Does Pretreatment Tumor Growth Hold Prognostic Information for Patients with Glioblastoma?

Accepted Manuscript Does pre-treatment tumor growth hold prognostic information for glioblastoma patients? Anne Line Stensjøen, M.D., PhD, Erik Magnus Berntsen, M.D., PhD, Vilde E. Mikkelsen, Sverre H. Torp, M.D., PhD, Asgeir S. Jakola, M.D., PhD, Øyvind Salvesen, PhD, Ole Solheim, M.D., PhD PII:

S1878-8750(17)30324-8

DOI:

10.1016/j.wneu.2017.03.012

Reference:

WNEU 5381

To appear in:

World Neurosurgery

Received Date: 18 November 2016 Revised Date:

1 March 2017

Accepted Date: 2 March 2017

Please cite this article as: Stensjøen AL, Berntsen EM, Mikkelsen VE, Torp SH, Jakola AS, Salvesen Ø, Solheim O, Does pre-treatment tumor growth hold prognostic information for glioblastoma patients?, World Neurosurgery (2017), doi: 10.1016/j.wneu.2017.03.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Title: Does pre-treatment tumor growth hold prognostic information for glioblastoma patients? Authors: Anne Line Stensjøen1,2 M.D., PhD, Erik Magnus Berntsen1,2 M.D., PhD, Vilde E. Mikkelsen3,

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Sverre H. Torp3,4 M.D., PhD, Asgeir S. Jakola5,6,7 M.D., PhD, Øyvind Salvesen8 PhD, Ole Solheim5,9,10 M.D., PhD Affiliations:

Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU - Norwegian

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University of Science and Technology, Trondheim, Norway

Department of Radiology, St. Olavs University Hospital, Trondheim, Norway

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Department of Laboratory Medicine, Children´s and Women´s Health, Faculty of Medicine,

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NTNU - Norwegian University of Science and Technology, Trondheim, Norway 4

Department of Pathology and Medical Genetics, St. Olavs University Hospital, Trondheim,

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Norway

Department of Neurosurgery, St. Olavs University Hospital, Trondheim, Norway

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Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden

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Institute of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska Academy,

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Gothenburg, Sweden.

Department of Public Health and General Practice, Faculty of Medicine, NTNU - Norwegian

University of Science and Technology, Trondheim, Norway 9

Department of Neuroscience, Faculty of Medicine, NTNU - Norwegian University of Science

and Technology, Trondheim, Norway

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10

National Competence Centre for Ultrasound and Image Guided Therapy, St. Olavs University

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Hospital, Trondheim, Norway

Corresponding author: Anne Line Stensjøen, MR Center, Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology,

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Postbox 8905, N-7491 Trondheim, Norway. Phone: 0047-40465213, [email protected]

Abbreviations: GTR Gross total resection IDH Isocitrate dehydrogenase

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KPS Karnofsky performance status

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Keywords: Glioblastoma, Longevity, Magnetic Resonance Imaging, Prognosis, tumor growth

MRI Magnetic resonance imaging

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PI Proliferation index

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Abstract Background

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Glioblastomas are highly aggressive and heterogeneous tumors, both in terms of patient outcome and molecular profile. Magnetic resonance imaging of tumor growth could potentially reveal new insights about tumor biology non-invasively. The aim of this exploratory retrospective study

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was to investigate the prognostic potential of pre-treatment growth rate of glioblastomas, after controlling for known prognostic factors.

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Methods

A growth model derived from clinical pre-treatment post-contrast T1-weighted MRI images was used to divide 106 glioblastoma patients into two groups. The “faster growth” group had tumors growing faster than expected based on their volume at diagnosis, while the “slower growth”

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group had tumors growing slower than expected. Associations between tumor growth and survival were examined using multivariable cox regression and logistic regression. Results

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None of the known prognostic factors were significantly associated with tumor growth. An extended multivariable cox model showed that during the first twelve months of follow up, there

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was no significant difference in survival between faster and slower growing tumors. However, beyond 12 months follow up, there was a significant, independent survival benefit in having a tumor with slower pre-treatment growth. In a multiple logistic regression model including patients receiving both radiotherapy and chemotherapy (n=82), slower pre-treatment growth of the tumor was shown to be a significant predictor of two-year survival (Odds Ratio 4.4). Conclusion 3

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Pre-treatment glioblastoma growth harbors prognostic information. Patients with slower growing

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tumors have higher odds of survival beyond two years, adjusted for other prognostic factors.

