Cognitive Consequences of the Treatment of Medulloblastoma Among Children

Cognitive Consequences of the Treatment of Medulloblastoma Among Children Jean-Michel G. Saury, PhD* and Ingrid Emanuelson, MD, PhD*† Progress in the treatment of medulloblastoma has resulted in increased survival among children. However, effective treatment, especially radiation therapy, produces negative consequences in the cognitive development of children, in terms of decreased intelligence quotients. Determining the factors underlying this decrease may influence the types of rehabilitation needed by children who undergo treatment for medulloblastoma. We review recent research on the impact of some factors that may underlie the cognitive deficits of pediatric and adolescent survivors, i.e., verbal comprehension, perceptual organization, attention, and processing speed. We assess eight pediatric survivors of medulloblastoma treatment with surgery, radiation, and chemotherapy. Children were assessed twice after diagnosis, using the Wechsler Intelligence Scale for Children-Third Edition. A large decrease in cognitive capacity was evident, as measured by intelligence quotients and factor indices. A raw score analysis of 12 subtests was performed, indicating a slower acquisition of functions and knowledge in the domains of verbal comprehension, perceptual organization, social perception, and psychomotor skills. We discuss issues of social reintegration, and propose that the rehabilitation of pediatric patients must include a program for social reinsertion, in addition to psychoeducational support. Ó 2011 by Elsevier Inc. All rights reserved. Saury J-MG, Emanuelson I. Cognitive consequences of the treatment of medulloblastoma among children. Pediatr Neurol 2011;44:21-30.

Introduction Cancer is the second highest cause of death in children [1]. Medulloblastoma is the most common malignant tumor of the central nervous system in childhood [2]. It occurs mainly

in the posterior fossa, most often in the cerebellum, but can also occur in the cerebral cortex. It is most often diagnosed before age 10 years, with a peak at age 5 years [3]. This tumor can occlude the fourth ventricle, creating high intracranial pressure, and it may expand into the craniospinal axis, spreading metastases [3]. The high risk for metastasis of these brain tumors, in combination with their high radiosensitivity, has resulted in the use of radiation therapy, which has included doses of 35 Gy to the craniospinal axis and a boost of approximately 20 Gy to the primary tumor site [1,4]. Radiation therapy has led to an improved survival rate, but has also exerted adverse effects on neuropsychologic functioning. Evaluations of children who have undergone radiation therapy, chemotherapy, and tumor resection have indicated negative effects on cognitive development, although these effects may not appear until 1-3 years after the completion of treatment [5]. These cognitive deficits are thought to be related to adverse effects on the development of white matter [6-8]. A consensus in the literature maintains that radiation therapy is the major cause of adverse cognitive effects among children treated for medulloblastoma [9]. Surgery [10,11] and chemotherapy [2,12] may also exert negative effects on cognitive function. However, the literature does not acknowledge the effects of combined treatment, because differentiating the effects of different treatments is difficult [13]. Many studies indicate that two factors influence the effects of radiation therapy, i.e., the radiation dose and an age of less than 4 years at time of diagnosis, especially when the two are combined. Children who received a lower dose of radiation (25 Gy, instead of the standard dose of 35 Gy) exhibited less impairment in measures of verbal and visuospatial intelligence [14], verbal fluency, immediate word-list recall, block design, and fine motricity of the dominant hand [15]. In particular, radiation of the craniospinal axis was revealed to exert unfavorable effects on cognitive function [16]. Despite reductions of radiation dose, a large proportion of

From the *Regional Rehabilitation Center for Children and Adolescents, Queen Silvia Children’s Hospital, Gothenburg, Sweden; and †Institution for Clinical Sciences, Department of Pediatrics, University of Gothenburg, Gothenburg, Sweden.

Communications should be addressed to: Dr. Saury; Regional Rehabilitation Center for Children and Adolescents; Queen Silvia Children’s Hospital; Box 221062; 41804 Go¨teborg, Sweden. E-mail: [email protected] Received October 2, 2008; accepted July 6, 2010.

Ó 2011 by Elsevier Inc. All rights reserved. doi:10.1016/j.pediatrneurol.2010.07.004  0887-8994/$ - see front matter

Saury and Emanuelson: Cognition and Medulloblastoma 21

children treated for medulloblastoma, especially at younger ages, continue to experience cognitive delays, academic failure, and a need for special education services [17]. They demonstrate difficulties in finishing school, causing problems in terms of future vocational activity [3], and deficits in reading and spelling skills [18-19], mathematics [20], and acquisition of foreign languages [21]. Other factors were also identified as predictive of cognitive impairments: hydrocephalus at the time of diagnosis, and tumor location in the cerebral hemisphere [22]. The investigation of neuropsychologic outcomes after the treatment of childhood tumors has ranged into studies of behavior [23], psychologic distress [20], psychologic and behavioral disorders [24], quality of life [2526], academic performance [21], intellectual functioning, and specific neuropsychologic functions, particularly verbal ability, memory, attention, and information processing speed. Typically, children with malignant brain tumors experience a period of increasing signs, most commonly morning headaches and nausea. When a tumor is identified, most often via computerized brain scan, the first course of action involves surgery. If a tumor is identified as medulloblastoma, radiation therapy is performed, together with chemotherapy. Neuropsychologic investigations are generally initiated when all these steps have been completed. Intelligence Quotient The most common measure of general cognitive function has been the intelligence quotient. Most studies of intelligence quotients of pediatric cancer survivors indicated a level lower than expected, at around 1-2 standard deviations below the mean. Younger children generally manifest lower intelligence quotients than do older children [2,12,27], although Poggi et al. [24] reported a lower performance intelligence quotient result for older children. Kieffer-Renaux et al. [15] reported that the intelligence quotients of their patients increased by one standard deviation when the radiation dose was lowered from 35 Gy to 25 Gy. In the longitudinal studies of Ris et al. [4] and Palmer et al. [28,29], pediatric survivors exhibited intelligence quotients around the mean at the end of their radiation treatment, and their intelligence quotients decreased thereafter at a rate between 2-4 points per year. Brie`re et al. [30] reported intelligence quotients of 1.5 standard deviations below the mean for performance intelligence, and 1 standard deviation below the mean for verbal intelligence, at their first assessment, with the same level at follow-up 1-3 years later. Some problems with the calculation of intelligence quotient may explain the differences between studies. First, most authors use the Wechsler scales, which are well validated and demonstrate very good reliability. However, the comparison of results when using different scales becomes problematic. With subjects younger than 6 years, the primary scale should be used, whereas the children’s scale should be used until age 15 years, and then the adult scale.

