Effect of ionizing radiation on the human brain: White matter and gray matter T1 in pediatric brain tumor patients treated with conformal radiation therapy

Int. J. Radiation Oncology Biol. Phys., Vol. 49, No. 1, pp. 79 –91, 2001 Copyright © 2001 Elsevier Science Inc. Printed in the USA. All rights reserved 0360-3016/01/$–see front matter

PII S0360-3016(00)01351-1

CLINICAL INVESTIGATION

Brain

EFFECT OF IONIZING RADIATION ON THE HUMAN BRAIN: WHITE MATTER AND GRAY MATTER T1 IN PEDIATRIC BRAIN TUMOR PATIENTS TREATED WITH CONFORMAL RADIATION THERAPY

XIOPING

R. GRANT STEEN, PH.D.,*†‡§ MATTHEW KOURY, B.S.,* C. ISABEL GRANJA, B.S.,* XIONG, PH.D.,㛳 SHENGJIE WU, M.S.,㛳 JOHN O. GLASS, M.S.,* RAYMOND K. MULHERN, PH.D.,¶ LARRY E. KUN, M.D.,# AND THOMAS E. MERCHANT, D.O., PH.D.#§

Departments of *Diagnostic Imaging, 㛳Epidemiology and Biostatistics, ¶Behavioral Medicine, and #Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN; Departments of †Pediatrics, ‡Radiology, and §Biomedical Engineering, University of Tennessee School of Medicine, Memphis, TN Objective: To test a hypothesis that fractionated radiation therapy (RT) to less than 60 Gy is associated with a dose-related change in the spin-lattice relaxation time (T1) of normal brain tissue, and that such changes are detectable by quantitative MRI (qMRI). Methods: Each of 21 patients received a qMRI examination before treatment, and at several time points during and after RT. A map of brain T1 was calculated and segmented into white matter and gray matter at each time point. The RT isodose contours were then superimposed upon the T1 map, and changes in brain tissue T1 were analyzed as a function of radiation dose and time following treatment. We used a mixed-model analysis to analyze the longitudinal trend in brain T1 from the start of RT to 1 year later. Predictive factors evaluated included patient age and clinical variables, such as RT dose, time since treatment, and the use of an imaging contrast agent. Results: In white matter (WM), a dose level of greater than 20 Gy was associated with a dose-dependent decrease in T1 over time, which became significant about 3 months following treatment. In gray matter (GM), there was no significant change in T1 over time, as a function of RT doses < 60 Gy. However, GM in close proximity to the tumor had an inherently lower T1 before therapy. Neither use of a contrast agent nor a combination of chemotherapy plus steroids had a significant effect on brain T1. Conclusion: Results suggest that T1 mapping may be sensitive to radiation-related changes in human brain tissue T1. WM T1 appears to be unaffected by RT at doses less than approximately 20 Gy; GM T1 does not change at doses less than 60 Gy. However, tumor appears to have an effect upon adjacent GM, even before treatment. Conformal RT may offer a substantial benefit to the patient, by minimizing the volume of normal brain exposed to greater than 20 Gy. © 2001 Elsevier Science Inc. T1 mapping, Brain tissue, Radiotherapy, Dose-related changes.

INTRODUCTION The potential for radiation-induced injury of normal brain (1) limits the dose of irradiation that can be used safely when treating brain tumors with external-beam radiation therapy (RT). Because incidental irradiation of tissue near a tumor is unavoidable, new techniques such as three-dimensional (3D) conformal RT have been developed to limit the radiation dose to normal tissues. The primary objective of conformal RT is to deliver a uniform dose to the targeted region, which includes the tumor and associated regions at risk for recurrence, and to keep the dose to surrounding healthy tissue below a level that might produce clinically significant side effects (2).

Inherent to the 3D radiation treatment technique, and to the goal of limiting the volume of brain exposed to the highest radiation dose, is an increase in the volume of normal brain that receives low doses of radiation (3). Little is known about the effects of low-dose irradiation of the normal brain, even though large volumes of brain may be incidentally irradiated during the process of delivering conformal RT. Conventional MR imaging (cMRI) shows diffuse changes in white matter, usually at doses greater than 50 Gy, which are consistent with radiation-induced normal tissue damage. Because such changes are qualitative and may depend upon clinical and technical factors, their value has been limited in predicting specific clinical or neurocognitive outcomes.

Reprint requests to: Dr. R. Grant Steen, Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, 332 North Lauderdale, Memphis, TN 38105-2794. E-mail: Grant.Steen@ stjude.org This work was supported by a research grant from the American

Cancer Society (RPG CDE-98803 to TEM), the National Cancer Institute through a Cancer Center Support (CORE) grant (P30CA21765), and the American Lebanese Syrian Associated Charities (ALSAC). Accepted for publication 31 July 2000. 79

80

I. J. Radiation Oncology

●

Biology

●

Physics

Nevertheless, many neurologic, neurocognitive, and behavioral effects have been observed at RT doses of less than 50 Gy (4, 5), especially in children (6 –26), suggesting that there can be occult damage to the brain at radiation doses of less than 50 Gy. There is also growing evidence that the appearance of white matter damage on cMRI does not correlate well with the severity of clinical or neurocognitive outcomes (27–30). Thus, there is a need for sensitive, quantitative, and objective measures of radiation effect at low RT dose levels. Such measures would be useful to assess for clinically significant treatment-related sequelae, to determine if there is a benefit of 3D treatment techniques, and to evaluate the significance of low-dose irradiation of normal brain. We sought to determine if therapeutic radiation has an effect on normal brain tissue in children at dose levels lower than what typically causes brain changes observable by cMRI. We used a quantitative MR imaging (qMRI) method, developed and extensively validated in our laboratory (31– 36), to measure tissue spin-lattice relaxation time (T1). Because tissue T1 is a physical property of the water molecule, which has a strong effect on the ability to visualize brain structures by cMRI, T1 may be sensitive to subtle changes below the resolution of current cMRI methods (34, 35, 37). In the work reported here, we assess radiationrelated changes in T1 of both white matter (WM) and gray matter (GM), in pediatric patients receiving fractionated conformal RT. Our hypothesis was that RT significantly alters the T1 of normal human brain, and that there is a dose–response relationship between RT and changes in brain tissue T1. METHODS AND MATERIALS Protocol overview and study design In July 1997, a prospective study, entitled “A Phase II study of image-guided radiation therapy for pediatric CNS tumors and quantification of radiation-related CNS effects,” was opened to patient enrollment by the Institutional Review Board (IRB) at St. Jude Children’s Research Hospital. The eligibility criteria for this protocol included the following: (1) age at diagnosis: 1.5–21 years; (2) histologically confirmed primary brain tumor; lesions involving the optic chiasm with either extension to the optic nerve and/or tract, or nonglobular involvement of the hypothalamus did not require histologic confirmation; (3) unifocal tumor (no dissemination within or beyond the central nervous system); (4) histologic type that requires only focal irradiation; (5) no prior fractionated radiation therapy; (6) no ongoing chemotherapy (corticosteroids excluded); and (7) adequate performance status (ECOG 0 –3). Parents or guardians of all children signed an informed consent after a detailed description of the protocol. Patients received an MRI examination involving both cMRI and qMRI before the initiation of RT; during the qMRI examination, images were acquired enabling us to measure WM T1 and cortical GM T1. The same examination was repeated

