Temporal muscle measurements as predictor for outcome in a cohort of IDH-wildtype glioblastoma patients

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Temporal muscle thickness has been suggested as an independent prognostic marker for glioblastoma patient outcome. Various cohort studies show however conflicting results. This study therefore aims to reevaluate the prognostic value of different types of temporal muscle measurements in glioblastoma patients. Methods. A retrospective cohort study was performed including 137 patients diagnosed with IDH-wildtype glioblastoma. Temporal muscle thickness (TMT) and volume (TMV) were measured on preoperative MR-imaging. Next, these measurements were thereafter used in a multivariate Cox survival analysis to identify their possible prognostic value. These results were compared to the literature after systematic review of the Medline database. Results. TMT has a moderate to strong linear correlation with total muscle volume (Pearson r = 0,6; P < 0,001). Glioblastoma patients “at risk for sarcopenia” show similar outcome compared to controls (median overall survival time: 13 months vs 11 months; P = 0,775). In a covariate Cox regression model, none of the temporal muscle measurements (TMT, TMV or sex-specific cut-off points) showed prognostic value for outcome in glioblastoma patients. Conclusion. Temporal muscle measurements show no independent relation to clinical outcome in IDH-wildtype glioblastoma patients. There seems adequate linear correlation of temporal muscle thickness and overall muscle volume. The literature on temporal muscle measurements was found to be severely flawed and should be interpreted with caution. glioblastoma treatment outcome temporal muscle thickness sarcopenia. Figures Figure 1 Figure 2 Introduction IDH-wildtype glioblastoma is the most frequently occurring malignant intrinsic brain tumor in adults affecting 4–6 patients per 100.000 person-years.[ 15 ] Its prognosis is dismal with a median overall survival time of 14,6 months after optimal treatment.[ 18 ] In real world registries however, survival of glioblastoma patients outside trial protocol seems far inferior with an estimated median overall survival time of only 9,3 months.[ 15 ] Current standard-of-care treatment for glioblastoma includes maximal safe surgical resection followed by radiotherapy with concomitant and adjuvant temozolomide.[ 18 ] Different prognostic factors for outcome in glioblastoma patients have been identified. Age and clinical performance scale at diagnosis are important patient related predictors.[ 11 ] Surgically, the aim is to perform a maximal safe resection of the tumor leaving as little contrast enhancing tumor as possible. Extent-of-resection (EOR) is a well-known predictor for outcome where a gross total or even supratotal resection of the tumor implies better survival.[ 10 ] Grabowski et al. furthermore demonstrated residual tumor volume (RTV) to be a superior predictor for outcome compared to EOR.[ 7 ] Finally, molecular characteristics of the tumor have prognostic relevance. Epigenetic methylation of the MGMT (O6-methylguanine-DNA methyltransferase) gene promoter is associated with improved efficacy of alkylating chemotherapy in high-grade glioma and therefore results in superior outcome.[ 8 ] Sarcopenia is defined as a progressive and generalized skeletal muscle disorder associated with adverse outcome in the general population.[ 2 ] The disorder is primarily characterized by low muscle strength, quantity or quality. In cancer patients, sarcopenia at diagnosis has been associated with adverse clinical outcome due to increased chemotherapy toxicity and reduced therapy response, ultimately leading to inferior survival.[ 3 ] Various biomarkers for clinical and scientific use have been proposed.[ 2 ] Grip strength is an easy to use and strong predictor for sarcopenia.[ 2 , 17 ] It is however difficult to assess in retrospective cohort studies when not routinely performed and registered. Muscle quantity can be assessed alternatively by X-ray absorptiometry or using more routinely performed imaging modalities such as CT- or MR-imaging.[ 2 ] Many modalities have been tested for their reliability and accuracy.