmetastases [16]. Similarly, our group reported that, in acohort of moderate-to-poor prognosis patients with brainmetastases, only 19% showed definitive improvement at 1month after WBRT, and 55% had progression of symptomsor died by 1 month [17]. Thus, the literature suggests thata significant proportion of patients die within a period oftime that is too short to experience the additional palliativebenefit of radiotherapy. The only exception may be patientswho did not respond to steroids. In these cases, neurolog-ical improvement may be seen with WBRT within thatperiod of time; this improvement would not be expected inthe absence of radiation treatment. In our study of symp-tom assessment in patients with brain metastases, 84%of patients referred for palliative WBRT had symptom im-provement with steroids; three out of five patients with noresponse to steroids showed some symptom improvement

after radiotherapy. The ability to predict the chance of anearly death (‘early’ is defined by the period that radio-therapy would be unlikely to benefit) would assist themanagement decision of whether to recommend whole-brain radiotherapy.

Prognostic indices are available to aid in the identificationof patients with brain metastases who have different lifeexpectancies. In 1997, the RTOG classified patients intothree classes (Table 1) based on recursive partitioninganalysis (RPA) [15]. This provided a tool to identify betterprognosis (median survival 7.1 months), intermediate prog-nosis (median survival 4.2 months) and poorer prognosis(median survival 2.3 months) patients. Other studies [18]have used different methods to identify prognostic groups inorder to determine appropriate treatment. However, thesereviews and the RTOG RPA classification system do notspecifically allow us to identify individuals who are unlikelyto live long enough to benefit from WBRT. Indeed, many ofthese tools were primarily developed to identify favourablepatient subsets that would benefit from more aggressivetreatment [19,20]. Therefore, the primary objective of ourstudy was to determine if there are variables that identifypatients with brain metastases who die within 8 weeks ofassessment. These patients may be seen as unlikely to benefitfrom the addition of whole-brain radiation to corticosteroids.The ultimate objective would be to develop a practicalbedside model to aid the decision of whether to recommendWBRT in a group of patients with a poor outcome.

Methods

Patients

We identified all patients referred for whole-brain radiother-apy to palliative radiotherapy teams at two separateinstitutions in a 2-year period, between June 1998 and June2000. The institutions are the PrincessMargaret Hospital andthe TorontoeSunnybrook Regional Cancer Centre. Bothcentres are tertiary cancer centres that provide radiationtherapy to the Greater Toronto Area. All patients hada diagnosis of brainmetastases confirmedby either computedtomography or magnetic resonance imaging. Patients witha primary diagnosis of lymphoma or leukaemia, or who hadpreviously received radiotherapy to the brain, were excluded.In all cases, radiotherapy was given 5 days aweek using 60Cogamma rays or 4- to 6-MV photons from a linear accelerator.The most frequent dose and fractionation was 2000 cGy in 5fractions given over 1 week. Six per cent of patients received3000 cGy in 10 fractions over 2 weeks. The remaining 6% ofpatients received shorter radiotherapy regimens, including800 cGy in 2 fractions, 1200 cGy in 3 fractions and 1600 cGyin 4 fractions. Corticosteroids were given before and duringradiotherapy. Corticosteroids were then tapered after com-pletion of radiotherapy.

Source of Data

Both palliative radiotherapy programmes maintain a pro-spective database that includes demographic, disease and

334 CLINICAL ONCOLOGY

treatment characteristics of all the patients seen and treated;these databases were the source of data extraction. Datawere collected by the oncologist or a palliative care nurse.Survival data were obtained from the hospital medicalrecords and from the provincial cancer registry.