22 PEDIATRIC NEUROLOGY Vol. 44 No. 1

Intelligence quotients from different scales are measured by different combinations of subtests, and are grounded on different sample norms. Second, some authors use abbreviated versions of the Wechsler scales, e.g., Palmer et al. [28] used three subtests (block design, similarities, and information), whereas Mabbott et al. [9] used four subsets (block design, coding, vocabulary, and information). Abbreviated intelligence quotients are not as valid as full-version intelligence quotients. Moreover, the use of an abbreviated intelligence quotient measure does not permit differentiation between verbal and perceptual reasoning abilities. Third, the Wechsler scales are regularly updated with new versions. The intelligence quotient calculated in one version is different from that in another because some tests have been modified or removed, new tests have been introduced, and the mode of calculating the intelligence quotient has been changed. The research challenge involves evaluating the premorbid cognitive level of children, i.e., their cognitive capacity before the emergence of their tumor. Studies have used the standard means for a reference population, and considered the difference between the actual result and the mean as a measure of cognitive decline after treatment. Butler and Haser [13], for instance, considered an intelligence quotient lower than 80 standard points as a characteristic of cognitive impairment. This approach presents the problem of presupposing that children’s premorbid capacity is normally distributed around the mean, which is not likely because of the small sample sizes. On the other hand, the control group used by Riva et al. [2] had a mean intelligence quotient of 116 points, which is well over 100. To gain a better knowledge of rate of development, Palmer et al. [28] compared the results of their subjects in terms of raw scores with their development in the general population. They reported that the declining trend in age-adjusted scaled scores was not evident in the analysis of raw score values. They found increases in each of the subtests administrated (information, block design, and similarities), but at a lower rate than in the general population. They concluded that children treated for medulloblastoma demonstrate a decline in intelligence quotient values because of an inability to acquire new skills and information at a rate comparable to that of their healthy same-age peers, as opposed to a loss of previously acquired information and skills. Much research has been devoted to discerning some factor underlying the cognitive deficits measured by the intelligence quotient. Four domains in particular have been examined: verbal ability, perceptual skills, attention/working memory, and processing speed. Verbal Ability and Perceptual Reasoning Skills Dennis et al. [12] reported that the processing of visuospatial information measured by the performance intelligence quotient was impaired in most patients (especially younger

patients). On the other hand, those authors revealed that the ability to gain new knowledge, as measured by the verbal intelligence quotient, was related to the amount of time since treatment, i.e., the longer after diagnosis, the lower the verbal intelligence quotient. Whereas some authors stated that the index of perceptual reasoning (i.e., the performance intelligence quotient) was lower than that of verbal ability (i.e., the verbal intelligence quotient) [12,15,28,31-33], some reported no differences between the visuospatial and verbal indices [27,29], and others described a verbal intelligence quotient lower than the performance intelligence quotient [4]. Attention and Working Memory A number of studies attempted to explain deficits of intelligence quotient in terms of an inability to allocate sufficient attention resources for cognitive activities. In a sample of 40 tumor survivors (including 18 with medulloblastoma), Reddick et al. [19] reported that the primary consequence of reduced white matter after radiation treatment comprised attention deficits leading to declining intelligence quotients and academic achievement. Stienlin et al. [10] stated that patients treated only with surgery for benign cerebellar tumors retained intact intellectual ability, but exhibited deficits in attention functions, in addition to problems with memory and processing speed. Deficits in focused attention, sustained attention [34], and selective attention [35] have been reported. Attention and organizational skills were impaired in 46% of the patients examined by Johnson et al. [36]. Brie`re et al. [30] reported that the freedom from distractibility index of the Wechsler Intelligence Scale for Children-Third Edition was the only index that declined at follow-up. Attention deficit was not evident during the first assessment, but appeared at the follow-up 5 years after diagnosis. The main effect involved a decrease in performance on the arithmetic subtest, whereas the subjects manifested no decrease in the digit span subtest. The authors explained this surprising result by stating that the decline only affected complex attention tasks, whereas simpler tasks such as the digit span test remained unaffected. The reason for the decline in complex attention involves the loss of white matter, which affects widely distributed systems such as attention, and disrupts normal prefrontal development. King et al. [37] reported that children with cerebellar tumors exhibited impairments in working memory. Micklewright et al. [38] demonstrated working memory impairments in their comparison of children with cerebellar tumors and children with third ventricle tumors. Processing Speed Mabbott et al. [9] investigated groups of children treated for brain tumors with surgery alone or in combination with radiation therapy. They reported that neither age at diagnosis nor time since diagnosis predicted intellectual

outcomes. They also stated that sustained attention and working memory were not affected by treatment. The only factor affected by radiation treatment was processing speed. Those authors argued for considering processing speed as a core deficit predicting cognitive decline, so that low processing speed, which is related to impairments in the development of white matter, yields poor acquisition of skills and knowledge. Other Background Factors Factors that have not been well investigated include the influence of socioeconomic status and family variables [39], but Palmer et al. [29] described a difference in baseline performance, such that higher parental education corresponded to higher intelligence quotients at first assessment. Rehabilitation There is growing awareness of the need to monitor the disabling effects of cancer treatment in childhood survivors, and to develop new methods of rehabilitation specifically aimed at remedying these effects [13], e.g., the proposals of Butler and Copeland [40]. Hence we must focus on the efficacy of follow-up programs in the rehabilitation of childhood cancer survivors [41]. We conclude that some support exists for the claim that treatment of medulloblastoma tumors with radiation therapy leads to some level of intellectual disability. On the other hand, the results are inconclusive regarding whether this impairment affects mostly perceptual reasoning skills or abstract verbal ability. We agree with Reimers et al. [42] that studies have not produced a distinct neuropsychologic profile of childhood brain tumor survivors. Interestingly, two of the most recent studies reported completely different results, explaining cognitive decline in terms of deficits in complex attention [30] or processing speed [10]. Intelligence quotient has apparently constituted a good measure of intellectual disability, because it enables comparisons between studies. However, studies have not used the full range of measures provided by the Wechsler scales, in regard to the four factors of verbal ability, perceptual skills, attention/working memory, and processing speed. Here, we focus on explaining the cognitive deficits of children treated for medulloblastoma, and examine intelligence quotients as well as the four factors (verbal ability, perceptual skills, attention/working memory, and processing speed), with the aim of shedding some light on the controversy between the complex attention and the processing speed models. Patients and Methods The difficulties in performing studies of pediatric cancer survivors have been described by Moore [7], who stated that small size samples, missing data, and variations in treatment diminish the possibility of using statistical methods to detect main effect variations.