Volume 49, Number 1, 2001

Table 1. Summary data for study patients, including the mean, standard deviation (SD), and range for all values Variable

Mean

SD

Minimum

Maximum

Age at RT Prescribed RT (Gray) Mean follow-up (weeks) Number of MRIs evaluated/patient

9.5 56.1 38.5

4.7 2.7 16.6

2.3 54.0 11.9

17.9 59.4 54.9

5.1

1.3

3

7

Among study patients, all diagnoses were biopsy-proven and included ependymoma (8 patients); juvenile pilocytic astrocytoma (7 patients); craniopharyngioma (3 patients); low-grade astrocytoma (1 patient); pleomorphic xanthoastrocytoma (1 patient); and glioblastoma multiforme (1 patient). Abbreviations: RT ⫽ radiotherapy; SD ⫽ standard deviation.

at Week 3 and Week 5 of RT, and every 3 months after the start of RT, to provide longitudinal follow-up of our patients. Contrast agent was injected before image acquisition in 77% of all examinations; if Weeks 3 and 5 are ignored, contrast was used in 89% of all examinations for clinical reasons. Although contrast injection is known to have a minimal effect on parenchymal T1 in brain tumor patients (36), we included contrast use as a factor in the statistical model, to test for its potential effect on measured T1. Patient description Included in this report are the first 21 eligible patients to have completed RT. Patient age ranged from 2.3 to 17.9 years, with a median age of 10.5 years (Table 1). Each patient was treated with conformal RT directed at the primary site of disease. The total dose prescribed was 54.0 or 59.4 Gy using conventional fractionation. Most patients (n ⫽ 14) received a minimum of 5 examinations, and additional examinations were obtained at Week 39 (n ⫽ 10) and Week 52 (n ⫽ 10) for some patients included in this report. Conventional MRI (CMRI) All MR imaging was performed on a 1.5 Tesla Magnetom SP63 or Vision (Siemens Medical Systems, Iselin, NJ) MR imager, using standard Siemens quadrature head coils. Conventional T1-weighted (T1W) gradient echo MR image sets were acquired across the brain in the sagittal and transverse planes. These images were used to select a slice for the qMRI examination. In addition, T2- and proton-density weighted (T2W) turbo spin-echo transverse images were acquired in a single sequence to screen patients for tumor progression. The total acquisition time for the cMRI images was about 11 min. Patients with progressive disease were identified by cMRI, so that they could be censored from further analysis. This is because, in those patients with progressive disease, acute changes in T1 could be due to radiation or the effect of tumor, whereas, in those patients with nonprogressive disease, an acute change in T1 should be due to radiation alone.

Radiation effects on normal human brain

●

R. G. STEEN et al.

81

Fig. 1. Explanation of method of analysis of brain T1 as a function of radiation dose. (A). Example of one of the 4 base images used for calculation of T1. (B). The parametric T1 map calculated from the image shown in A. (C). Segmentation of the T1 map. (D). A “cored” T1 map, showing how central structures are erased before further analysis. (E). The radiation isodose lines shown superimposed on a CT image. (F). The isodose lines have been transferred to the T1 map, and all structures that received greater than 500 cGy have been erased. The remaining tissue in the image is white matter (dark green) or gray matter (yellow), from which average T1 values were calculated.

Quantitative MRI (qMRI) Quantitative imaging of T1 was done with a precise and accurate inversion-recovery (PAIR) method optimized and validated previously in our laboratory (31–36). A single transverse slice through the basal ganglia was selected, to be consistent with prior data from patients and controls (36). This slice level was imaged with the PAIR sequence, which requires an imaging time of less than 14 min (Fig. 1A).

Recently, the PAIR method was improved, so that imaging time has been reduced to only 4 min with a TurboPAIR sequence. Results from the PAIR method and the TurboPAIR method are equivalent, as demonstrated by a study of 11 volunteers imaged with both methods (36). Control data were derived from a population of 115 healthy subjects (32), of which 45 were in the age range of patients reported here. Data from these healthy subjects

82

I. J. Radiation Oncology

●

Biology

●

Physics

Volume 49, Number 1, 2001

Fig. 1. Continued.

were analyzed by using a statistical model, to predict expected T1 in WM and GM as a function of subject age (32, 33). Measurement of T1 After acquisition, PAIR or TurboPAIR images were transferred to a Silicon Graphics Indy workstation, for further analysis. Pixels identified as noise were excluded by statistical criteria (38) and the remaining pixels were submitted to a curve-fitting procedure (31, 38, 39). The T1 equation was solved for ␣ (spin-density factor corrected for T2 losses), k (cosine of the effective flip angle of the inversion pulse), and T1 in each pixel. The T1 value was used to make a parametric T1 map, wherein pixel grayscale value is equivalent to the relaxation time in msec (Fig. 1B). Segmentation of qMRI images The qMRI images were further analyzed using a neural network algorithm (40, 41), adapted specifically for segmenting tissues in the PAIR images (42). The non-normalized signal intensity from each pixel in the 4 PAIR base images was used as input to a single-layer Kohonen selforganizing map. After segmentation was completed, each of the 9 levels in the segmented image was manually classified as GM, WM, cerebrospinal fluid (CSF), partial volume of GM ⫹ WM, partial volume of GM ⫹ CSF, or background. A pseudocolor image of the brain was created (41), and this image was imported into PhotoShop (Adobe PhotoShop 4.0.1) running on a PowerCenter Pro 180 (Power Computing, Round Rock, TX). Extrameningial tissues were erased using standard PhotoShop tools (Fig. 1C). In the segmented image, yellow pixels correspond to GM and dark green

pixels correspond to WM. Lime green pixels correspond to volumes that contain a partial volume of GM ⫹ WM, and such pixels were not further analyzed. Central WM and GM structures were erased (Fig. 1D), so that these pixels were also excluded from further analysis. This was done for two reasons: we lack adequate control data from which to determine expected values for WM tracts in the central structures of the brain (32); and dosimetric lines were usually too densely placed in central structures to accurately assess T1 of GM, as a function of RT dose. Conformal radiation therapy Conformal 3D treatment plans employing 4 –25 beams (some with intensity-modulated radiation therapy) were developed using the “PLan University of North Carolina” treatment planning system. The majority of treatments were delivered on MLC-equipped Siemens Primus and Primart linear accelerators. Patients were immobilized using a relocatable stereotactic head frame, a thermoplastic face mask, or a vacuum bag molded to the patient. General anesthesia was used when necessary. Treatment-planning guidelines specified a 10-mm anatomically defined clinical target volume (CTV) for ependymoma, low-grade astrocytoma or low-grade neuronal tumors, and craniopharyngioma. Patients with high-grade astrocytoma or high-grade neuronal tumors were treated with a 20-mm anatomically defined CTV. The geometric margin used to define the planning target volume was fixed at 5 mm for all patients independent of immobilization. Targeting followed ICRU Report 50 guidelines (43). Isodose contours were generated for each patient in the plane corresponding to the qMRI image slice (Fig. 1E). The contours included dose levels ranging from 500 cGy to 6000 cGy, in increments of 500 cGy. Because