Statistics

Logistic regression was used to determine which variableswould differentiate those patients who die an early death.Early death was operationally defined as dying within8 weeks of consultation. Sex, whether the patient had alung primary (yes/no), weight loss O10% (yes/no) andpresence/absence of brain metastases at diagnosis wereanalysed as categorical variables. Eastern CooperativeOncology Group (ECOG) performance status, number ofmetastatic sites, age, brain metastases disease-free interval(time from primary diagnosis to diagnosis of brain meta-stases) and RTOG RPA status (Table 1) were analysed ascontinuous variables. Therefore, the total number ofdegrees of freedom is eight. Given a sample size of 275patients, this provides sufficient statistical power to detectany significant clinical predictors of early death. Thevariable ‘brain metastases disease-free interval’ is definedas the time from primary diagnosis to diagnosis of brainmetastases. For example, if a patient is diagnosed with ametastatic brain lesion 1 year after the diagnosis of lungcancer, then the patient has experienced a brain metastasesdisease-free interval of 1 year. If the diagnosis of the brainlesion occurs on initial work-up of a new lung cancer, thenthe disease-free interval is defined as zero. The variablecalled ‘number of metastatic sites’ refers to the areas oranatomical sites of metastases, excluding the primarydisease site. For example, if a patient had metastases inthe ribs, spine and liver, the patient would be scored as twometastatic sites (bone, liver). The variable ‘weight loss’was converted to a binary variable by defining significantweight loss as a decrease of greater than 10% of initialbody weight in the past 6 months.

Multivariate analysis using forward stepwise selectionprocess was used to develop an ‘optimal’ prediction index/model. The estimated probability of surviving at least 8

Table 1 e RTOG recursive partitioning analysis

Class Variables

Median survival

(months)

Class I !65 years of age; KPS R70;

controlled primary tumour;

metastases to brain only

7.1

Class II KPS R70, but uncontrolled

primary tumour; KPS R70,

primary controlled, but age R65;

KPS R70, primary controlled,

age !65, but metastases to brain

and other sites

4.2

Class III KPS !70 2.3

KPS, Karnofsky performance status.

weeks was calculated for each patient based on the multi-variate model. A decision rule was developed, such that allpatients with an estimated probability of 8-week survivalgreater than a selected threshold value were classified as‘predicted to survive’. Patients whose probability waslower than this threshold were ‘predicted to not survive’.Specificity and sensitivity were then calculated for allpossible probability values. The cut-off value that was usedin the final analyses was the value which maximised thesum of specificity and sensitivity.

As the 8-week survival cut-off is an arbitrary threshold,Cox proportional hazards models were also developed toinvestigate variables predictive of an increased patient riskof dying at any time. All tests were two-sided, and a resultwas considered statistically significant if the P value wasless than 0.05. Analysis was conducted using SAS version8.2 (SAS Institute Inc., Cary, NC, USA).

Results

Patient Characteristics

Two hundred and seventy-six consecutive patients withbrain metastases treated at the two palliative radiationprogrammes were included in the study (Table 2): 199 fromthe Princess Margaret Hospital Palliative Radiation Oncol-ogy Program (PROP) and 77 from the TorontoeSunny-brook Regional Cancer Centre Rapid Response RadiationProgram. One patient from the TorontoeSunnybrookRegional Cancer Centre did not have any data collectedand was excluded. Data on the remaining patients werecomplete; in other words, there were no missing datapoints. The median follow-up was 19 weeks (range 0e108weeks). All surviving patients were followed beyond8 weeks; thus, no patient was censored before the 8-weekcut-point. There was no loss to follow-up, and the medianfollow-up of patients alive at last follow-up was 35 weeks.The estimated median overall survival was 5.3 months,with 28.0% of patients dying by the 8-week cut-off. Therange of survival was 0e755 days. A total of 37.5% of

Months

Surv

ival

Pro

porti

on

0 2 4 6 8 10 12 14 16 18 20 22 24

1.0

0.8

0.6

0.4

0.2

0.0

Predicted to survive 8 weeksPredicted not to survive 8 weeks

Fig. 1 e Survival curves of individuals predicted to survive8 weeks compared with those predicted not to survive 8 weeks.

335PROGNOSTIC FACTORS IN BRAIN METASTASES

patients were alive at the time of this analysis. All patientswere accounted for at the end of the study.