Saury and Emanuelson: Cognition and Medulloblastoma 23

Participants Participants were selected from the records of patients who had undergone a rehabilitation assessment twice after the end of treatment for medulloblastoma. The inclusion criterion required that patients were assessed neuropsychologically twice with the Wechsler Intelligence Scale for Children-Third Edition [43]. This study was approved by the Regional Ethics Review Board at the University of Gothenburg (Gothenburg, Sweden). Eight children diagnosed and treated for medulloblastoma were selected at a Swedish rehabilitation facility. The age of patients at diagnosis varied between 4-11 years (mean, 7.8 years; standard deviation [S.D.], 2.3 years). The tumor was localized in the posterior fossa in five patients (in the cerebellum in two cases, in the fourth ventricle in two cases, and in both locations in one case), in the right frontal lobe in two patients, and in the parietal lobe in one patient. Four children had developed hydrocephalus, with signs of metastases in one case. The tumor was radically operated upon in four cases. All children were treated with radiation therapy and chemotherapy, according to standard protocols. Radiation therapy consisted of a boost to the tumor site of 35-78 Gy, and a dose between 43-55 Gy to the craniospinal axis. Adjuvant chemotherapy consisted of a mix of vincristine, lomustine, cisplatin, and cyclophosphamide. One child received with a high dose of methotrexate.

Procedures The children were assessed with the Wechsler Intelligence Scale for Children-Third Edition as part of a formal assessment on two occasions after the radiation treatment. The initial assessment was performed 2.9 years after diagnosis (S.D., 1.6 years; range, 1-6 years), and the followup assessment was performed 5.1 years after diagnosis (S.D., 2.1 years; range, 2-8 years). The mean time between the first and second assessments was 2.3 years (S.D., 1.0 years; range, 1-4 years). These data are recapitulated in Table 1. We used the Wechsler scale because the development of its third version permitted a more detailed analysis of the parameters underlying the decrease in intelligence quotients exhibited by child tumor survivors. The Wechsler Intelligence Scale for Children-Third Edition provides three intelligence quotients and four factor indices. The quotients include a fullscale intelligence quotient, which is a measure of general cognitive ability, a verbal intelligence quotient, which is a measure of abstract verbal ability, and a performance intelligence quotient, which is a measure of perceptual reasoning skills. The four factors comprise verbal comprehension, which is a measure of verbal abstract ability, perceptual organization, which is a measure of abstract perceptual skills, freedom from distractibility, which is a measure of attention and working memory, and processing speed, which is a measure of psychomotor tempo. All quotients and indices were computed during the initial and followup assessments, and were compared with normative samples by means of a one-sample t test with a test value of 100, which is the mean for the

normative sample. This procedure permitted an assessment of the relative differences in our patients’ performance compared with their healthy peers. The differences between results during the initial assessment and those at the follow-up assessment were then computed for each intelligence quotient and each factor, using a paired t test. This procedure allowed for the assessment of intellectual development in our child survivors from the first to the second assessment. In addition to the calculation of the three intelligence quotients and four factors, we computed the raw score values of each subtest according to the method of Palmer et al. [28], and in accordance with our clinical practice. Those authors demonstrated that cognitive decline is a consequence of a lower rate of skill acquisition, and not a consequence of loss of function. The focus on raw score values permitted an evaluation of the rate of acquiring cognitive abilities. For each patient, we computed the progress on each subtest between initial and follow-up assessments by subtracting the raw score at initial assessment from the raw score at follow-up assessment for that subtest. We then computed the expected progress on each subtest by subtracting the expected raw value for the mean age norm of each patient at the time of the initial assessment from the corresponding value at the time of follow-up assessment. The rate of skill acquisition for each patient in each particular subtest was computed as the quotient of a patient’s progress on the subtest in terms of difference in raw points divided by the expected progress in terms of population mean raw value difference. If this rate is below 100, it means that a patient’s rate of skill acquisition is slower than expected. If the rate is over 100, it means that it is better than expected. The investigation by Palmer et al. [28] only involved three subtests and compared different editions and versions of the Wechsler test. In our case we compared the 12 subtests of the same version of the Wechsler test. However, our sample is small. To obtain reference values for our raw data, we used the expected mean raw value for each subtest and each child according to norms from the Swedish standardization sample [44]. The raw-score differences were examined using the Wilcoxon signed-rank test. All data were computed with the Statistical Package for the Social Sciences, version 17 (www.spss.com).

Results First we will present the results concerning intellectual functioning during the initial assessment, approximately 3 years after diagnosis. Afterward, we will examine the results of the follow-up assessment and compare them with those of healthy peers. Moreover, we will examine the difference in pediatric survivors’ performance between initial and follow-up assessments in terms of intelligence quotients and factors. Finally, we will examine the raw score differences between initial and follow-up assessments.

Table 1. Mean age, standard deviation, and range at diagnosis, initial and follow-up assessments, and mean times between diagnosis, initial assessment, and follow-up assessment

Age at diagnosis (yr) Age at initial assessment (yr) Age at follow-up assessment (yr) Time between diagnosis and initial assessment (yr) Time between initial and follow-up assessment (yr) Time between diagnosis and follow-up assessment (yr)