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Introduction The clinical course in patients with glioblastoma is heterogeneous and prognostication is difficult 2

Despite advances in imaging and molecular biology, the most

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in individual patients.1,

established and strongest prognostic factors are still age at diagnosis and preoperative Karnofsky performance status (KPS). In addition, treatment associated factors such as surgical extent of

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resection, radiotherapy, and temozolomide treatment are linked to survival.3-5

It is frequently observed that some glioblastomas grow rapidly after the diagnostic MRI scan,

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before therapy is initiated, while others are seemingly more stable. For low-grade gliomas, tumor growth rate is an independent prognostic marker.6,

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Still, few studies have investigated the

prognostic potential of growth for untreated glioblastomas. A small study including treated gliomas indicated an association between slower growth rates and longer survival for all

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astrocytomas, but did not evaluate survival in glioblastoma patients alone.8 Other studies have proposed that mathematical models of glioblastoma growth kinetics can predict IDH1 status,9 and may have a prognostic role.10

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In this exploratory study, we sought to investigate the prognostic potential of measured growth in untreated glioblastomas. We also sought to assess potential associations between tumor growth

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and established prognostic factors.

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Material and methods Patients

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We retrospectively evaluated all patients with histopathologically verified glioblastoma who were diagnosed at our hospital between January 2004 and May 2014. The inclusion criteria in this study were (1) patients ≥18 years, (2) histopathological diagnosis of glioblastoma, (3) no

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previous history of brain tumor, (4) at least two post-contrast T1-weighted MRI scans before surgery. Patients were excluded if the time interval between the two MRI examinations was less

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than 14 days, if the tumor was non-contrast enhancing, or if they had gliomatosis cerebri according to radiological criteria.11 Tumor growth data concerning the same patients have previously been reported.12 The study was approved by the Regional Ethics Committee (Central) as part of a larger project (references 2011/974 and 2013/1348), and adhered with the

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Declaration of Helsinki. Most patients had provided informed consent to be included in a related glioma outcome study (reference 2011/974), and the regional ethics committee waived informed

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consent for retrospective evaluation of patient data for the remaining patients.

Magnetic resonance imaging

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Two preoperative, post-contrast, T1-weighted magnetic resonance scans of each patient were retrieved from the hospital’s radiology database. These were images obtained as a part of the routine preoperative investigation using 1T, 1.5T or 3T magnetic resonance scanners. Scan parameters and their impact on image segmentations have been reported previously.12 The first scan, hereafter referred to as the diagnostic scan, was obtained at one of 15 local hospitals. The second scan, hereafter referred to as the preoperative scan, was obtained at St. Olavs University Hospital, usually the day before surgery, and used for intraoperative neuronavigation. 6

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Tumor growth The tumor volume at each MRI scan was segmented on the post contrast T1-weighted images by

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one of the authors (A.L.S.) using the software BrainVoyager QX (Brain Innovation, Maastricht, the Netherlands), and verified by a neuroradiologist (E.M.B.). The segmentation method has previously been described in detail.12 Tumor volume was defined based on the border of the contrast-enhancing rim of the tumor, and included all non-enhancing tumor tissue enclosed by

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this (i.e. necrosis).

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As a quality control, tumor growth was inspected on preoperative T2/FLAIR images in addition to the post-contrast T1-weighted images. Lesions on T2 images beyond the contrast enhancing tumor were classified into edema and non-enhancing tumor, based on the criteria of the VASARI features.13 Significant non-enhancing tumor compartments were evaluated for growth by a neuroradiologist (E.M.B), using diameter measurements and the RANO criteria.14 Edema was

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evaluated visually for significant volume changes. The results of this quality control can be found in Supplementary Figure 1. All the tumors with a growing non-enhancing tumor

Growth groups

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compartment had concurrent extensive growth of the contrast enhancing tumor.

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In a previous study, tumor volumes and scan interval were used to estimate glioblastoma growth dynamics, based on the assumption that all tumors follow the same growth pattern.12 Tumor growth rates correlated with tumor volume, indicating that point estimates of growth rate will be a poor representation of tumor biology. Since we also found that the volume of necrosis increases faster in lager tumors, and since intracranial space is limited, we further assumed that glioblastomas eventually approach a plateau phase with slower growth. Based on these assumptions we found that glioblastoma growth was best fitted by a gompertzian growth model, 7

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with a declining growth rate over time.15 Figure 1 shows the gompertzian growth curve with parameters fitted from the observed tumor growth. Under the assumption that all glioblastomas

and predict future growth of a tumor with one known volume.