Radiation effects on normal human brain

the tumor was not always in or near the qMRI image slice, the T1 map did not always include tissue that received the highest RT dose. Analyzing T1 as a function of radiation dose To determine the relationship between RT dose and changes in T1, the isodose contours for each patient were superimposed on the corresponding segmented T1 map. The average T1 of WM or GM which received, for example, ⬍ 500 cGy could then be calculated by erasing all tissue that received 500 cGy or more (Fig. 1F). Tissue T1 was thus calculated as a function of RT dose, for WM and GM, and T1 was recorded as a function of time since RT. The completed data set, therefore, encodes: radiation dose to brain, classified into one of 8 categories (⬍5 Gy; 5 to ⬍ 10 Gy; 10 to ⬍ 20 Gy; 20 to ⬍ 30 Gy; 30 to ⬍ 40 Gy; 40 to ⬍ 50 Gy; 50 to ⬍ 54 Gy; and ⱖ 54 Gy); tissue type (WM and GM); and time since RT (in weeks). Statistical tests Each patient received multiple qMRI exams longitudinally (pretreatment, Weeks 3, 5, 13, 26, 40, and 53 after RT). Our analysis sought to estimate and compare the longitudinal trends of T1 in WM and GM as a function of RT dose. Because each patient had multiple qMRI exams, and because T1 for WM and GM was measured in multiple brain areas, the T1s in different areas and at different times are correlated. Hence, we used a mixed linear model (44, 45) to analyze the data, in which T1 is the response variable, each patient is treated as a cluster, the day from initiation of RT is the longitudinal variable, and the RT dose to tissue is the primary covariate variable. The age of the patient at initiation of RT was included in the model, to control for the known effect of age on T1 (32, 33). Initially, we used a simple longitudinal model, freeing the linear longitudinal trend in T1 in each brain region from the influence of RT dose. This was done by using RT as a categorical variable, rather than as a numerical variable. This model strongly suggested that the estimated intercepts and slopes of the longitudinal trends in T1 changed in accordance with the magnitude of radiation dosage. Because of this finding, we then fitted a surface model, in which the intercepts and slopes of the longitudinal trends in T1 were linearly related to the RT dosage to brain tissue. The T1 trends predicted by the surface model matched well to those in the simple longitudinal model. However, we believe the surface model gave a better estimation by eliminating a layer of variability due to error. We also wanted to know whether trends in WM T1 were influenced by clinical variables such as: use of a contrast agent before T1 measurement (yes vs. no); use of chemotherapy before RT (yes vs. no); tumor type (glial vs. nonglial); use of steroids (yes vs. no); or the presence of hydrocephaly (yes vs. no). We used the mixed model to examine longitudinal trends in T1 as a function of several covariate variables (RT dose, patient age, and time since

●

R. G. STEEN et al.

83

starting RT), and one clinical status variable at a time (e.g., use of cMRI contrast). The model then tested for interaction effects of these variables. RESULTS Patient summary Clinical data are summarized for all patients (Table 1). This table shows that, while there were some very young patients, most patients were around 10 years of age. The mean follow-up period for patients was 39 weeks (9 months), and 5 examinations per patient (average) were evaluated. There were no consistent differences between younger and older patients in any of several clinical features, including tumor type (glial vs. nonglial), use of steroids, or prior exposure to chemotherapy. However, there were significant differences between younger and older patients in the location of the tumor (infratentorial vs. supratentorial) and in the presence of hydrocephaly. Among younger patients, infratentorial tumor was more prevalent than in older patients (p ⬍ 0.002), and younger patients tended to have hydrocephaly (p ⬍ 0.04). The qMRI evaluations of patients, scheduled as part of the protocol, were completed at very close to the scheduled examination dates. Examination 1, scheduled to occur before RT, was completed an average of 9.0 (⫾ 6.2) days pre-RT. Examination 2, scheduled to occur 3 weeks after the first day of RT, actually occurred on Day 20.8 (⫾ 2.2). Examination 3, scheduled to occur 5 weeks after the first day of RT, actually occurred on Day 35.4 (⫾ 2.5). Therefore, both Examinations 2 and 3 were completed within 1 day of the scheduled time. Examination 4, scheduled to occur 3 months after the beginning of RT, actually occurred on Day 92.1 (⫾ 6.3), approximately 50 days after completion of RT. In some patients, follow-up exams were done at 6 months (Examination 5), 9 months (Examination 6), and 12 months (Examination 7) after the beginning of RT. A total of 107 qMRI exams were analyzed in the work presented here. Conventional MRI Conventional MRI (cMRI) films were used to identify patients with progressive disease; 1 patient was censored because of disease progression, with progression first noted by cMRI at 6 months after RT began. A second patient was censored because the patient’s last qMRI examination was completed nearly 2 months after the scheduled time interval. Quantitative MRI A scatterplot of T1 values for WM that received ⬍ 5 Gy is shown (Fig. 2). This figure shows that several patients had extremely high values for WM T1 before RT, but these values decreased rapidly upon treatment, so that WM T1 for most patients fell within a fairly narrow range by the end of treatment. Statistical analysis iden-

84

I. J. Radiation Oncology

●

Biology

●

Physics

Volume 49, Number 1, 2001

Fig. 2. Scatterplot of T1 of white matter at the lowest radiation therapy (RT) dose level (ⱕ 5 Gy) for 21 patients. This scatterplot demonstrates the heterogeneity of patient T1 values and examination times.