24 PEDIATRIC NEUROLOGY Vol. 44 No. 1

N

Mean Number of Years

Standard Deviation

Range

8 8 8 8 8 8

7.8 10.6 12.9 2.9 2.3 5.1

2.3 1.7 1.5 1.6 1.0 2.1

4-11 8-12 11-15 1-6 1-4 2-8

Intellectual Functioning During Initial Assessment

Intellectual Functioning During Follow-Up Assessment

The results for intelligence quotients and the four factors are presented in Fig 1. The mean full-scale intelligence quotient was very low (mean, 69.4; S.D., 14.2; range, 4284), i.e., 2 standard deviations below the mean for the age group norms (P < 0.001). The mean verbal intelligence quotient was also low (mean, 79.1; S.D., 12.3; range, 5694), i.e., 1.3 standard deviations below the mean for the age group norms (P = 0.002). The mean performance intelligence quotient was exceptionally low (mean, 65.5; S.D., 15.8; range, 40-82), i.e., 2.3 standard deviations below the mean for the age group norms (P < 0.001). Two factors were exceptionally low at the first assessment: perceptual organization (mean, 67.7; S.D., 13.1; range, 49-91) and processing speed (mean, 70.7; S.D., 17.5; range, 47-97), both with a value more than 2 standard deviations below the mean for the age group norms (P < 0.001). One factor, freedom from distractibility (mean, 74.1; S.D., 6.8; range, 63-85), was low, with a value 1.7 standard deviations below the mean for the age group norms (P = 0.004). The final factor, verbal comprehension (mean, 84.0; S.D., 13.8; range, 59-102), was 1 standard deviation below the mean for the age group (P = 0.01). These results suggest that the cognitive level of our patients during the first assessment, about 3 years after diagnosis, was unusually low compared with the performance of healthy children. Consistent with the results of Poggi et al. [24] and Dennis et al. [12], visual perception capacity was lower than verbal capacity. These results are also comparable to those of Brie`re et al. [30], whose design was similar to ours. The deficit in visual perception may partly depend on the results for processing speed, which were also exceptionally low. These results of the first assessment suggest (1) that, at the time of the initial assessment, our subjects already exhibited significant cognitive deficits in the more impaired range, especially in their perceptual reasoning skills, their attention capacity, and their processing speed; and (2) that their abstract verbal capacity was relatively preserved compared with the other measures.

The results for the three intelligence quotients and four factors during the second assessment, approximately 5 years after diagnosis, are presented in Fig 1. The mean full-scale intelligence quotient at follow-up was 58.5 points (S.D., 14.7; range, 37-81), i.e., 2.8 standard deviations below the mean for the age group norms (P < 0.001). The mean verbal intelligence quotient was 68.5 points (S.D., 12.8; range, 47-84), i.e., 2.1 standard deviations below the mean for age group norms (P < 0.001). The mean performance intelligence quotient was 56.6 points (S.D., 16.1; range, 35-86), i.e., 2.9 standard deviations below the mean for healthy children (P < 0.001). Three of the four factors exhibited a lower result compared with the first assessment: verbal comprehension, with a mean of 72.6 points (S.D., 9.1; range, 62-85), i.e., 1.8 standard deviations below the mean for the age group norms (P < 0.001); perceptual organization, with a mean of 60.6 points (S.D., 12.5; range 49-86), i.e., 2.6 standard deviations below the mean for the age group norms (P < 0.001); and processing speed, with a mean of 68.7 points (S.D., 15.8; range, 50-88), i.e., 2.1 standard deviations below age group norms (P = 0.001). One factor, freedom from distractibility, with a mean of 78.4 points (S.D., 13.8; range, 63-106), was higher than at first assessment, i.e., 1.4 standard deviations below age group norms (P = 0.03). The results during follow-up 5 years after diagnosis were characterized by (1) a decrease in ability to a level about 2 standard deviations below the mean for all intelligence quotients and factors, except for freedom from distractibility; (2) a specific decrease in perceptual reasoning skills to a very low level; (3) a relatively higher level of attention capacity, as expressed by the freedom from distractibility factor; and (4) a lack of decrease in the processing speed factor to that of the three intelligence quotients.

Figure 1. Intelligence quotients (IQ) and indices during initial and follow-up assessments. VIQ = verbal IQ; PIQ = performance IQ; FSIQ = full-scale IQ; VC = verbal comprehension; PO = perceptual organization; FD = freedom from distractibility; PS = processing speed.

Development of Intellectual Functioning From Initial to Follow-Up Assessment The differences between the first and second assessments revealed a large decrease in the three intelligence quotients. The full-scale intelligence quotient decreased 10.9 points, i.e., 5.5 points per year (P = 0.01). The verbal intelligence quotient decreased 10.6 points, i.e., 5.3 points per year (P = 0.03). The performance intelligence quotient decreased 8.9 points, i.e., 4.5 points per year (P = 0.01). These results are consistent with those of Ris et al. [4], who demonstrated a decrease of 4 points per year. The results are not consistent with those of Palmer et al. [28], whose subjects manifested a decrease of 2 points per year. Two of the four factors exhibited a significant decrease from the initial to the follow-up assessment: verbal comprehension, with an average decrease of 11.4 points, i.e., 5.7 points per year (P = 0.03), and perceptual organization, with an average decrease of 7.1 points, i.e., 3.5 points per year (P = 0.02). One factor, processing

Saury and Emanuelson: Cognition and Medulloblastoma 25

speed, shows a nonsignificant decrease with an average of 2.0 points, i.e., 1.0 per year. One factor, freedom from distractibility, demonstrated a nonsignificant increase from the initial to the follow-up assessment, with an average of 4.3 points, i.e., 2.1 points per year. These results are not consistent with those of Brie`re et al. [30], whose subjects only demonstrated a decrease of function in the freedom from distractibility factor. When examining the subtests included in this factor, Brie`re et al. [30] discovered that both the subtest arithmetic and the subtest digit span had decreased from the first to the second assessment. We observed the same trend for arithmetic but not for digit span, which instead increased substantially. Similarly, our patients performed better on the freedom from distractibility factor during the second assessment. These results suggest that the cognitive decline indicated by intelligence quotient data involves primarily a child’s perceptual reasoning skills and abstract verbal ability, and not attention capacity or processing speed. These results are not consistent with many studies indicating that either the attention capacity or processing speed of pediatric survivors had decreased 5 years after treatment for medulloblastoma. Investigation of Raw Scores for Subtests During Initial Assessment and Follow-Up According to our examination of intelligence quotients and factors during the initial and follow-up assessments, pediatric survivors exhibited a cognitive decline in terms of intelligence quotient, a decrease in their verbal abstract ability in term of low scores on the verbal comprehension factor, and a decrease in their perceptual reasoning skills as measured by the perceptual organization factor. On the other hand, these results did not corroborate a decline in children’s attention capacity, as measured by the freedom from distractibility factor, or a decrease in psychomotor tempo, as measured by the processing speed factor. Next we will examine the raw score gains obtained by our patients from the initial to the follow-up assessment, and compare them with the expected results taken from the normative tables for the same-age reference groups. The results are presented in Table 2, where the expected raw value gain ratios are set at 100% (actual raw value gain O expected raw value gain  100). A raw value gain ratio below 100% indicates that patients performed at a lower rate than expected with reference to the norms corrected for age, i.e., they acquired the skill measured by the test more slowly than their same-age peers. A raw value gain ratio above 100% indicates that patients performed at a higher rate that the reference group, i.e., they acquired the skill measured by the test faster than their same-age peers. We observed a decreasing trend for nine of the 12 subtests when the raw score ratios were examined. The raw score ratios revealed a different picture from what we obtained by analyzing intelligence quotients and