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follow the same gompertzian growth pattern, this curve can be used to estimate previous growth

In the present study, the gompertzian growth model was used to divide the patients into two groups, based on how their tumor growth related to the growth model (Figure 1). The “faster

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growth” group was defined as tumors with larger volume increase than expected from the model,

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while a “slower growth” group included tumors with a smaller volume increases than expected. Examples of MRI scans of tumors in the two groups are presented in Figure 2. This dichotomization is used in the following survival analyses.

Clinical data collection

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Clinical characteristics, treatment data, and survival was either retrieved from a prospectively collected research database, or retrospectively retrieved from hospital records. The clinical data retrieved were sex, age at diagnosis, KPS, treatment and overall survival. KPS was mostly

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retrieved from the research database, but in 25 cases with missing data, KPS was estimated after retrospective review of hospital records. Four different treatment variables were recorded;

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Complete resection of enhancing tumor (GTR) (yes/no), chemotherapy treatment (yes/no), radiotherapy (yes/no) and corticosteroid treatment (yes/no). Due to the difficulties in evaluating contrast enhancement in the rim of the resection cavity, GTR calculated from an ellipsoid formula on post-operative MRI scans was defined as residual volume of less than 0.175 ml, as done by others.16 A more detailed categorization of extent of resection was not performed, since the benefit of subtotal resection is not settled.17, 18 Chemotherapy treated patients were defined as patients who received temozolomide within the first six months after surgery, either before 8

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radiotherapy, concomitant, adjuvant or any combination of these. Radiotherapy treated patients were defined as patients who received radiotherapy in any regime during follow-up. Corticosteroid treatment was defined as any corticosteroids given before or between the two

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retrieved MRI examinations. Overall survival was defined as the time from first surgery to death. Patients were censored if alive at the end of the study (June 2016), which was 24.5 months after

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the surgery of the last included patient.

Histopathology and molecular markers

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A medical student (V.E.M), in close collaboration with an experienced neuropathologist (S.H.T.), reviewed all tissue samples and confirmed the glioblastoma diagnosis in all included patients, according to the 2007 WHO classification.3 All cases underwent immunohistochemical analyses. Paraffin sections, 4 µm thick, were incubated with monoclonal antibodies using

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standard immunohistochemical analyses using an automated immunohistostainer (DAKO Techmate 500). In 12 cases for IDH1 and 9 cases for Ki-67/MIB-1, formalin-fixed paraffinembedded frozen sections were used instead of paraffin sections. For determination of

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proliferative activity, the sections were incubated with Ki-67/MIB-1 (dilution 1:800 or 1:50; Dako, Glostrup, Denmark). A Ki-67/MIB-1 proliferation index (PI) was calculated as a

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percentage of distinct immunoreactive tumor cell nuclei in areas with highest labeling density (“hot spots”). IDH1 mutant protein status was examined by incubation with the IDH1 R132H antibody (dilution 1:100, Dianova, Hamburg, Germany). In each staining experiment, positive controls were included, and in the negative controls the primary antibodies were omitted. According to the recent revision of the WHO classification criteria, immunohistochemistry against IDH1 R132H is considered adequate for evaluation of IDH mutation status in patients

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older than 55 years, while mutation status should be confirmed by IDH gene sequencing in younger patients.19 Gene sequencing was not available for this cohort.

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Statistical methods Exploratory statistical analyses were performed using Stata/IC 13.1 for Windows (32-bit) and R version 3.1.2. The statistical significance level was set to P <.05. Descriptive statistics were

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assessed in Stata, and possible associations between the growth groups and the different variables were investigated using Wilcoxon Rank-sum tests or Fisher’s exact/Pearson Chi square

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tests, as appropriate. Quantitative variables were treated as continuous in all analyses. The outcome in all cox proportional hazards models were overall survival. In R, unadjusted cox proportional hazards models were fitted for the following variables: Age, KPS, Ki-67/MIB-1 PI, Preoperative tumor volume, GTR, Chemotherapy, radiation, corticosteroids and Slower growth.