tified 3 T1 measurements in WM as being statistical outliers, so some of the following analyses of WM were done both with and without outliers. The scatterplot also demonstrates that patient exams were generally completed within a fairly small window of time. This made it possible to pool patient exams, so that separate patient exams could be analyzed together (as shown for Examination 5, Fig. 2). Brain T1 as a function of radiation dose Brain tissue T1 was measured in a total of 1201 separate regions of interest (ROIs) among the 107 exams evaluated (Table 2). A nearly equal number of ROIs were evaluated in WM and GM, suggesting that the statistical sensitivity of the method should be comparable for radiation-related changes in both tissue types. Roughly 66% of the data analyzed were acquired within 3 months of initiation of RT, and only 11% of all data were acquired at the 1-year examination. Therefore, analysis will have greater sensitivity to T1 changes at short post-RT intervals. White matter T1 at each of the separate dose levels is plotted (Fig. 3). This scatterplot suggests that WM exposed to 30 Gy or greater shows a reduction in T1 by Week 40, which persists at Week 53. Because of the apparent difference between WM exposed to more than 30 Gy and WM exposed to less than 30 Gy, a 30-Gy cut-point will be used in certain analyses of WM, as noted. Gray matter T1 at each of the separate dose levels is also plotted (Fig. 4). This scatterplot suggests that GM exposed

to 50 Gy or more has a lower T1 at all time points than GM exposed to less than 50 Gy. Because of this apparent difference, a 50-Gy cut-point will be used in certain analyses of GM, as noted. A comparison of observed and expected values of T1 for WM and GM exposed to 5 Gy or less is shown (Table 3). Observed T1 for WM and GM was calculated from all patients evaluated pre-RT and at the 53-week examination. Expected T1 for WM and GM was determined by modeling T1 data from 115 healthy volunteers (32). Before RT, observed WM T1 was higher than expected (p ⬍ 0.02), whereas observed GM T1 was not significantly different from expected. By the end of follow-up, observed WM had normalized, whereas observed GM T1 was significantly less Table 2. T1 data collected at each time point: summary of number of regions of interest (ROIs) evaluated at each exam, in white matter and in gray matter: A total of 1201 ROIs were evaluated in this study Number of ROIs evaluated Examination

White matter

Gray matter

Preradiotherapy Week 3.0 Week 5.1 Week 13.2 Week 26.3 Week 39.9 Week 52.7 Total ROIs evaluated ⫽

121 88 80 107 80 58 61 595

120 87 89 101 86 57 66 606

Radiation effects on normal human brain

●

R. G. STEEN et al.

85

Fig. 3. Scatterplot of white matter (WM) T1 as a function of radiation therapy (RT) dose at various intervals after treatment. WM that received greater than 30 Gy is shown with filled symbols, whereas WM that received 30 Gy or less is shown with hollow symbols.

than expected (p ⬍ 0.01). Brain T1 is known to decrease with age in healthy subjects, in both white and gray matter (32, 33). As a function of age alone, WM T1 values were expected to change ⫺ 0.2%, whereas GM values were expected to change ⫺ 0.4%, over the average follow-up interval of 0.8 years. However, the actual change in T1 over the course of follow-up was ⫺ 4.4% in WM and ⫺ 4.5% in GM (Table 3). The fact that observed T1 changes were 22-fold larger than expected in WM and 11-fold larger than expected in GM suggests that age is not a significant confounder in this dataset. Changes in WM T1 over time were analyzed as a function of radiation dose, after adjusting for age of the patient (Table 4). This analysis was performed for the entire data set, and the analysis was repeated after exclusion of 3 WM T1 values identified as statistical outliers. Exclusion of outliers had no effect on the trends noted, but did have an effect on the significance levels obtained. White matter exposed to less than 10 Gy RT showed no change over time, whereas WM exposed to 10 Gy or greater showed a significant decrease in T1 with time. The significance level of the decrease was high for all dose levels, when

outliers were included, but significance was low for an RT dose less than 30 Gy, when outliers were excluded. These results demonstrate that radiation of 30 Gy or greater has a significant effect on WM T1, consistent with the trend seen in Fig. 3. White matter T1 was also analyzed as a function of RT dose at various time intervals following the initiation of RT (Table 5). In virtually every case, the slope of the dose–response relationship between WM T1 and RT is negative, but no significant change in WM T1 as a function of RT dose was noted until 13 weeks after treatment began. Changes in GM T1 over time were also analyzed as a function of radiation dose, after adjusting for age (data not shown). This analysis shows no significant change in GM T1 over time, at any RT dose level, suggesting that GM T1 is insensitive to radiation of less than 60 Gy. Inspection of Fig. 4 suggests that GM T1 that was low at the beginning of treatment remained low, even though none of the curves declined with time. Paradoxically, there was a significant dose–response relationship between radiation and GM T1 at every time interval ana-

86

I. J. Radiation Oncology

●

Biology

●

Physics

Volume 49, Number 1, 2001

Fig. 4. Scatterplot of gray matter (GM) T1 as a function of radiation therapy (RT) dose at various intervals after treatment. GM that received greater than 50 Gy is shown with filled symbols, whereas GM that received 50 Gy or less is shown with hollow symbols.

lyzed, with this dose–response relationship being significant (p ⬍ 0.0001) even in the time interval before radiation was given. The apparent dose–response relationship, present before the RT was given, argues that GM close to the tumor, which would receive a higher radiation dose by prescription, is inherently different from GM distant from the tumor, and that proximity to tumor has an effect on GM T1. A summary table (Table 6) contrasts WM exposed to RT of less than 30 Gy with WM exposed to RT of 30 Gy or more. This table also contrasts GM exposed to RT of less than 50 Gy with GM exposed to RT of 50 Gy or more. The cut-points for white and GM were identified by inspection of Figs. 3 and 4. The T1 reduction in WM exposed to greater than 30 Gy becomes significant after about 6 months, whereas T1 reduction in GM exposed to greater than 50 Gy was present at the first examination.

Effect of clinical variables on white matter T1 A mixed-model analysis was used to determine whether clinical variables had an impact on measured T1 in WM or GM. In this model, we combined prior chemotherapy and concurrent steroids into one factor, because we found that use of chemotherapy before RT was significantly correlated with steroid use during RT, as patients who received chemotherapy tended also to receive steroids (␹2 test, p ⬍ 0.001). Our analysis confirms that patient age has a significant impact on measured T1, and that T1 is significantly effected by an interaction between patient age and day posttreatment, in both WM and GM (Table 7). This suggests that the brain tissue of young children may respond to radiation in a fundamentally different way than does the brain tissue of older children. In WM but not GM, T1 is significantly affected by an interaction between RT dose and day posttreatment (as in Table 4).

Radiation effects on normal human brain

●

R. G. STEEN et al.

87

Table 3. Comparison of observed and expected mean T1 values for white matter (WM) and gray matter (GM) exposed to ⱕ5 Gy Tissue

Observed T1 (⫾ SD)

Expected T1 (⫾ SD)

⌬

729 (⫾ 90) n ⫽ 21 1094 (⫾ 152) n ⫽ 19

665 (⫾ 24) n ⫽ 113 1203 (⫾ 62) n ⫽ 113

⫹ 9.6% ⫺ 9.1%

NS (0.06)

697 (⫾ 37) n ⫽ 11 1045 (⫾ 71) n ⫽ 12

664 (⫾ 24) n ⫽ 113 1198 (⫾ 62) n ⫽ 113

⫹ 5.0%

NS (0.11)

⫺ 12.8%

0.009

At start of RT WM GM At end of follow-up WM GM

p⫽ 0.02

Observed values are the average of all patients evaluated before radiation therapy (RT) and at the 53-week examination. Expected values were determined by modeling T1 data from 115 healthy people. The average patient age at the start of RT was 9.5 years, whereas the average age at the end of follow-up was 10.3 years. For this interval of 0.8 years of follow-up, T1 values were expected to change by ⫺0.2% in WM and ⫺0.4% in GM, as a function of age alone. Actual T1 change in patients was ⫺4.4% in WM and ⫺4.5% in GM, suggesting that age is not a significant confounder in this data set.