Most patients had a primary diagnosis of lung cancer(67%) or breast cancer (11%). Brain metastases as thepresenting diagnosis occurred in 32% of patients. TheRTOG RPA status was class III for 185 (67.3%) patients.Characteristics of patients dying before and after 8 weeksare listed in Table 3.

Logistic Regression

The highest odds ratio for predicting patient death within8 weeks was 4.21 (P! 0:001; 95% CI 2.12e8.34) forRTOG RPA status (Table 4). ECOG performance statusand weight loss had odds ratios of 2.28 (P! 0:001; 95% CI1.71e3.04) and 2.30 (P ¼ 0:009; 95% CI 1.23e4.27),respectively. No other variable was a statistically signifi-cant predictor of early death (Table 5), although age (OR1.02; P ¼ 0:062; 95% CI 1.00e1.05) and number ofmetastatic sites (OR 1.26; P ¼ 0:058; 95% CI 0.99e1.61)both approached statistical significance. In multivariatelogistic regression analysis, ECOG performance status(OR 2.38; P! 0:001; 95% CI 1.77e3.19) followed bynumber of metastatic sites (OR 1.39; P! 0:015; 95% CI1.07e1.81) entered the model. Weight did not enter into themultivariate model, although the P value was 0.053 afteradjusting for both ECOG performance status and number ofmetastatic sites. RTOG RPA class was not entered in thisanalysis, as RTOG class is a composite of variables thatwere entered separately.

Table 2 e Patient characteristics

Parameter Number %

Male : female ratio 141 : 134 51 : 49

Died before : after 8 weeks 77 : 198 28.0 : 72.0

Number of metastatic sites

(including brain metastases)

(1) 158 (1) 57.5

(2) 75 (2) 27.3

(3) 29 (3) 10.6

(4e7) 13 (4e7) 4.4

Lung primary 183 67

Greater than 10% weight loss 54 19.6

ECOG performance status (0) 14 (0) 5.1

(1) 101 (1) 36.7

(2) 75 (2) 27.3

(3) 66 (3) 24.0

(4) 19 (4) 6.9

Brain metastases at diagnosis 87 31.6

Median age (range) 65 years (26e84 years)

RTOG RPA classification (I) 14 5.1

(II) 76 27.6

(III) 185 67.3

ECOG, Eastern Cooperative Oncology Group; RPA, recursive partitioning

analysis; RTOG, Radiation Therapy Oncology Group.

Development of a Predictive Model

The sum of specificity and sensitivity was maximised whena threshold value of 0.253 was used. The sensitivity was71.4% and specificity was 66.7% for this model. Using thisas a cut-off, survival status was correctly predicted for 187(68.0%) patients (Table 6). This model predicted 154patients to survive for at least 8 weeks, of whom only 22(14.3%) died. Thus, the model has a negative predictivevalue of 86%. However, the model predicted 121 patientsto die before 8 weeks and 66 (54.6%) of these patientssurvived beyond 8 weeks (Fig. 1). If a higher probabilitywas used as the cut-off, the positive predictive value wouldincrease at the expense of a decrease in the negativepredictive value.

Multivariate Model Incorporating RTOG RPA Class

All 14 patients in RTOG class I survived beyond 8 weeks;66 out of 76 (86.8%) in class II survived beyond 8 weeksand 118 out of 185 (63.8%) in class III survived beyond 8weeks (Fig. 2). Patients in RTOG RPA class III have thepoorest predicted survival, and are the least likely to benefitfrom radiation. Therefore, a multivariate model incor-porating only this RTOG RPA class was developed. As inthe entire group, the ECOG performance status and numberof metastatic sites were the only variables predictive of earlydeath for this subgroup. Once again, weight loss approachedsignificance. The multivariate logistic regression modelpredicted that 106 patients would survive at least 8 weeks.Twenty-six (24.5%) of these patients died before the 8-weekcut-off. Of the 79 patients predicted to die before 8 weeks,38 (48.1%) survived at least 8 weeks.