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To determine the independent prognostic value of glioblastoma growth, a multivariable cox proportional hazards model was fitted, including previously established prognostic factors and significant factors from unadjusted models, as reported in Supplementary Table 2. The

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proportional hazards assumption was tested for all variables using Schoenfeld residuals.20 Violation of this assumption lead to extension of the cox proportional hazards model, to account

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for time-varying effects of the growth variable.21

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Results Participants

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262 patients were evaluated for inclusion in the study, and 106 of these were included in the analyses. Reasons for exclusion are reported in Figure 3. All patients were followed until death

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or the end of the study.

Descriptive data

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Of the 106 patients, 34 were female (32%). The mean age at diagnosis was 62.9 ±11.7 years. The median preoperative KPS was 80 (range 40 to 100), and 18 of the patients (17%) had a KPS below 70. Median Ki-67/MIB-1 PI was 13.2% (range 1.4-57.3%). Two patients had tumors immunopositive for the IDH1 R132H mutation. The distributions of the treatments received are

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presented in Table 1. Biopsy only had been performed in 17 cases (16 %). There was no missing data for any of the variables. As seen in Table 1, there were no significant associations between

Outcome data

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patient characteristics and established prognostic factors and fast versus slow growth.

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Median overall survival for the patients in this study was 12.6 months (95% CI 10.1-15.4 months). One-year survival was 52.8%, while two-year survival was 19.8%.

The prognostic potential of glioblastoma growth Figure 4 shows the unadjusted Kaplan-Meier plot for the two growth groups. The Kaplan-Meier plot showed that the two survival curves crossed, indicating that the proportional hazards assumption was violated for the growth variable. This was confirmed in both unadjusted and multivariable cox regression models, as testing using Schoenfeld residuals showed that the 11

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violation was significant (see Supplementary Table 1 and 2, with results from cox regression models). This would make inferences from standard cox models inaccurate. To account for the non-proportional hazard functions of the growth groups, an extended cox model was developed.

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This model allowed different hazard ratios between the growth groups in the first 12 months of follow-up and the later follow up for all patients. The results from this analysis are shown in Table 2. In the first 12 months of follow-up, growth group was not significantly linked to

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survival. However, after 12 months follow-up, the hazard rate was significantly lower in patients

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with tumors with slower growth (hazard ratio 0.41 (95% CI 0.21-0.78), P=0.007).

Tumor growth as a predictor of two-year survival

The extended cox regression model suggested that slower tumor growth might be a predictor of longevity. Of patients who survived more than two years, 15 (71.4%) had a slower growth than

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expected, while 6 (28.6%) had a faster growth. All eight patients (7.5%) who had survived more than 3 years at the time of analysis belonged to the slower growth group. Associations between two-year survival and clinical variables are shown in Supplementary Table 3. We explored

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predictors of two-year survival in a multiple logistic regression model, including known prognostic factors. Patients who had not received either temozolomide chemotherapy or

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radiotherapy were excluded, as this corresponded perfectly with not surviving beyond two years. For the remaining 82 patients, the logistic regression model showed that slower growth was a significant predictor of two-year survival, after controlling for age, KPS and GTR. Tumors with slower growth had 4 times higher odds of survival beyond two years, compared to patients with faster growing tumors (Table 3).

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Results using different growth pattern In the review process, we were asked to supplement our findings with another way of stratifying tumor growth. We then used a linear radial growth curve, which was fitted as previously

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described.12 The tumors were stratified according to whether they grew slower or faster than expected, based on this growth pattern, and these groups were used for additional survival analyses. The results of these analyses were similar to the results using the gompertzian growth

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pattern (Supplementary Table 4-6).

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Discussion Key results

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In this exploratory study, we found that slow radiological tumor growth in glioblastomas was associated with survival. While there was no significant difference in survival between faster and slower growing tumors during the first twelve months of follow up, there was a significant

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survival benefit in having a tumor with slower growth beyond 12 months. In a multiple logistic regression model including the patients who received both chemotherapy and radiotherapy,

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slower growth of the tumor was shown to be a significant predictor of two-year survival. None of the clinical variables, including established prognostic factors, were significantly associated with radiological tumor growth. Pre-treatment tumor growth harbors prognostic properties in glioblastoma, and holds potential to capture differences in biological properties in vivo.