In GM but not WM, T1 is significantly effected by RT dose, and by an interaction between RT dose and patient age, and by a three-way interaction between age, dose, and day post-RT treatment. There was no significant effect of MRI contrast agent use on the longitudinal trend in T1, in either WM or GM (Table 7). Because the effects of chemotherapy and steroids were confounded, it was impossible to separate these two variables. However, patients who received neither chemotherapy nor steroids (14 patients) were compared to those patients who received both agents (5 patients). Analysis suggests that the main effects of chemotherapy ⫹ steroids on the longitudinal trend in T1 (intercept and slope) are not significant in either WM or GM. However, there was a significant interaction effect of age and chemotherapy on the slope of the T1 trend, suggesting that the rate of T1 change in GM differs significantly among patient subgroups, according to patient age (young vs. old) and chemotherapy ⫹ steroids (yes vs. no). In addition, there was no effect of any of the other clinical variables on the rate of decline of T1. For exam-

Table 4. Statistical significance levels resulting from a surface model of the change in white matter T1 over time, as a function of radiation dose Radiation dose

Including outliers

Excluding outliers

⬍ 5 Gy 5 to ⬍ 10 Gy 10 to ⬍ 20 Gy 20 to ⬍ 30 Gy 30 to ⬍ 40 Gy 40 to ⬍ 50 Gy 50 to ⬍ 54 Gy 54 to 59 Gy

NS NS 0.0009 ⬍ 0.0001 ⬍ 0.0001 ⬍ 0.0001 ⬍ 0.0001 ⬍ 0.0001

NS NS NS 0.03 0.005 0.006 0.001 0.001

Radiation dose is classified into discrete dose categories. Statistical analysis showed that 3 of the 602 white matter T1 values were outliers, so the dose-response relationship is shown with and without identified outliers.

ple, patients with glial tumor were not significantly different from patients with nonglial tumor, suggesting that tumor type has no impact on the response of normal brain T1 to RT. Patients with hydrocephaly were not significantly different from patients free of hydrocephaly, suggesting that hydrocephaly also has no impact on the response of normal brain T1 to RT. DISCUSSION The results reported here suggest that qMRI evaluation of T1 may be sensitive to radiation-related changes in the human brain. In WM, radiation at a dose level of 30 or more Gy is associated with a decrease in T1 over time (Table 4), which becomes significant by 3 months following treatment (Table 5). In GM, there was no significant change in T1 over time as a function of RT dose, although GM T1 decreased with RT dose prescription, even before treatment. The GM which received a radiation dose of greater than 50 Gy had a lower T1 at all time

Table 5. Change in white matter T1 as a function of radiation therapy (RT) dose at various times following treatment according to a surface model including all WM T1 data Weeks since RT

Intercept (⫾SD)

Slope (⫾SD)

ANOVA p⫽

Pre-RT 3 5 13 26 40 53

704 ⫾ 7 703 ⫾ 7 703 ⫾ 7 702 ⫾ 7 701 ⫾ 7 699 ⫾ 8 698 ⫾ 9

⫹ 0.04 (⫾ 0.12) ⫺ 0.01 (⫾ 0.11) ⫺ 0.04 (⫾ 0.11) ⫺ 0.18 (⫾ 0.09) ⫺ 0.40 (⫾ 0.11) ⫺ 0.62 (⫾ 0.15) ⫺ 0.83 (⫾ 0.21)

NS NS NS 0.03 0.0001 ⬍ 0.0001 ⬍ 0.0001

The analysis of variance (ANOVA) p value shown tests the significance of the dose–response slope at the indicated time; a low p value indicates that there is a significant dose–response relationship. No significant radiation-related change in white matter T1 is seen until at least 3 months after radiation therapy.

88

I. J. Radiation Oncology

●

Biology

●

Physics

Volume 49, Number 1, 2001

Table 6. Comparison of white matter T1 and gray matter T1 as a function of radiation therapy (RT) dose category at various times following treatment White matter T1

Gray matter T1

Interval

⬍ 30 Gy

ⱖ 30 Gy

p value

⬍ 50 Gy

ⱖ 50 Gy

p value

Pre-RT T1 (⫾ SD) Week 3 (⫾ SD) Week 5 (⫾ SD) Week 13 (⫾ SD) Week 26 (⫾ SD) Week 40 (⫾ SD) Week 53 (⫾ SD)

721 (⫾ 19) 693 (⫾ 9) 706 (⫾ 11) 703 (⫾ 10) 694 (⫾ 8) 695 (⫾ 11) 697 (⫾ 12)

719 (⫾ 19) 695 (⫾ 10) 710 (⫾ 12) 707 (⫾ 12) 681 (⫾ 9) 664 (⫾ 12) 669 (⫾ 13)

NS

1081 (⫾ 29) 1034 (⫾ 18) 1063 (⫾ 16) 1044 (⫾ 14) 1069 (⫾ 21) 1066 (⫾ 21) 1042 (⫾ 19)

1036 (⫾ 31) 990 (⫾ 21) 1012 (⫾ 19) 984 (⫾ 17) 1018 (⫾ 23) 1008 (⫾ 25) 1006 (⫾ 22)

0.0004

NS NS NS 0.03 0.0001 0.0001

intervals (Table 6), suggesting that GM close to the tumor, which would receive a higher dose of radiation by prescription, has an inherently lower T1, compared to GM remote from the tumor. This unexpected finding may reveal an effect of tumor on adjacent GM. However, because there was no significant change in GM T1 over time, even at the highest RT doses (dose ⫻ day, Table 7), GM appears to be insensitive to the effect of radiation of less than 60 Gy over the time interval studied. Table 7. Mixed-model fitted for the response variable T1 in white matter and gray matter Parameter White matter Intercept ⫹ contrast agent ⫺ contrast agent Patient age Day post treatment Age ⫻ day RT dose Dose ⫻ day Gray matter Intercept ⫹ contrast agent ⫺ contrast agent Patient age Day posttreatment Age ⫻ day RT dose Dose ⫻ day Dose ⫻ age Age ⫻ dose ⫻ day

Estimate

Standard error

784.63 ⫺ 1.97 0.00 ⫺ 7.7144 ⫺ 0.1257 0.0115 ⫺ 0.2953 ⫺ 0.0006

⫾ 16.99 ⫾ 4.60

1222.70 4.28 0.00 ⫺ 15.8910 ⫺ 0.5160 0.0510 ⫺ 2.4120 0.0054 0.1822 ⫺ 0.0006

⫾ 31.89 ⫾ 13.64

⫾ 1.5770 ⫾ 0.0603 ⫾ 0.0056 ⫾ 0.3406 ⫾ 0.0020

⫾ 2.8370 ⫾ 0.0943 ⫾ 0.0089 ⫾ 0.7455 ⫾ 0.0034 ⫾ 0.0654 ⫾ 0.0003

p value 0.0001 NS NS 0.0001 NS 0.05 NS 0.0007 0.0001 NS NS 0.0001 0.0001 0.0001 0.001 NS 0.006 0.04

Covariate variables including use of an MRI contrast agent, patient age, day post-RT, and interactions between variables are also shown. This analysis demonstrates that contrast agent had no significant effect on results. Similarly, when use of chemotherapy and steroids was tested in the same way, there was also no significant effect.