Table 3 e Characteristics of patients dying before and after 8 weeks

Parameter

Before

8 weeks

After

8 weeks

Male : female ratio 42 : 35 99 : 99

Median number of metastatic sites 2 1

Primary disease site

Unknown 9% 7%

Genitourinary 3% 7%

Melanoma 3% 3%

Lung 69% 66%

Gastrointestinal 8% 5%

Breast 5% 13%

Other 4% 0.5%

Weight loss O10% 30% 16%

ECOG performance status 0 or 1 17% 52%

Median age 66 years 65 years

RTOG RPA classification (I) 0% 7%

(II) 13% 33%

(III) 87% 60%

ECOG, Eastern Cooperative Oncology Group; RPA, recursive partitioning

analysis; RTOG, Radiation Therapy Oncology Group.

336 CLINICAL ONCOLOGY

Table 4 e Results of univariate logistic regression

Variable

Logistic regression

odds ratio P value

95% Confidence

interval

Sex (male) 1.20 0.50 0.71, 2.04

ECOG performance status 2.28 !0.001 1.71, 3.04

Number of metastatic sites 1.26 0.058 0.99, 1.61

Lung primary 1.16 0.62 0.66, 2.03

Weight loss >10% 2.30 0.009 1.23, 4.27

Age 1.02 0.062 1.00, 1.05

Brain metastasis at initial diagnosis 0.75 0.33 0.42, 1.34

Disease-free interval (initial diagnosisebrain

metastasis, in years)

0.94 0.27 0.84, 1.05

RTOG status 4.21 !0.001 2.12, 8.34

ECOG, Eastern Cooperative Oncology Group; RTOG, Radiation Therapy Oncology Group.

Cox Proportional Hazards Regression Model

The Cox proportional hazards regression model indicatedthat a higher ECOG performance status (hazard ratio[HR]Z 1.40; P! 0:001; 95% CI 1.21e1.63), weight lossof greater than 10% (HR ¼ 1:55; P ¼ 0:019; 95% CI1.07e2.23) and higher RTOG RPA class status (HR ¼1:63; P! 0:001; 95% CI 1.22e2.18) were statisticallysignificant predictors of increased risk of death in theunivariate analysis. In the multivariate model, the numberof metastatic sites was also not statistically significant(P ¼ 0:13) after adjusting for both ECOG performancestatus and weight. The results of the Cox analysis weresimilar to those of the logistic analysis for both univariateand multivariate cases of all patients and for the subgroupof RTOG RPA class III patients. ECOG performance statuswas highly predictive of early death, and an increasedrisk of dying; weight loss and number of metastatic siteswere somewhat predictive of early death and an increasedrisk of dying. However, although 8 weeks is an arbitrarycut-off, the similarity of the results from these analysesindicates that, if another time was chosen as the cut-offto define early death, the results would probably becomparable.

Discussion

Why Develop a New Model?

The RTOG RPA classification system provides an im-portant tool to determine the prognosis of patients withbrain metastases. Developed from a large database, theRPA has been validated in several studies [21e23].Although it is useful for identifying patients with betterprognosis, a large proportion of patients in clinical practiceare RPA class III. Within that large group, the RPA doesnot help to identify patients with particularly poor prog-nosis, who perhaps should not be offered radiotherapy. Inan attempt to better document the palliative benefit ofWBRT at Princess Margaret Hospital, a longitudinalobservational prospective study [8] revealed that, 1 monthafter completing WBRT, 55% of patients had progressionof symptoms or died. Only 19% had either improvement orresolution of their presenting symptoms. The authorsconcluded that many patients might not benefit from evenshort duration WBRT schedules. There is no clear survivalbenefit of WBRT, and most oncologists recommend treat-ment principally for symptom control. In North America,the incidence of brain metastases ranges from 97 800 to170 000 [24]. As many as 50 000 patients each year could