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Growth as indicator of tumor biology

While molecular classification of lower grade gliomas has been successful in terms of predicting survival,2 more heterogeneous and conflicting results are reported for glioblastoma in

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comprehensive genetic studies.2, 22 The Cancer Genome Atlas identified four clusters of genetic alterations in glioblastomas, but the prognostic value seems modest.23 A limitation of molecular

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studies is the heterogeneity of genetic profile within the same tumors and over time.24, 25 In contrast to molecular and genetic markers, radiological assessment of glioblastomas holds the potential to readily investigate both the entire tumor at once, and changes over time. Growth rates and response to treatment can be assessed from conventional structural MR sequences.14, 26 More advanced imaging modalities have also been shown to harbor biological information, including perfusion and diffusion MRI and PET-imaging.27, 28 However, so far studies exploring

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potential prognostic features from preoperative MRIs have been static in nature, i.e. assessing data at one time point.29-31

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Tumor growth is not associated with clinical parameters While advanced age is a strong negative prognostic factor in glioblastoma32 that is also linked to molecular profiles, we did not find a significant association between preoperative growth and

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age. This might indicate that the poor survival in the elderly may reflect response to therapy, treatment nihilism, or diagnostic lead-time bias, more than the speed of growth of the disease.

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Further, we did not find an association between KPS and preoperative tumor growth. The prognostic value of functional status may in part reflect the tradition for avoiding interventions in functionally dependent patients, i.e. with lesions in eloquent regions, and does not always reflect the extent or the aggressiveness of the disease.

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Ki-67/MIB-1 PI was not a prognostic marker in this study, and while some authors have previously shown it to be a weak prognostic marker,33 several others have not.34, 35 Ki-67/MIB-1 was not associated with radiological growth groups in our study. As with other molecular

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markers, Ki-67 PI will be based on a sample of the heterogeneous tumor tissue, and might not reflect the biology of the most aggressive part of the tumor. Furthermore, Ki-67/MIB-1 PIs in

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glioblastomas display a wide range of values, which overlap those of low-grade astrocytomas,36, so one may wonder whether this proliferation marker fully unveil the proliferative activity in

human glioblastomas.

Speed of growth and long-term survival Previously suggested prognostic or predictive factors for long-term survival for glioblastoma include age and KPS, in addition to MGMT hypermethylation and IDH1/2-mutations.38,

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Gerber et al. found that patients with long term survival had very heterogeneous genetic profiles,40 which could indicate that molecular profiling will not be helpful in identifying these patients. The present study shows that radiological growth might improve prognostication, as

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long term survival beyond 2 years is more unlikely if fast radiological growth is measured preoperatively.

The long term survival in this study was not linked to IDH-mutations, as eight patients had a

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survival of more than 3 years, and only one of these harbored a tumor with an IDH1 R132H mutation. Although it has been reported that IDH mutations in glioblastoma is linked to

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survival,41 two recent studies reported that IDH-mutations are not essential for long time survival.40, 42

While longevity in this study was linked to tumor growth, survival the first year was not. This may reflect that numerous factors, including extent of disease at diagnosis, treatment given and

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treatment responses affect survival in the short term, while a less aggressive biology of disease is necessary for long term survival. However, the lack of significant survival differences the first year of follow-up may also be due to lack of statistical power, or it may illustrate that the

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association between growth and survival is complex, with several interfering factors.

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Potential implications

The findings in this study, even if confirmed in later studies, will probably be difficult to use directly in a clinical setting. The measurement of growth rates requires two pre-treatment MRI scans, which are often not obtained as part of clinical routine. Still, our results may suggest that in search of biological subgroups with different prognoses or clinical courses, radiological assessments including growth rates may be a way to advance. As a research field this is much more unexplored than for example tumor genetics and molecular biology. 16

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While our study shows that long term survival is unlikely with fast growing tumors, clinicians should not withhold treatment from an otherwise stable patient with a fast growing tumor. However, tumor growth might be included in a larger decision algorithm, or as suggested by

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Wang et al., as part of a personalized mathematical model.10 Tumor growth has been used to quantify the invasiveness and proliferative potential of each tumor, which again was reported predictive of IDH1 status and response to radiotherapy.9, 43

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Generalizability

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Patients were included consecutively in a department with a defined geographical catchment region. This enhances external validity of our findings. As previously reported, the patients included in this study had no significant differences in the age and gender distribution compared to excluded patients.12

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It is expected that the incidence of IDH immunoreactive tumors would be low in a glioblastoma patient cohort without any previous history of brain tumors, and this was indeed the case in our study, with only two positive cases. This means that the results presented in this thesis to a large

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degree reflects the growth of IDH-wildtype glioblastomas, and should not necessarily be generalized to IDH-mutated glioblastomas.