0.0009 0.0001 0.0001 0.0001 0.0001 0.003

Information on the tolerance of brain tissue to therapeutic irradiation is critically important. Many tumors are radiosensitive, yet radiation-induced injury of normal brain tissue limits the dose of radiation that can be delivered safely to a brain tumor (1). Historically, survival of patients with malignant astrocytoma was improved as the RT dose to tumor increased from 45 Gy to 60 Gy (46), but dose escalation beyond 60 Gy has been limited by concerns about the radiation tolerance of normal brain (47). It has been accepted that the entire human brain can tolerate 45 Gy, with smaller volumes receiving 55– 60 Gy, at a fraction size of 1.8 –2.0 Gy daily (5), yet these guidelines are empirically derived. Therefore, it is important to determine the relationship between RT dose and changes in brain tissue, and to determine if a biophysical parameter, such as T1, can provide an objective surrogate marker for brain damage or clinical side effects. There is strong evidence of a dose–response relationship between radiation exposure and damage to normal WM. No cMRI-visible WM changes were reported in leukemic children who received prophylactic radiation of 18 –24 Gy (27). No radiation-related reduction in glucose metabolic rate was reported in WM or cortical GM of long-term survivors of childhood acute lymphoblastic leukemia, among patients who were treated with an average of 21.9-Gy cranial RT, as compared to patients who received methotrexate chemotherapy only (48). No significant change in normal WM T1 was measured in 7 adult malignant glioma patients who received 24.3–30.6 Gy of pion RT to the brain near tumor (49). However, at dose levels of 40 Gy and higher, WM damage was detected by cMRI in 10 of 11 children (50), and a 5% incidence of radionecrosis was reported in a prospective study of patients with primary brain tumor who received fractionated RT to 45 Gy or more (51). In a cohort of adults who received 60 Gy RT for tumor in the base of the skull (52), memory impairment was found in 80% of patients, and the severity of neurocognitive symptoms was related to the total radiation dose delivered to the brain. It was

Radiation effects on normal human brain

concluded that the pattern of deficits was consistent with radiation injury to subcortical white matter (52), although no evidence of brain damage was sought by cMRI. Incipient radiation damage to normal brain may not be routinely detectable by cMRI. Although most cMRI studies show little evidence of radiation damage below 54 – 60 Gy (53), neurologic, neurocognitive, and behavioral effects have been observed in many studies of children given RT doses of less than 50 Gy (6 –25). Intellectual decline in children, first discernible at 4 – 6 months, can become pronounced over 2 to 3 years follow-up, and can occur after full cranial doses of as little as 30 –35 Gy (1). Recent findings suggest that there may even be cognitive deficits in children given as little as 35 Gy to the posterior fossa (25). Newer, more-sensitive imaging methodologies suggest that there may be damage to the brain at radiation levels well below 54 Gy. Quantitative CT showed mild brain atrophy in adults given as little as 14.4 Gy total-body irradiation (54). Analysis of brain tissue volume by cMRI in children surviving medulloblastoma has shown WM loss after 23.4 –36 Gy craniospinal RT (with a boost to the posterior fossa for a total dose of 49 –54 Gy), and WM volume loss was found to correlate significantly with neurocognitive deficits (26). An early study of 7 patients with malignant glioma found that WM T1 was reduced near tumor within 2 months of RT, although no changes were seen in T1 of brain tissue distant from the tumor, even in tissue that received fractionated RT of up to 34 Gy (49). However, this early study of T1 in brain tumor patients had several limitations: it did not attempt to determine if there was a dose– response relationship between RT and changes in T1; it did not examine WM exposed to greater than 34 Gy; and it did not define the effect of RT on GM. We cannot yet determine the clinical significance of the radiation-related reduction in T1 that we report. It is not known whether T1 reduction correlates with cognitive outcome or predicts which patients are at risk of treatmentrelated sequelae or tumor progression. Nor do we yet understand the pathologic basis for the change in brain tissue T1, although several previously described pathologies are consistent with reduction of T1 (1, 55–58). Conventional MRI has demonstrated Wallerian degeneration of WM tracts, and imaging changes were postulated to result from an increase in the lipid:protein ratio of brain (59). This pathology could certainly produce a reduction in T1. The reduction in brain T1 we report is also consistent with an autopsy study of 25 patients with intracranial glioma (60), which found that fractionated RT at 50 – 60 Gy induced dry granular or fibrinoid necrosis, with calcification, perivascular fibrosis, collagenization, and vessel telangiectasia, with all changes occurring within 6 months of treatment (60). Other pathologies that are consistent with a reduction of T1 include brain atrophy, reactive astrocytosis, arteriolar hya-

●

R. G. STEEN et al.

89

linization, accumulation of lipid-laden macrophages in the vessel intima, hemorrhage, or mineralizing microangiopathy (58, 61, 62). The most parsimonious explanation for our results may be that T1 is an indirect measure of tissue density, with reduced T1 associated with increased tissue density (and/or decreased tissue water), in both WM and GM. Thus RT, which is known to induce gliosis in WM (60, 62), could produce a dose-related decrease in T1 because of a doserelated increase in gliosis. In GM, reduction of T1 near a tumor could potentially be caused by compression of cortical parenchyma near the growing tumor mass, or by tumor cell invasion directly into the cortical parenchyma. Such a change in cell density might not be obvious to a pathologist, in either WM or GM, because changes in cell density are notoriously difficult to quantify (53). Yet we believe that qMRI may offer sufficient sensitivity to detect subtle pathologic changes in cell density that could potentially result from therapeutic radiation exposure. Measurement of brain T1 may provide a sensitive measure of brain damage that could become clinically useful when better characterized. Any region of the brain can potentially be examined, and the effect of very low RT doses can be studied. MRI is generally acknowledged to be superior to CT for visualizing radiation damage in the brain (28, 63– 65). Quantitative CT (qCT) methods are able to detect a 0.2% change in tissue density, which would correspond to a loss in cell density of about 2% in WM (53). Such a small change in cell density could not be detected by standard histologic methods, and would require quantitative analysis of pathologic samples (53). Thus, if qMRI is comparable to qCT for characterizing radiation damage in the brain, then qMRI may offer a sensitivity to cell density that is comparable to quantitative analysis of a biopsy sample. Because biopsy of normal brain tissue is not desirable, qMRI may be the most sensitive method available to assess normal brain response to RT, without exposing the patient to further radiation. Eventually, the sensitivity of the qMRI method may make it possible to predict which patients are most at risk of damage from RT. This is a compelling consideration because risk factors for radiation damage are, as yet, poorly characterized (66). Our results demonstrate that WM exposed to ⬍ 20 Gy radiation does not undergo a significant change in T1, even at 6 to 12 months following radiation exposure. We also find no evidence that GM is sensitive to RT ⬍ 60 Gy. If qMRI is sensitive to pathologic changes in brain tissue, then our results suggest that there should be no damage to WM exposed to less than 20 Gy, and no damage to GM exposed to less than 60 Gy. Because conformal RT can be used to minimize the volume of brain exposed to RT of greater than 20 Gy (47), our results predict that conformal RT offers a substantial benefit to patients.