Table 5 e Results of the Cox proportional hazards regression

Variable

Cox regression

hazard ratio P value

95% Confidence

interval

Sex (male) 1.22 0.21 0.90, 1.64

ECOG performance status 1.40 !0.001 1.21, 1.63

Number of metastatic sites 1.10 0.15 0.96, 1.26

Lung primary 1.29 0.13 0.93, 1.79

Weight loss >10% 1.55 0.019 1.07, 2.23

Age 1.01 0.26 1.00, 1.02

Brain metastasis at initial diagnosis 0.87 0.40 0.63, 1.21

Disease-free interval (initial diagnosisebrain

metastasis, in years)

0.96 0.15 0.90, 1.02

RTOG status 1.63 !0.001 1.22, 2.18

ECOG, Eastern Cooperative Oncology Group; RTOG, Radiation Therapy Oncology Group.

337PROGNOSTIC FACTORS IN BRAIN METASTASES

Table 6 e A comparison of the predictions based on the two-variable regression models and the actual survival of patients

Observed Totals

Less than

8 weeks survival

Greater than

8 weeks survival

Predicted by two-variable

regression models*

Less than 8 weeks survival 55 66 121 PPV 45%

Greater than 8 weeks survival 22 132 154 NPV 86%

Totals 77 198 275

Sensitivity 71.4%, Specificity 66.7%

NPV, negative predictive value; PPV, positive predictive value. *Variables: ECOG performance status, number of metastatic sites.

possibly be spared the time for treatment and recovery fromthe side-effects of radiotherapy. The important radiationoncology question is, ‘can we identify this poor prognosticgroup?’ In an area of active investigation and clinicalresearch, this question is not currently addressed.

Current Model

The variables analysed in this study were chosen a prioribased on a review of the literature. Variables that wereeasily obtainable from a standard history were of particularinterest. Variables requiring time-consuming investigationsor phlebotomy may add undue hardship for the patient andwould limit the generalisability of the envisioned bedsidemodel. Our analysis reveals that patients with a poorperformance status were more than twice as likely to dieearly. An increase in the number of metastatic sites alsoincreased the odds of dying early. A model based on themultivariate logistic regression analysis with these twovariables alone has a sensitivity of 71% and a specificity of67%. These two variables correctly predicted 68% of casesoverall. However, this model incorrectly identified 66 outof 121 (55%) patients to die within 8 weeks, but whoactually survived beyond this period. Thus, despite the highnegative predictive value if this model was implemented,WBRT would not have been recommended to a subset of

RTOG 1RTOG 2RTOG 3

Months

Surv

ival

Pro

porti

on

1086420 12 14 16 18 20 22 24

1.0

0.8

0.6

0.4

0.2

0.0

Fig. 2 e Survival curves divided by Radiation Therapy OncologyGroup recursive partitioning class.

patients that would have potentially benefited from WBRT.The other four variables did not provide statistically signif-icant predictive value to the analysis. Age, which was animportant variable in the RPA, approached but did notreach statistical significance.

Is the RTOG RPA Sufficient to Identify PatientsWho Benefit from WBRT?

A second logistic regression model was developed usingonly patients in RPA class III. Patients in RTOG classes Iand II were assumed to be good candidates for WBRT. Thisimproved the predictive ability, but the concern is that 38(48.1%) patients were still incorrectly predicted to die. Inother words, the model would predict that these patientswould die before the beneficial period and they would nothave been recommended to receive WBRT. Yet, thesepatients did survive 8 weeks and could have benefited fromWBRT. In addition, the use of any model constructed fromthe analysis of a single study will result in over-optimisticestimates of efficiency compared with later practical use[25,26]. Thus, the goal of developing a bedside tool todetermine which patients should be considered for thiscommon treatment cannot be determined to a sufficientlyhigh degree from the clinical variables entered into thisanalysis alone.