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It has previously been shown that the growth rate of glioblastomas correlates with tumor volume, and thus could be expected to change over time.12 A simple point estimate of tumor growth rate would potentially have been easier to implement in the clinic, but would not be an accurate description of tumor biology. In the present study, we instead used a previously described model of glioblastoma growth, and classified the tumors according to whether they grew faster or slower than predicted by the model. Assuming that the model is a correct representation of group-level glioblastoma growth, this classification gives us a more reliable representation of 17

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tumor biology. It should be noted that this growth model still lacks validation in other data sets. While the growth measures underlying this model are similar to the ones recently reported by Ellingson et al,26 independent testing of the assumption of gompertzian growth is needed.

conclusions did not change if this pattern was assumed.

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Limitations

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Another possible growth pattern for glioblastomas is the linear radial growth pattern,12 and our

The main limitation is the exploratory nature of our study with few pre-planned statistical tests.

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This data driven post hoc methodology was a necessity due to the novelty of the study approach, but limits the strength of our conclusions.

Only two of the included patients had a positive immunohistochemistry for the IDH1 R132H mutation. It has been shown that immunohistochemistry is less reliable for detection of IDH

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mutations in patients younger than 55 years.19 However, among the patients who survived more than three years in our study, none were younger than 55, indicating that the probability of false negative IDH status is very low.44

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An important molecular marker for glioblastoma patients is MGMT promoter status.45 The prognostic role of this marker is controversial. While some have found that it is a prognostic

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marker,46 others report that it is only predictive for response to temozolomide, at least in IDH wildtype glioblastomas.47,

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Unfortunately, this genetic marker was not available for this

retrospective cohort. If MGMT status is predictive of temozolomide response, statistical dependence between temozolomide treatment and MGMT status would be expected, and we adjusted for temozolomide treatment in the regression model. In future studies, it will be

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important to evaluate whether the prognostic role of glioblastoma growth is independent of MGMT status.

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In this study, we have investigated glioblastoma growth as seen on contrast enhanced T1weighted images. It is likely that some glioblastomas have a growth characterized more by a diffuse spread of cells, than growth of the tumor bulk.10 This means that the total tumor burden could increase, while the visible tumor on contrast enhanced T1-weighted images stays constant.

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Unfortunately, this diffuse growth of the tumor is difficult to detect or differentiate from edema

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using standard anatomical imaging methods.49 In addition, changes in T2/FLAIR volume may have several different reasons, such as effects from corticosteroid use, and it is difficult to differentiate such changes from true tumor growth.14 When visually inspecting the T2/FLAIR images of all patients, we found that none of the patients had a significant growth of the T2/FLAIR compartment, without concurrent growth of the contrast enhancing tumor. It is

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therefore unlikely that we would have detected any additional tumors with rapid, diffuse spread, by segmenting the T2/FLAIR components.

Our inclusion criteria reduced the number of eligible patients from 262 to 106, which could have

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introduced selection bias. However, as previously reported, there were no significant differences

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in age and sex between included and excluded patients.12 The range of KPS scores in our patient sample corresponds to the values found in various international population based studies.50, 51 We did not examine the survival in the excluded patients. However, the median overall survival in the included patients was 12.6 months (95% CI 10.1-15.4 months), which is only slightly higher than the overall survival of 10.1 months (95% CI 9.4-11.0 months) reported in a registry-based study of unselected glioblastoma patients in Norway.52

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Conclusion Pre-treatment tumor growth harbors prognostic properties in glioblastoma, and holds potential to

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capture differences in biological properties in vivo. For patients receiving both chemotherapy and radiotherapy, having a slower growing tumor gave higher odds of survival beyond two years.

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Due to the exploratory nature of this study, our findings should be subject to validation studies.

Acknowledgements

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We would like to thank M.Sc. Lisa Millgård Sagberg for her contribution to the collection of clinical data.

Funding

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or not-for-profit sectors.

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This research did not receive any specific grant from funding agencies in the public, commercial,

Conflicts of interest

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Ole Solheim is an unpaid member of a national advisory committee on treatment guidelines for brain tumors. Asgeir S. Jakola is an unpaid member of the Swedish National Brain Tumor Trialist Group. The other authors disclose no potential conflicts of interest.