90

I. J. Radiation Oncology

●

Biology

●

Physics

Volume 49, Number 1, 2001

REFERENCES 1. Schultheiss TE, Kun LE, Ang KK, et al. Radiation response of the central nervous system. Int J Radiat Oncol Biol Phys 1995;31:1093–1112. 2. Leibel SA, Ling CC, Kutcher GJ, et al. The biological basis for conformational three-dimensional radiation therapy. Int J Radiat Oncol Biol Phys 1991;21:805– 811. 3. Hamilton RJ, Kuchnir FT, Sweeney P, et al. Comparison of static conformal field with multiple non-coplanar arc techniques for stereotactic radiosurgery or stereotactic radiotherapy. Int J Radiat Oncol Biol Phys 1995;33:1221–1228. 4. DeAngelis LM, Delattre JY, Posner JB. Radiation-induced dementia in patients cured of brain metastases. Neurology 1989;39:789 –796. 5. Crossen JR, Garwood D, Glatstein E, et al. Neurobehavioral sequelae of cranial irradiation in adults: A review of radiationinduced encephalopathy. J Clin Oncol 1994;12:627– 642. 6. Peylan-Ramu N, Poplack DG, et al. Abnormal CT scans of the brain in asymptomatic children with acute lymphocytic leukemia after prophylactic treatment of the central nervous system with radiation and intrathecal chemotherapy. N Engl J Med 1978;298:815– 818. 7. Meadows AT, Gordon J, Massari DJ, et al. Declines in IQ scores, and cognitive dysfunctions in children with acute lymphocytic leukemia treated with cranial irradiation. Lancet 1981;2:1015–1018. 8. Kun L, Mulhern RK, Crisco JJ. Quality of life in children treated for brain tumors: intellectual, emotional, and academic function. J Neurosurg 1983;58:1– 6. 9. Jannoun L. Are cognitive and educational development affected by age at which prophylactic therapy is given in acute lymphoblastic leukaemia? Arch Dis Childhood 1983;58:953– 958. 10. Meadows A, Silberg J. Delayed consequences of therapy for childhood cancer. Cancer 1985;35:271–286. 11. Ellenberg L, McComb JG, Siegel SE, Stowe S. Factors affecting intellectual outcome in pediatric brain tumor patients. Neurosurgery 1987;21:638 – 644. 12. Fogarty K, Volonino V, Caul J, et al. Acute leukemia: Learning disabilities following CNS irradiation. Clin Pediatr 1988; 27:524 –528. 13. Packer RJ, Sutton LN, Atkins TE, et al. A prospective study of cognitive function in children receiving whole-brain radiotherapy and chemotherapy: 2-year results. J Neurosurg 1989; 70:707–713. 14. Jannoun L, Bloom HJG. Long-term psychological effects in children treated for intracranial tumors. Int J Radiat Oncol Biol Phys 1990;18:747–753. 15. Mulhern RK, Hancock J, Fairclough DL, et al. Neuropsychological status of children treated for brain tumors: A critical review and integrative analysis. Med Pediatric Oncol 1992; 20:181–191. 16. Silber JH, Radcliffe J, Peckham V, et al. Whole brain irradiation and decline in intelligence: The influence of dose and age on IQ score. J Clin Oncol 1992;10:1390 –1396. 17. Radcliffe J, Packer RJ, Atkins TE, et al. Three-, and four-year cognitive outcome in children with noncortical brain tumors treated with whole-brain radiotherapy. Ann Neurol 1992;32: 551–554. 18. Duffner PK, Horowitz ME, Krischer JP, et al. Postoperative chemotherapy and delayed radiation in children less than three years of age with malignant brain tumors. N Engl J Med 1993;328:1725–1731. 19. Carpentieri SC, Mulhern RK, Douglas S, et al. Behavioral resiliency among children surviving brain tumor: A longitudinal study. J Clin Child Psychol 1993;22:236 –246. 20. Ris MD, Noll RB. Long-term neurobehavioral outcome in

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

pediatric brain tumor patients: Review and methodological critique. J Clin Exp Neuropsychol 1994;16:21– 42. Jankovic M, Brouwers P, Valsecchi MG, et al. Association of 1800 cGy cranial irradiation with intellectual function in children with acute lymphoblastic leukemia. Lancet 1994;344: 224 –227. Hoppe-Hirsch E, Brunet L, Laroussinie F, et al. Intellectual outcome in children with malignant tumors of the posterior fossa: Influence of the field of irradiation and quality of surgery. Childs Nerv Syst 1995;11:340 –345. Dennis M, Spiegler BJ, Hetherington CR, et al. Neuropsychological sequelae of the treatment of children with medulloblastoma. J Neurooncol 1996;29:91–101. Mulhern RK, Kepner JL, Thomas PR, et al. Neuropsychological functioning of survivors of childhood medulloblastoma randomized to receive conventional or reduced-dose CSI: A Pediatric Oncology Group study. J Clin Oncol 1998;16:1723– 1728. Grill J, Renaux VK, Bulteau C, et al. Long-term intellectual outcome in children with posterior fossa tumors according to radiation doses, and volumes. Int J Radiat Oncol Biol Phys 1999;45:137–145. Mulhern RK, Reddick WE, Palmer SL, et al. Neurocognitive deficits in medulloblastoma survivors and white matter loss. Ann Neurol 1999;46:834 – 841. Kramer JH, Norman D, Brant-Zawadzki M, et al. Absence of white matter changes on magnetic resonance imaging in children treated with CNS prophylaxis therapy for leukemia. Cancer 1988;61:928 –930. Constine LS, Konski A, Ekholm S, et al. Adverse effects of brain irradiation correlated with MR and CT imaging. Int J Radiat Oncol Biol Phys 1988;15:319 –330. Wilson DA, Nitschke R, Bowman ME, et al. Transient white matter changes on MR images in children undergoing chemotherapy for acute lymphoblastic leukemia: Correlation with neuropsychologic deficiencies. Radiology 1991;180:205–209. Ball WS, Prenger EC, Ballard ET. Neurotoxicity of radio/ chemotherapy in children: Pathologic and MR correlation. Am J Neuroradiol 1992;13:761–776. Steen RG, Gronemeyer SA, Kingsley PB, et al. Precise and accurate measurement of proton T1 in human brain in vivo: Validation and preliminary clinical application. J Magn Reson Imag 1994;4:681– 691. Cho S, Jones D, Reddick WE, et al. Establishing norms for age-related changes in proton T1 of human brain tissue in vivo. Magn Reson Imaging 1997;15:1133–1143. Steen RG, Ogg R, Reddick WE, et al. Age-related changes in the pediatric brain: Quantitative magnetic resonance (qMRI) provides evidence of maturational changes during adolescence. Am J Neuroradiol 1997;18:819 – 828. Steen RG, Reddick WE, Mulhern R, et al. Quantitative MRI of the brain in children with sickle cell disease reveals abnormalities unseen by conventional MRI. J Magn Reson Imaging 1998;8:535–543. Steen RG, Xiong X, Mulhern RK, et al. Subtle brain abnormalities in children with sickle cell disease: Relationship to blood hematocrit. Ann Neurol 1999;45:279 –286. Steen RG, Reddick WE, Ogg RJ. More than meets the eye: Significant regional heterogeneity in human cortical T1. Magn Reson Imaging 2000;18:361–368. Ball WS, DeGrauw A. Metabolic, congenital neurodegenerative, and toxic disorders. In: Edelman RR, Hesselink JR, Zlatkin MB, editors. Clinical magnetic resonance imaging. Volume I. Philadelphia: WB Saunders; 1996. p. 880 –910. Reddick WE, Ogg RJ, Steen RG, et al. Statistical error map-