Weight loss was a statistically significant predictor ofincreased risk of patient death at any time, and approachedsignificance as a predictor of death within 8 weeks, evenafter adjusting for other significant variables. This was truefor all patients and for the subgroup of RTOG RPA class IIIpatients. The trend towards statistical significance of weightloss suggests that a decision rule based on RTOG RPAalone can be enhanced to address the 8-week question byincorporating other variables. Several other variables werecollected, but could not be entered into analysis, as theywere not collected uniformly between centres. For exam-ple, variables, such as whether the primary was controlledand the number and size of brain metastases, were notuniformly collected. Greater uniformity of data collectionbetween centres will allow evaluation of these variables.These, and other characteristics, may improve the pre-dictive ability of this model, and a clinically acceptabledecision rule might then be constructed. Possible additionalvariables include the size, location, functional imaging andnumber of brain metastases [18,27e31]. However, many of

338 CLINICAL ONCOLOGY

these variables have come to light in studies, such asradiosurgery studies, which aim to identify the patientswith the best natural history. Factors, such as the degree ofimprovement on steroids and progressive extracranialdisease, may provide additional prognostic value to thispoor risk group. Possible, future research directions alsoinclude studying quality of life for RPA class III patients onsteroids randomised to early versus deferred WBRT.

Conclusions

Poor performance status and the number of metastatic siteswere significant predictors of early death in our patientpopulation using the multivariate logistic regression model.These variables are consistent with studies such as theRTOG RPA, and were able to produce a sensitive andspecific model. However, these studies and this model couldnot reliably identify a subset of patients whose survival is sopoor that they could avoid WBRT. We believe that theremay be uncollected variables that will be useful inidentifying those at risk for early death. Variables obtainedby new techniques, such as functional imaging ( positronemission tomography and functional magnetic resonanceimaging) [32] may prove to have prognostic value. Theaddition of these variables into the current analysis mayimprove the predictive ability. A practical model could bedeveloped to aid the decision of whether to recommendwhole-brain radiotherapy for those referred for treatment ofbrain metastases. Thus, despite the understanding that weare over-treating a subset of patients, further research isrequired to allow us to identify these patients.

Acknowledgements. This work was supported in part by the AllanKerbel Trust Fund of the Princess Margaret Hospital Foundation.

References

1 Johnson JD, Young B. Demographics of brain metastases. NeurosurgClin North Am 1996;7:337e344.

2 Paterson AH, Agarwal M, Lees A, et al. Brain metastases in breastcancer patients receiving adjuvant chemotherapy. Cancer 1982;49:651e654.

3 Pieterman RM, Que TH, Elsinga PH. Comparison of (11) C-cholineand (18) F-FDG PET in primary diagnosis and staging of patients withthoracic cancer. J Nucl Med 2002;43:167e172.

4 Coia LR, Aaronson N, Linggood R, et al. A report of the consensusworkshop panel on the treatment of brain metastases. Int J RadiatOncol Biol Phys 1992;23:223e227.

5 Paszat L, Shenouda G, Blood P, et al. The role of palliative radio-therapy for brain metastases. Can J Oncol 1996;6(suppl 1):48e53.

6 Osoba D, Aaronson NK, Muller M, et al. The development andpsychometric validation of a brain cancer quality-of-life questionnairefor use in combination with general cancer-specific questionnaires.Qual Life Res 1996;5:139e150.

7 Weitzner MA, Meyers CA, Gelke CK, et al. The functional assessmentof cancer therapy (FACT) scale. Development of a brain subscale andrevalidation of the general version (FACT-G) in patients with primarybrain tumours. Cancer 1995;75:1151e1161.

8 Sneeuw KC, Aaronson K, Sprangers MA, et al. Evaluating the qualityof life of cancer patients: assessments by patients, significant others,physicians and nurses. Br J Cancer 1999;81:87e94.

9 Miller AB, Hoogstraten B, Staquet M, et al. Reporting results of cancertreatment. Cancer 1981;47:207e214.

10 Order SE, Hellman S, von Essen CF, et al. Improvement in quality ofsurvival following whole-brain irradiation for brain metastases.Radiology 1968;91:149e153.

11 Van der Steen-Banasik E, Hermans J, Tjho-Heslinga R, et al. Theobjective response of brain metastases on radiotherapy: a pros-pective study using computer tomography. Acta Oncol 1992;31:777e780.