[ 2 ] Temporalis muscle thickness (TMT) for example correlates well with the cross-sectional area of lumbar vertebral muscles.[ 12 ] TMT might therefore be used as a surrogate biomarker for skeletal muscle mass in glioblastoma patients to identify frail patients “at risk for sarcopenia”.[ 20 ] Furtner et al. were the first to show reduced TMT as an independent negative prognostic parameter in patients with recurrent glioblastoma.[ 5 ] More recently, a meta-analysis of various cohort studies seemed to confirm TMT as a predictive parameter for clinical outcome in glioblastoma patients.[ 16 ] Nevertheless, various other studies could not find a negative prognostic value of TMT measurements.[ 9 , 21 ] The value of TMT as a clinically relevant prognostic factor therefore remains uncertain. This study aims to advance the literature concerning the prognostic value of temporal muscle measurements in glioblastoma patients. It focusses not only on temporal muscle thickness but explores if temporal muscle volume (TMV) correlates better with outcome in molecularly defined glioblastoma patients. Finally, these results are corelated with a critical review of the literature. Material and methods Patient selection and characteristics A retrospective patient cohort study was performed. Patients newly diagnosed with an IDH-wildtype glioblastoma between 2005 and 2023 were included. All cases were histologically confirmed after obtaining surgical specimen. Patients were treated afterwards in accordance with the Stupp protocol.[ 18 ] Hypofractionated adjuvant radiotherapy, as proposed for older and/or frail patients, was allowed for inclusion.[ 13 , 14 ] Relevant clinical parameters were retrieved from the patient files: age at diagnosis, functional status at baseline (Karnofsky Performance Scale), molecular parameters of the tumor (IDH-mutation and MGMT promoter methylation status), residual tumor volume (RTV), completion of concomitant radiotherapy (45 Gy or 60 Gy) and number of adjuvant temozolomide cycles. Patients were excluded if: (1) not all clinical or radiological parameters were available for review; (2) they received experimental treatment within a clinical trial. This study was approved by the ethics committee of Ghent University Hospital (reference: THE-2022-0069). The study was conducted in accordance with the Declaration of Helsinki. Tumor and temporal muscle measurements Residual tumor volume (RTV) was determined on postoperative Gd-enhanced T1 magnetization-prepared rapid acquisition gradient echo (MPRAGE) images obtained within 72h after surgery using semi-automated segmentation software available on the Medtronic Stealthstation S7 (Medtronic, Louisville, CO, US). Temporal muscle thickness (TMT) and volume (TMV) were measured on preoperative T1-MPRAGE images using the same software. In short, muscle thickness was measured perpendicular to the skull on an axial slice parallel to the AC-PC plane. The orbital roof and Sylvian fissure were used as landmarks.[ 4 ] Muscle thickness was measured bilaterally in each patient and the mean value was used for statistical analysis. If the patient was operated on one side before, this side was not measured to reduce the impact of postsurgical muscle atrophy. TMV was measured on the side with the thickest muscle. To identify patients “at risk for sarcopenia” sex-specific cut-off values for TMT were used. These values were introduced before by Steindl et al. who examined TMT values in a healthy Caucasian population.[ 17 ] Cut-off points to identify patients “at risk for sarcopenia” were determined 6,3 mm for male and 5,2 mm for female persons. Statistical analysis The Pearson correlation coefficient was used to determine linear correlation between the various continuous variables. Multivariate Cox regression analysis was performed to investigate the prognostic relevance of the different temporal muscle measurements. Means were statistically compared using the independent-samples student t-test. For all statistical test, a two-sided p-value of 0,05 was used to determine significance. Statistical analysis was performed using the SPSS software v29 (IBM, New York, US). Literature review In order to compare the results of this study with the current literature a review of the literature was performed. The Medline database was searched using the following