26 PEDIATRIC NEUROLOGY Vol. 44 No. 1

Table 2. Mean gains in raw scores for each subtest between initial and follow-up assessments for patients and population, their mean gain ratios (%), and P values Subjects Population Gain Ratio Mean S.D. Mean S.D. Percentage P Value* Information Similarities Arithmetic Vocabulary Comprehension Digit span Picture completion Coding Picture arrangement Block design Object assembly Symbol search

1.75 2.31 4.37 1.53 1.87 4.05 4.25 1.79 2.00 1.93 2.75 1.28 1.75 5.97 8.43 2.74 1.75 5.97 4.18 1.98 2.25 2.19 1.87 0.99 2.75 2.49 2.50 1.41 7.50 5.95 12.12 4.63 1.50 10.17 7.37 3.07 7.50 4.62 9.62 4.53 1.00 5.68 4.37 2.55 6.12 8.93 5.62 2.01

40 44 71 22 43 121 112 62 20 78 22 109

0.035 0.123 0.332 0.063 0.263 0.605 0.865 0.028 0.107 0.182 0.181 0.083

* P value according to Wilcoxon signed-rank test.

factors: Only one factor, verbal comprehension, was accurately reflected at the raw score level, because its four subtests produced a similar picture of a very low rate of acquisition, between 22-44%. Several subtests are included in the verbal factor: (1) vocabulary (22%), i.e., a measure of a child’s learning ability, fund of information, richness of ideas, and ability to form concepts [45]; (2) information (40%), i.e., a measure of a child’s amount of knowledge, alertness to the environment, awareness of social and cultural background, and attitudes toward school-like tasks; (3) comprehension (43%), i.e., a measure of a child’s ability to draw on previous experiences, knowledge of conventional standards of behavior, and development of social judgment; and (4) similarities (44%), i.e., a measure of concept formation and the ability to categorize events and objects and find relationships that are not at first obvious. The results for the subtest information were significant (P = 0.03), and those for the subtest vocabulary almost reached significance (P = 0.06). These results indicate that our patients manifested a reduced rate of development in their ability to learn from experience, and to enrich their knowledge of social and cultural matters. They were less alert in their environment, less interested in school-like activities, less prone to verbalize their experience, and more prone to difficulties in placing activities into a meaningful category. Palmer et al. [28] reported on the raw-values development in two verbal subtests. For the information subtest, their subjects exhibited a gain of 0.95 points per year, compared with 1.53 points in the general population. In our material, we observed an annual gain of 0.94 points, and 2.19 points in the general population. For the similarities subtest, Palmer et al. [28] reported an annual gain of 0.76 points, compared with 1.52 in the general population, whereas we observed an annual gain of 0.88 points in our patients compared with 2.13 in the general population. Our subjects exhibited a very low rate of skill acquisition for two of the four subtests included in the perceptual

organization factor: (1) picture arrangement (20%), which is a measure of nonverbal reasoning involving planning ability, anticipation, social organization, temporal sequencing, and the ability to interpret social situations; and (2) object assembly (22%), which is a measure of visual organization where a child must anticipate the relationships among the individual parts of a pattern that is not immediately recognizable [45]. Our subjects performed on a level around average in two subtests: (1) block design (78%), which is a constructional task involving spatial relationships and figure-ground separation; and (2) picture completion (112%), which is a measure of visual discrimination requiring visual recognition and the identification of familiar objects. However, none of these results reached significance. The results of Palmer et al. [28] for the block design subtest involved an annual gain of 2.69 points, compared with 4.40 in the general population, whereas our results involved 3.75 points in our patients and 4.81 in the general population. These results indicate that our patients experienced difficulties in inductive reasoning, i.e., anticipating a whole pattern based on its parts. They exhibited a reduced ability to organize stories in temporal sequences, to interpret social situations, and to plan activities. On the other hand, they displayed an average ability to manipulate visible objects in a constructional task, and to identify missing parts in familiar objects. Concerning the attention factor, patients’ performances took different directions: they performed at a lower rate on the arithmetic subtest (71%), which is a measure of numerical reasoning requiring concentration and working memory, and at a higher rate on the digit span subtest (121%), which is a measure of auditory working memory and attention. These results suggest that patients experienced difficulties with numerical reasoning, but a capacity to develop sequential auditory memory and attention. On the processing speed factor, patients also demonstrated results in different directions: they performed at a lower rate on the coding subtest (62%), which is a measure of the speed and accuracy of visual-motor coordination, the speed of mental operations, visual scanning, and visual tracking requiring fine-motor skills. The results for the coding subtest were significant (P = 0.02). Patients performed at a higher rate on the symbol search subtest (109%), which is a measure of perceptual discrimination, speed and accuracy, attention and concentration, shortterm memory, and cognitive flexibility. These results further indicate that our patients demonstrated poor perceptual-motor integration when a task required more than drawing a straight line, i.e., they exhibited difficulties in fine-motor skills. Therefore, the results concerning the analysis of raw-score differences on subtests from the Wechsler Intelligence Scale for Children-Third Edition suggest that: (1) pediatric survivors, as a group, exhibit some level of skill acquisition between their initial and follow-up assessments, even if their intelligence quotient scores and factor indices decrease, a finding consistent with those of Palmer