12 Epstein BE, Scott CB, Sause WT, et al. Improved survival duration inpatients with unresected solitary brain metastasis using acceleratedhyperfractionated radiation therapy at total doses of 54.4 Gray andgreater. Cancer 1993;71:1362e1367.

13 Bezjak A, Adam J, Panzarella T, et al. Radiotherapy for brainmetastases: defining response. Radiother Oncol 2001;61:71e76.

14 Horton J, Baxter DH, Olson KB, et al. The management of metastasesto the brain by irradiation and corticosteroids. Am J RoentgenolRadium Ther Nucl Med 1971;3:334e336.

15 Gaspar L, Scott C, Rotman M, et al. Recursive partitioning analysis(RPA) of prognostic factors in three radiation therapy oncology group(RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys 1997;37:745e751.

16 Priestman TJ, Dunn J, Brada M, et al. Final results of the RoyalCollege of Radiologists’ trial comparing two different radiotherapyschedules in the treatment of cerebral metastases. Clin Oncol 1996;8:308e315.

17 Bezjak A, Adam J, Barton R, et al. Symptom response after palliativeradiotherapy for patients with brain metastases. Eur J Cancer 2002;38:487e496.

18 Lagerwaard FJ, Levendag PC, Nowak PJ, et al. Identification ofprognostic factors in patients with brain metastases: a review of 1292patients. Int J Radiat Oncol Biol Phys 1999;43:795e803.

19 Kurtz JM, Gelber R, Brady LW, et al. Palliation of brain metastases ina favorable patient population: a randomized clinical trial by theRadiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys1981;7:891e895.

20 Diener-West M, Dobbins TW, Walsh JW, et al. Identification of anoptimal subgroup for treatment evaluation of patients with brainmetastases using RTOG study 7916. Int J Radiat Oncol Biol Phys1989;16:669e673.

21 Scott CB, Scarantino C, Urtasun R, et al. Validation and predictivepower of Radiation Therapy Oncology Group (RTOG) recursivepartitioning analysis classes for malignant glioma patients: a reportusing RTOG 90-06. Int J Radiat Oncol Biol Phys 1998;40:51e55.

22 Gaspar LE, Scott C, Murray K, et al. Validation of the RTOG recursivepartitioning analysis (RPA) classification for brain metastases. Int JRadiat Oncol Biol Phys 2000;47:1001e1006.

23 Lutterbach J, Bartelt S, Stancu E, et al. Patients with brain metastases:hope for recursive partitioning analysis (RPA) class 3. Radiother Oncol2002;63:339e345.

24 Posner JB. Management of brain metastases. Rev Neurol 1992;148:477e487.

25 Altman DG. Systematic reviews of evaluations of prognostic variables.BMJ 2001;28(323):224e228.

26 Simon R, Altman DG. Statistical aspects of prognostic factor studies inoncology. Br J Cancer 1994;69:979e985.

27 Shaw E, Scott C, Souhami L, et al. Single dose radiosurgical treatmentof recurrent previously irradiated primary brain tumors and brainmetastases: final report of RTOG protocol 90-05. Int J Radiat OncolBiol Phys 2000;47:291e298.

28 Su MY, Yu H, Chiou JY, et al. Measurement of volumetric andvascular changes with dynamic contrast enhanced MRI for cancertherapy monitoring. Technol Cancer Res Treat 2002;1:479e488.

29 Lassman AB, DeAngelis LM. Brain metastases. Neurol Clin 2003;21:1e23.

30 Weber WA, Avril N, Schwaiger M. Relevance of positron emissiontomography (PET) in oncology. Strahlenther Onkol 1999;175:356e373.

31 Smalley S, Schray MF, Laws ER, et al. Adjuvant radiation therapyafter surgical resection of solitary brain metastasis: association withpattern of failure and survival. Int J Radiat Oncol Biol Phys 1987;13:1611e1616.

32 Sawaya R. Considerations in the diagnosis of brain metastases.Oncology 2001;15:1144e1154.