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effect of temozolomide in the treatment of glioblastoma multiforme. Neuro Oncol.

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52.

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Figure captions Figure 1: The sigmoid curve shows the expected growth pattern of glioblastomas on a

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logarithmic time scale, based on growth data from all patients. The points are the observed preoperative total tumor volumes for all patients. Tumors above the curve had a faster growth than expected, while tumors below the curve had a slower growth than expected. The black points are the tumors of patients surviving less than two years, while the red points represent the

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tumors of patients who survived at least two years. The curve is drawn from day 10 with a tumor

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volume of 0.226 mL, corresponding to an arbitrary starting volume of 0.135 mL at day 0. Figure 2: Examples of tumors in the two growth groups. Panel A shows a tumor growing more slowly than expected, with a volume increase from 13.2 mL to 14.3 mL in 17 days. This tumor was expected to have grown to 17.8 mL in 17 days. Panel B shows a tumor growing faster than expected, with a volume increase from 0.7 mL to 11.2 mL in 31 days. This tumor was expected

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to have grown to 2.2 mL in 31 days.

Figure 3: Patient selection diagram with exclusion criteria.

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Figure 4: Survival in glioblastoma patients in two different growth groups. The “faster growth” group was defined as tumors with larger volume increase than expected from a mathematical

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model, while a “slower growth” group included tumors with a smaller volume increases than expected.

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Fast growth

(n=53)

(n=53)

Age (years)

63

64

Gender (n of women)

16

18

KPS

80

70

Preoperative volume (mL)

24.0

28.8

Ki-67/MIB-1 (%)

13.7

12.3

IDH1+ (n)

1

Corticosteroids (n)

45

GTR (n)

15

Chemotherapy (n)

40

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Slow growth

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Table 1: Distribution of different variables between growth groups.

Radiation (n)

49

P-value

Rank-sum

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.08

Statistical test

χ2

.17

Rank-sum

.62

Rank-sum

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.68

.91

Rank-sum

1

1

Fisher exact

41

.32

χ2

15

1

χ2

43

.48

χ2

47

.51

χ2

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Numbers in italic are median values. For treatments, the numbers indicate how many patients who recieved each treatment. Abbreviations: KPS, Karnofsky performance status; GTR,

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Gross total resection.

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Hazard ratio (95% CI)

P-value

Age

1.02 (1.00-1.04)

.11

KPS (10 points)

0.76 (0.62-0.93)

.008

Preoperative volume

1.00 (0.99-1.01)

GTR

0.72 (0.43-1.21)

Chemotherapy

0.27 (0.14-0.53)

Radiation

0.15 (0.06-0.39)

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Table 2: Results of an extended multivariable Cox regression model

Slower growth (<12 months)

1.55 (0.88-2.74)

Slower growth (≥12 months)

.98

.22

<.001

<.001

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Variable

0.41 (0.21-0.78)

.13 .007

Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; GTR, Gross

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total resection.

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Variable

Odds ratio (95% CI)

P-value

Age

0.97 (0.92-1.01)

.16

KPS (10 points)

1.44 (0.87-2.38)

.16

GTR

1.32 (0.40-4.38)

.65

Slower growth

4.42 (1.33-14.74)

.015

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analysis

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Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; GTR, Gross

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total resection.

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*Including only patients receiving both chemotherapy and radiotherapy (n=82)

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Highlights

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• •

Using a gompertzian growth model, tumors were divided into slower and faster growing Established prognostic factors were not associated with glioblastoma growth groups Slower pre-treatment growth was a significant predictor of two-year survival

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•

ACCEPTED MANUSCRIPT Supplementary table 1: Unadjusted cox regression models for each variable, with tests of proportional hazard assumption. Unadjusted cox regression models Variable

Hazard ratio

Schoenfeld residuals test

p-value

Chi-square statistic 3.05

.08

4.28

.04

1.93

.17

0.19

.67

0.06

.80

<.001

1.13

.29

0.05 (0.02-0.12)

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1.03 (1.01-1.05)

.009

KPS (10 points)

0.68 (0.58-0.80)

<.001

Preoperative volume

1.01 (1.00-1.01)

.02

Ki-67/MIB-1 PI

1.00 (0.98-1.02)

.74

GTR

0.80 (0.51-1.26)

.35

Chemotherapy

0.14 (0.08-0.23)

Radiation

<.001

0.04

.84

Corticosteroids

1.55 (0.90-2.65)

.11

0.14

.71

Slower growth

0.74 (0.48-1.12)

.15

7.79

.005

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Age

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(95% CI)

p-value

Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; PI, Proliferation

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Index, GTR, Gross total resection.