et al. [28]; (2) great variation occurs in our patients’ rate of acquiring different skills; (3) for three subtests (symbol search, picture completion, and digit span), patients exhibited a higher skill rate than their same-age normal peers, which is a sign of recovery. These results, however, were not significant. (4) The four verbal subtests revealed a slow rate of acquisition, at 22-44% of the rate for same-age healthy children, indicating that pediatric survivors developed more slowly after medulloblastoma treatment than same-age healthy children in their ability to learn from experience and to enrich their knowledge of facts in social and cultural matters. They appeared less alert to their environment, less interested in school-like activities, less prone to verbalize their experiences, and more prone to difficulties in organizing concepts into a meaningful category. These results, however, were only significant for the information subtest. (5) Two perceptual subtests (picture arrangement and object assembly) revealed a very low rate of skill acquisition (between 20-22%), although the results were not significant. These two subtests contain the characteristic in common of requiring the synthesis of parts into a whole without a model [45]. The model must be deduced by the patient, requiring some level of visualization ability. The results suggest that our patients experienced difficulties in planning and organizing activities, in the temporal sequencing of stories, in the ability to interpret social situations, and in the anticipation of relationships between parts not immediately recognizable. The third perceptual subtest (block design) revealed a higher skill acquisition rate (78%), and does not require the same visualization capacity, because the percept is visible to the patient during the test. (6) A significant delay in skill acquisition was indicated by the low rate for subtest coding (62%), which is a measure of fine-motor skills. (7) Patients’ low results for the two subtests, picture arrangement (20%) and comprehension (43%), suggest a lower skill acquisition rate in the domain of social perception than for same-age healthy peers. (8) The rates of information and skill acquisition in this study are consistent with those reported by Palmer et al. [28], who observed that their medulloblastoma patients performed at a rate 49-62% of that shown by healthy, same-age peers. Discussion A consensus maintains that pediatric survivors of medulloblastoma treatment manifest significant cognitive impairments 3-5 years after the end of treatment. The present study supports that claim. In our sample, the Wechsler Intelligence Scale for Children-Third Edition was administered on two occasions, approximately 3 and 5 years after diagnosis. During the initial assessment, our patients manifested significant cognitive deficits in the more impaired range, in terms of low full-scale intelligent quotient, low perceptual intelligence quotient, low perceptual organization factor, low freedom from distractibility

Saury and Emanuelson: Cognition and Medulloblastoma 27

factor, and low processing speed factor, whereas they exhibited verbal intelligence quotients and verbal comprehension factors relatively well preserved compared with the other measures. Hence our patients already exhibited greater cognitive impairment around 3 years after diagnosis than did patients in other studies. These differences are difficult to explain for two reasons: (1) We do not know the premorbid cognitive capacity of our patients, in part because medulloblastoma tumors appear at an age when children have not attended school very long. (2) We cannot separate the effects of radiation therapy from those emerging after surgery or chemotherapy. However, we can identify two factors that may have contributed to the large effects on cognitive functions among our patients. (1) At least half of the children in our study exhibited signs of hydrocephalus. Hydrocephalus was associated with deficiencies in intellectual development because of its effects on visuoperceptual rather than verbal skills [46]. That finding is consistent with findings in our patients, whose visuoperceptual capacities, as measured by the perceptual organization factor, were much lowered compared with their verbal capacities. (2) Our children seem to have received a relatively high dose of radiation. The adverse effects of radiation on the brain were demonstrated to be dose-related. A high dose of radiation is thought to create white matter injuries. Both the occurrence of hydrocephalus and the high doses of radiation used may have contributed to the high level of impairment in our patients during the first assessment. In addition to perceptual intelligence and perceptual organization, the freedom from distractibility factor and the processing speed factor were substantially lower, which also can be explained by white matter injuries. These results are consistent with the claim that intellectual decline can be explained either by a deficit in working memory or attention capacity, as measured by the freedom from distractibility factor, or by disturbances in the psychomotor component, as measured by the processing speed factor. During follow-up approximately 2 years after the first assessment, we obtained unexpected results. First, the already low levels of capacity indicated by intelligence quotients became even lower, at a rate of about 5 points per year. Hence our patients exhibited a cognitive decline, as measured by intelligence quotient, at a faster rate than previously reported. Second, the difference between the verbal and perceptual components was no longer evident, because both had similarly decreased from the initial to the follow-up assessment. How can we explain the abrupt decline in children’s verbal ability, which was relatively preserved at the first assessment, during the period between the two assessments? The reduced rate of acquisition of verbal knowledge in our patients indicates that 3 years after diagnosis, their ability to learn from experience was reduced, they possessed a lower level of knowledge in social and cultural matters, they were less alert to their environment, and they

28 PEDIATRIC NEUROLOGY Vol. 44 No. 1

had lost interest in school-like activities. Armstrong and Briery [5] explained the late cognitive effects in pediatric survivors of brain tumors in terms of radiation affecting the rate of brain growth after treatment, by disrupting the development of myelin and neural connections that should appear as the child grows. Radiation does not exert an immediate effect on cognitive functions already acquired, but affects functions to be developed later. Third, the attention capacity, as measured by the freedom from distractibility factor, had increased between the two assessments. This result is surprising, insofar as many studies have explained intellectual decline as a decrease in subjects’ attention capacities. The rationale behind this explanation posits that if children have difficulties with sustained attention or working memory, it affects their learning capacity and reduces their opportunities to acquire new knowledge. Therefore, that theory was not supported by our results. Fourth, this decrease did not affect the processing speed component, because no measurable difference was evident in the processing speed factor between the two assessments. Our results contrast with those in a number of studies that explained the cognitive decline of pediatric survivors as a decrease in the ability to process information quickly. Mabbott et al. [9], for instance, claimed that because these children may not process information as quickly as their peers, they may not keep pace at school and may miss valuable information, resulting in increasingly larger gaps in their skills and knowledge. Again, our results do not support this theory, because the decline in intelligence quotient of our patients was not related to a similar decrease in processing speed. On the other hand, our patients demonstrated a significantly lower rate of acquisition of psychomotor skills, as measured by the coding subtest. This finding suggests that our patients experienced difficulties with tasks demanding fine-motor ability, such as writing. These difficulties, in turn, exert an impact on school performance, and lead to increased risk for academic failure. Fifth, two subtests of the Wechsler Intelligence Scale for Children-Third Edition (comprehension and picture arrangement) were among those with the lowest rate of development, compared with same-age peers (43% and 20%, respectively), suggesting that our patients had difficulties interpreting social situations, acquiring knowledge in social and cultural matters, and developing social judgment. This finding is not surprising, because pediatric and adolescent survivors of brain tumors often demonstrate social difficulties in addition to their educational problems, preventing favorable psychosocial development [47]. They exhibit significant deficits in social functioning compared with children hospitalized for other illnesses [48], and they often become socially isolated [49]. The importance of school as an environment fostering social development, and the adverse consequences of the disruption of interactions with peers because of hospital treatment, were pointed out by Katz et al. [49] more than two decades ago. They stated that pediatric cancer patients