Radiation effects on normal human brain

39.

40.

41.

42.

43.

44. 45. 46. 47. 48. 49. 50. 51.

ping for reliable quantitative T1 imaging. J Magn Reson Imaging 1996;6:244 –249. Kingsley PB, Ogg RJ, Reddick WE, et al. Correction of errors caused by imperfect inversion pulses in MR imaging measurement of T1 relaxation times. Magn Reson Imaging 1998;16: 1049 –1055. Reddick WE, Glass JO, Cook EN, et al. Automated segmentation and classification of multispectral magnetic resonance images of brain using artifical neural networks. IEEE Trans Med Imaging 1997;16:911–918. Reddick WE, Mulhern RK, Elkin TD, et al. A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors. Magn Reson Imaging 1998;16: 413– 421. Glass JO, Reddick WE, Yo V, et al. Hybrid artificial neural network segmentation of precise and accurate inversion recovery (PAIR) images from normal human brain. Magn Reson Imaging 2001; in press. International Commission on Radiation Units and Measurements (ICRU). ICRU Report 50. Dose specification for reporting external beam therapy with photons and electrons. Washington, DC: International Commission on Radiation Units and Measurements; 1978 (ICRU report issued September 1993). Rutter CM, Elashoff RM. Analysis of longitudinal data: Random coefficient regression modelling. Stat Med 1994;13: 1211–1231. Little RC, Milliken GA, Stroup WS, et al. SAS System for mixed models. Cary, NC: SAS Institute; 1996. Bleehen NM, Stenning SP. A Medical Research Council trial of two radiotherapy doses in the treatment of grades 3 and 4 astrocytoma. Br J Cancer 1991;64:769 –774. Robertson JM, Kessler ML, Lawrence TS. Clinical results of three-dimensional conformal irradiation. J Natl Cancer Inst 1994;86:968 –974. Kahkonen M, Metsahonkala L, Minn H, et al. Cerebral glucose metabolism in survivors of childhood acute lymphoblastic leukemia. Cancer 2000;88:693–700. Boesiger P, Greiner R, Schoepflin RE, et al. Tissue characterization of brain tumors during and after pion radiation therapy. Magn Reson Imaging 1990;8:491– 497. Packer RJ, Zimmerman RA, Bilaniuk LT. Magnetic resonance imaging in the evaluation of treatment-related central nervous system damage. Cancer 1986;58:635– 640. Marks JE, Baglan RJ, Prasad SC, et al. Cerebral radionecrosis:

52. 53. 54. 55. 56. 57.

58. 59. 60.

61. 62. 63. 64. 65. 66.

●

R. G. STEEN et al.

91

Incidence and risk in relation to dose, time, fractionation, and volume. Int J Radiat Oncol Biol Phys 1981;7:243–252. Meyers CA, Geara F, Wong P-F, et al. Neurocognitive effects of therapeutic irradiation for base of skull tumors. Int J Radiat Oncol Biol Phys 2000; 46: 51–55. Fike JR, Cann CE, Turowski K, et al. Radiation dose response of normal brain. Int J Radiat Oncol Biol Phys 1988;14:63–70. Peper M, Steinvorth S, Schraube P, et al. Neurobehavioral toxicity of total body irradiation: A follow-up in long-term survivors. Int J Radiat Oncol Biol Phys 2000; 46: 303–311. Emami B, Lyman J, Brown A, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 1991;21:109 –122. Dewey WC, Ling CC, Meyn RE. Radiation-induced apoptosis: Relevance to radiotherapy. Int J Radiat Oncol Biol Phys 1995;33:781–796. Fajardo LF. Morphology of radiation effects on normal tissues. In: Perez CA, Brady LW, editors. Principles and practice of radiation oncology. 5th ed. Philadelphia: Lippincott-Raven; 1997. p. 143–154. O’Connor MM, Mayberg MR. Effects of radiation on cerebral vasculature: A review. Neurosurgery 2000;46:138 –151. Kuhn MJ, Mikulis DJ, Ayoub DM, et al. Wallerian degeneration after cerebral infarction: Evaluation with sequential MR imaging. Radiology 1989;172:179 –182. Burger PC, Mahaley MS, Dudka L, et al. The morphologic effects of radiation administered therapeutically for intracranial gliomas: A postmortem study of 25 cases. Cancer 1979; 44:1256 –1272. Graham SJ, Stanisz GJ, Kecojevic A, et al. Analysis of changes in MR properties of tissues after heat treatment. Magn Reson Med 2000; 42: 1061–1071. Asai A, Matsutani M, Kohno T, et al. Subacute brain atrophy after radiation therapy for malignant brain tumor. Cancer 1989;63:1962–1974. Dooms GC, Hecht S, Brant-Zawadzki M, et al. Brain radiation lesions: MR imaging. Radiology 1986;158:149 –155. Curnes JT, Laster DW, Ball MR, et al. MRI of radiation injury to the brain. Am J Roentgenol 1986;147:119 –124. Curran WJ, Hecht-Leavitt C, Shut L, et al. Magnetic resonance imaging of cranial radiation lesions. Int J Radiat Oncol Biol Phys 1987;13:1093–1098. Buchholz TA. Finding our sensitive patients. Int J Radiat Oncol Biol Phys 1999;45:547–548.