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Hazard ratio

Schoenfeld residuals test

p-value

Chi-

(95% CI)

p-value

square

.13

KPS (10 points)

0.77 (0.63-0.93)

.008

Preoperative volume

1.00 (0.99-1.01)

.88

GTR

0.72 (0.43-1.21)

.22

Chemotherapy

0.27 (0.14-0.52)

<.001

Radiation

0.16 (0.06-0.41)

<.001

Slower growth

0.86 (0.56-1.33)

.50

<.01

.93

2.10

.14

.74

.39

1.85

.17

.23

.63

.20

.66

12.23

<.001

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1.02 (1.00-1.04)

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statistic

Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; GTR, Gross total

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resection.

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Two-year

P-value

Statistical Test

survival Yes

(n=85)

(n=21)

Age (years)

65

62

.12

Rank-sum

KPS (score)

70

90

.003

Rank-sum

Preoperative volume (mL)

30.5

16.2

.10

Rank-sum

Ki-67/MIB1 (%)

12.5

15.9

.26

Rank-sum

Male

58

14

Female

27

7

14

6

71

15

23

0

62

21

10

0

75

21

62

14

23

7

Slow growth

38

15

Fast growth

47

6

Yes Chemotherapy (n)

No Yes

Radiation (n)

No

No Yes

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Yes GTR (n)

.89

χ2

.22

Fisher’s exact

.006

Fisher’s exact

.21

Fisher’s exact

.57

χ2

.03

χ2

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Corticosteroids (n) No

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Gender (n)

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No

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Numbers in italic are median values. Abbreviations: KPS, Karnofsky performance status; GTR, Gross total resection.

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Results with patients divided in groups based on linear radial growth Supplementary table 4: Results of an ordinary multivariable Cox regression model with growth groups based on assumption of linear radial growth pattern. Multivariable cox regression model Hazard ratio

p-value

Chi-

p-value

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Variable

Schoenfeld residuals test

(95% CI)

square

statistic

1.02 (1.00-1.04)

.13

KPS (10 points)

0.77 (0.63-0.94)

.009

Preoperative volume

1.00 (0.99-1.01)

.82

GTR

0.73 (0.43-1.22)

.23

Chemotherapy

0.27 (0.14-0.52)

<.001

Radiation

0.16 (0.06-0.42)

Slower growth

0.85 (0.55-1.31)

.02

.90

1.95

.16

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Age

.60

2.05

.15

.24

.62

<.001

.24

.62

.46

8.53

.004

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Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; GTR, Gross total

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resection.

Supplementary table 5: Results of an extended multivariable Cox regression model with growth groups based on assumption of linear radial growth pattern. Hazard ratio (95% CI)

P-value

1.02 (1.00-1.04)

.12

0.76 (0.63-0.93)

.008

Preoperative volume

1.00 (0.99-1.01)

.96

GTR

0.72 (0.43-1.22)

.22

Chemotherapy

0.27 (0.14-0.52)

<.001

Radiation

0.15 (0.06-0.39)

<.001

Slower growth (<12 months)

1.37 (0.77-2.42)

.28

Slower growth (≥12 months)

0.48 (0.25-0.90)

.02

Age

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KPS (10 points)

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Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; GTR, Gross total resection.

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Variable

Odds ratio (95% CI)

P-value

Age

0.97 (0.92-1.02)

.18

KPS (10 points)

1.49 (0.90-2.47)

.12

GTR

1.25 (0.38-4.09)

.71

Slower growth

3.90 (1.18-12.87)

.03

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regression analysis*, with growth groups based on assumption of linear radial growth.

Abbreviations: CI, confidence interval; KPS, Karnofsky performance status; GTR, Gross total resection.

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Conflicts of interest Ole Solheim is an unpaid member of a national advisory committee on treatment guidelines for brain tumors. Asgeir S. Jakola is an unpaid member of the Swedish National Brain Tumor

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Trialist Group. The other authors disclose no potential conflicts of interest.