should return to their pre-illness activities and environments as soon as medically possible. They stressed the necessity of providing children and adolescents with a school reintegration program to facilitate their return to school. In addition to providing information about the effects of cancer to peers and teachers, these children and adolescents often need a social skills training program to promote optimal rehabilitation [47,50]. Such a program was observed to be relevant in terms of peer relationships and the prevention of social isolation [51]. We were able to delineate three parameters that may have contributed to the low performance of our patients in measures of verbal comprehension, perceptual organization, social cognition, and psychomotor skills: (1) high intracranial pressure, (2) high levels of radiation, and (3) limited support for school reintegration. The issue of high intracranial pressure is related to the early diagnosis of brain tumors, and the issue of radiation dose is related to the clinical picture presented by each patient. There is no controversy in stating that early diagnosis decreases the risk for injuries from high intracranial pressure and facilitates treatment. The issue of school reintegration, on the other hand, has not been discussed in detail in the literature concerning the cognitive consequences of brain tumors. The social difficulties of children and adolescents with brain tumors were previously described [48]. We think that a school reintegration program cannot be restricted to providing information to teachers and classmates, as proposed by Armstrong et al. [5]. It must also include social skills training, to optimize social reintegration. Such an approach must be combined with an educational rehabilitation program of the type proposed by Butler and Copeland [40]. Such a program should not be restricted to attention rehabilitation, but should also include remedial programs in other cognitive domains, depending on the child’s or adolescent’s profile of cognitive impairment in connection with school reentry. However, the limitations of the present study restrict our ability to shed light on the complex problems encountered by pediatric and adolescent survivors of brain tumors. The major problems include the low number of patients because of the rarity of medulloblastoma in our country, the lack of a control group, the lack of reliable information on radiation doses and sites, and a lack of background information on earlier school achievements. The present study illustrates the manner in which some children and adolescents treated for medulloblastoma may develop significant impairments in cognitive capacities, which will exert a major impact on their future. Our patients are probably not representative of the common population with brain tumors, as indicated by the large number of studies demonstrating a lower level of disability. However, the fact remains that certain children and adolescents are seriously affected by brain tumors and exhibit the cognitive profiles described in this study, constituting a challenge for their families, rehabilitation professionals, and school teachers. Therefore, it is important to design rehabilitation programs that meet the needs of this special group.

Conclusions The present study focused on a sample of pediatric and adolescent survivors of medulloblastoma, assessed on two occasions approximately 3 and 5 years after diagnosis. During the first assessment, patients manifested surprisingly low levels of cognitive function in the domains of perceptual organization, attention, and processing speed. During the second assessment, they demonstrated a low level in the domain of verbal ability and a continuing decrease in perceptual organization, whereas attention and processing speed only marginally changed. The raw score analysis indicated significant deficits in verbal ability, perceptual organization, social perception, and psychomotor skills. These results demonstrate that cognitive deficits after medulloblastoma do not necessarily affect attention and processing speed. In seeking an explanation for the deficits in verbal ability and social cognition, we advocate concentrating on the reintegration of children and adolescents into school. School is an environment not only for cognitive and educational rehabilitation, but also for social reintegration. Therefore, we suggest that it is not sufficient to design psychoeducational programs for children and adolescents returning to school. Their social reentry must also be planned, and their social skills need to be supported. References [1] Yazigi-Rivard L, Masserot C, Lachenaud J, et al. Le me´dulloblastome de l’enfant. Arch Pediatr 2008;15:1794-802. [2] Riva D, Giorgi C, Nichelli F, et al. Intrathecal methotrexate affects cognitive functions in children with medulloblastoma. Neurology 2002;59:48-53. [3] Palmer SL, Reddick WE, Gajjar A. Understanding the cognitive impact on children who are treated for medulloblastoma. J Pediatr Psychol 2007;9:1040-9. [4] Ris MD, Packer R, Goldwein J, Jones-Wallace D, Boyett JM. Intellectual outcome after reduced-dose radiation therapy plus adjuvant chemotherapy for medulloblastoma: A Children’s Cancer Group study. J Clin Oncol 2001;15:3470-6. [5] Armstrong FD, Briery BG. Childhood cancer and the school. In: Brown RT, editor. Handbook of pediatric psychology in school settings. Mahwah, NJ: Laurence Erlbaum, 2004:263-81. [6] Mulhern RK, White HA, Glass JO, et al. Attentional functioning and white matter integrity among survivors of malignant brain tumors of childhood. J Int Neuropsychol Soc 2004;10:180-9. [7] Moore BD. Neurocognitive outcomes in survivors of childhood cancer. J Pediatr Psychol 2005;1:51-63. [8] Mabbott DJ, Noseworthy MD, Bouffet E, Rockel C, Laughlin S. Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: Correlation with IQ. Neuro Oncol 2006;8:244-52. [9] Mabbott DJ, Witol A, Penkman L, Strother D, Bouffet E. Core neurocognitive functions in children treated for posterior fossa tumors. Neuropsychology 2008;22:159-68. [10] Stienlin M, Imfeld S, Zulauf P, et al. Neuropsychological longterm sequelae after posterior fossa tumour resection during childhood. Brain 2003;126:1998-2008. [11] Huber JF, Bradley K, Spiegler BJ, Dennis M. Long-term effects of transient cerebellar mutism after cerebellar or medulloblastoma tumor resection in childhood. Childs Nerv Syst 2006;22:132-8. [12] Dennis M, Spiegler BJ, Hetherington CR, Greenberg ML. Neuropsychological sequelae of the treatment of children with medulloblastoma. J Neurooncol 1996;29:91-101.

Saury and Emanuelson: Cognition and Medulloblastoma 29

[13] Butler RW, Haser JK. Neurocognitive effects of treatment for childhood cancer. Ment Retard Dev Disabil Res Rev 2006;12:184-91. [14] Grill J, Kieffer-Renaux V, 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;45:137-45. [15] Kieffer-Renaux V, Bulteau C, Grill J, Kalifa C, Vigner D, Jambaque I. Patterns of neuropsychological deficits in children with medulloblastoma according to craniospatial irradiation doses. Dev Med Child Neurol 2000;42:741-5. [16] Grill J, Viguier D, Kieffer V, et al. Critical risk factors for intellectual impairment in children with posterior fossa tumors: The role of cerebellar damage. J Neurosurg 2004;101:152-8. [17] Mitby PA, Robison LL, Whitton JA, et al. Utilization of special education services and educational attainment among long-term survivors of childhood cancer. Cancer 2003;97:1115-26. [18] Mulhern RK, Palmer SL, Merchant TE, et al. Neurocognitive consequences of risk-adapted therapy for childhood medulloblastoma. J Clin Oncol 2005;23:5511-9. [19] Reddick WE, White HA, Glass JO, et al. Developmental model relating white matter volume to neurocognitive deficits in pediatric brain tumor survivors. Cancer 2003;97:2512-9. [20] Mabbott DJ, Spiegler BJ, Greenberg ML, Rutka JT, Hyder DJ, Bouffet E. Serial evaluation of academic and behavioural outcome after treatment with cranial radiation in childhood. J Clin Oncol 2005;23: 2256-63. [21] La¨hteenma¨ki PM, Harila-Saari A, Pukkala E, Kyyro¨nen P, Salmi TT, Sankila R. Scholastic achievements of children with brain tumors at the end of comprehensive education. Neurology 2007;9:296-305. [22] Reimers TS, Ehrenfels S, Mortensen EL, et al. Cognitive deficits in long-term survivors of childhood brain tumors: Identification of predictive factors. Med Pediatr Oncol 2003;4:26-34. [23] Rourke MT, Kazak AE. Psychological aspects of long-term survivorship. In: Schwarz CL, editor. Survivors of childhood and adolescent cancer: A multidisciplinary approach. Berlin: Springer, 2005:295-304. [24] Poggi G, Liscio M, Galbiati S, et al. Brain tumors in children and adolescents: Cognitive and psychosocial disorders at different ages. Psychol Oncol 2005;14:386-95. [25] Sharp LK, Kinahan KE, Didwania A, Stolley M. Quality of life in adult survivors of childhood cancer. J Pediatr Oncol Nurs 2007;24:220-6. [26] Feeny D, Furlong W, Mulhern RK, Barr RD, Hudson M. A framework for assessing health-related quality of life among children with cancer. Int J Cancer 1999;12(Suppl.):2-9. [27] George AP, Kuehn SM, Vassilyadi M, et al. Cognitive sequelae in children with posterior fossa tumors. Pediatr Neurol 2003;28:42-7. [28] Palmer SL, Goloubeva O, Reddick WE, et al. Patterns of intellectual development among survivors of pediatric medulloblastomas: A longitudinal analysis. J Clin Oncol 2001;19:2302-8. [29] Palmer SL, Gajjar A, Reddick WE, et al. Predicting intellectual outcome among children treated with 35-40 Gy craniospinal irradiation for medulloblastoma. Neuropsychology 2003;17:548-55. [30] Brie`re M-E, Scott JG, McNall-Knapp RY, Adams RL. Cognitive outcomes in pediatric brain tumor survivors: Delayed attention deficit at long-term follow-up. Pediatr Blood Cancer 2008;50:337-40. [31] Spiegler BJ, Bouffet E, Greenberg ML, Rutka JT, Mabbott DJ. Change in neurocognitive functioning after treatment with cranial radiation in childhood. J Clin Oncol 2004;22:706-13. [32] Hewitt M. Late effects of childhood cancer. In: Hewitt M, Weiner SL, Simone JV, editors. Chidhood cancer survivorship: Improv-

30 PEDIATRIC NEUROLOGY Vol. 44 No. 1

ing care and quality of life. Washington, DC: National Academic Press, 2003:49-88. [33] Copeland DR, deMoor C, Moore BD, Ater JL. Neurocognitive development of children after a cerebellar tumor in infancy: A longitudinal study. J Clin Oncol 1999;17:3476-86. [34] Reeves CB, Palmer SL, Reddick WE, et al. Attention and memory functioning among pediatric patients with medulloblastoma. J Pediatr Psychol 2006;31:272-80. [35] Mulhern RK, Palmer SL, Reddick WE, et al. Risks of young age for selected neurocognitive deficits in medulloblastoma are associated with white matter loss. J Clin Oncol 2001;19:472-9. [36] Johnson DL, McCabe MA, Nicholson HS, et al. Quality of longterm survival in young children with medulloblastoma. J Neurosurg 1994; 80:1004-100. [37] King TZ, Fennell EB, Williams L, et al. Verbal memory abilities of children with brain tumors. Child Neuropsychol 2004;10:76-88. [38] Micklewright JL, King TZ, Morris RD, Morris MK. Attention and memory in children with brain tumors. Child Neuropsychol 2007; 13:522-7. [39] Patel SK, Carldon-Green B. Commentary: Toward greater integration and specificity in conceptual models of neurocognitive functioning in childhood cancer survivors. J Pediatr Psychol 2005;30:85-8. [40] Butler RW, Copeland DR. Attentional processes and their remediation in children treated for cancer: A literature review and the development of a therapeutic approach. J Int Neuropsychol Soc 2002;8: 115-24. [41] Geenen MM, Cardous-Ubbink MC, Kremer LCM, et al. Medical assessment of adverse health outcomes in long-term survivors of childhood cancer. JAMA 2007;297:2705-15. [42] Reimers TS, Mortensen EL, Schmiegelow K. Memory deficits in long-term survivors of childhood brain tumors may primarily reflect general cognitive dysfunctions. Pediatr Blood Cancer 2007;48:205-12. [43] Wechsler D. Manual for the Wechsler intelligence scale for children-third edition (WISC-III). New York: Psychological Corporation, 1991. [44] Wechsler D. Manual for the Wechsler intelligence scale for children-third edition. Swedish version. Stockholm: Psykologifo¨rlaget, 1999. [45] Sattler JM, Saklofske DH. WISC-III subtests. In: Sattler JM, editor. Assessment of children, cognitive applications. 4th ed. San Diego: J.M. Sattler, Inc, 2001:266-97. [46] Lindquist B. Hydrocephalus in children: Cognition and behaviour. Go¨teborg: Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy at Go¨teborg University, 2007. [47] Barrera M, Shaw AK, Speechley KN, Maunsell E, Pogany L. Educational and social late effects of childhood cancer and related clinical, personal, and familial characteristics. Cancer 2005;104: 1751-60. [48] Bonner MJ, Hardy KK, Willard VW, Anthony KK, Hood M, Gururangan S. Social functioning and facial expression recognition in survivors of pediatric brain tumors. J Pediatr Psychol 2008;33:1142-52. [49] Katz ER, Rubinstein CL, Hubert NC, Blew A. School and social reintegration of children with cancer. J Psychosoc Oncol 1988;6:123-40. [50] Harris MS. School reintegration for children and adolescents with cancer: The role of the school psychologists. Psychol Schools 2009;46:579-92. [51] Kazak AE. Evidence-based interventions for survivors of childhood cancer and their families. J Pediatr Psychol 2005;30:29-39.