Mesh terms: glioblastoma, temporal muscle, sarcopenia, survival rate, prognosis, survival analysis and treatment outcome were used. The precise search query is illustrated in supplementary material 1. Only manuscripts using a similar methodology were included. Furthermore, temporal muscle measurements should have been performed on preoperative MR-imaging in a glioblastoma only patient cohort. Results Epidemiology In total, 137 patients were included for statistical analysis (Table 1 ). Mean age at diagnosis was 61,9 years; 36,5% were female. Median overall survival time for the study population was 12 months. Diagnosis was pathologically confirmed after surgery in all cases; 38,7% underwent surgical biopsy without resection. On postoperative imaging, mean residual tumor volume (RTV) was 19,4 ml in the biopsied patients and 1,7 ml after surgical resection. Molecular review confirmed IDH-wildtype status in all patients and MGMT promoter hypermethylation status in 34,3% of cases. After surgery, 94,9% of patients completed their concomitant radiotherapy. The mean number of cycles of adjuvant temozolomide was 4. Table 1 epidemiology of study cohort Variables Study cohort (n = 137) Sex female 50 (36,5%) male 87 (63,5%) Age-at-diagnosis (years) mean 61,9 Karnofsky Performance Scale < 70 24 (17,5%) ≥ 70 113 (82,5%) MGMT promoter methylation status unmethylated 90 (65,7%) methylated 47 (34,3%) Surgery Biopsy only 53 (38,7%) Resection 84 (61,3%) RTV (ml) 8,6 Completion of radiotherapy 60 Gy 117/124 (94,4%) 45 Gy 13/13 (100%) Number of TMZ cycles mean 4 Overall survival time (months) median 12 mean 20,2 Temporal muscle measurements TMT (mm) 7,6 TMV (cm 3 ) 21,5 Correlation of temporal muscle thickness and volume The mean thickness of the temporal muscle in the study cohort was 7,6 mm; the mean volume 21,5 cm 3 . Using sex-specific cut-off values for temporalis muscle thickness, 11 female patients (22,9%) and 16 male patients (19%) were identified “at risk for sarcopenia”. There was no correlation between age at diagnosis and temporal muscle thickness (r = -0,023), nor muscle volume (r = 0,04). On the other hand, there seemed moderate to strong positive correlation between muscle thickness and volume (r = 0,6; P < 0,001; Fig. 1 ). Correlation of temporal muscle measurements and clinical outcome Survival analysis was performed using a multivariate Cox regression analysis using all known prognostic risk factors in glioblastoma as covariates (Table 2 ). Absolute temporal muscle thickness was not associated with inferior outcome after correction for age at diagnosis, clinical status of the patient (KPS), MGMT promoter methylation status, residual contrast-enhancing tumor volume on postoperative imaging (in ml), completion of concomitant radiotherapy (45 Gy or 60 Gy) and number of adjuvant cycles of Temozolomide (HR 0,982; P = 0,687). The same was true for temporal muscle volume after correction for the same covariates (HR 1,024; P = 0,054). Finally, the risk for sarcopenia was not associated with inferior outcome as determined by a multivariate Cox regression analysis (HR 0,873; P = 0,582) (see Fig. 2 ). Table 2 multivariate Cox regression analysis for TMT parameter HR p age at diagnosis (year) 1,02 0,012 Karnofsky Performance Scale (< 70) 2,17 0,006 MGMT promoter methylation status (no) 2,26 < 0,001 Residual Tumor Volume (ml) 1,01 0,063 Completion of adjuvant radiotherapy (no) 4,70 < 0,001 Number of adjuvant cycles of Temozolomide 0,83 < 0,001 Temporal muscle thickness (mm) 0,98 0,687 Discussion Temporal muscle measurements and outcome in newly diagnosed glioblastoma patients An overview of the relevant literature is presented in Table 3 . The papers are listed in chronologically and their statistical methodology is presented. The inclusion of known prognostic factors as covariates in the multivariate Cox regression model is reviewed for each paper: age at diagnosis, functional status at baseline (KPS or ECOG), molecular parameters of the tumor (IDH-mutation status and MGMT promoter methylation status), extent of resection or residual tumor volume and administration of adjuvant radio- and chemotherapy. The larger and/or statistically more robust studies are discussed below. Table 3 Overview of the literature concerning temporal muscle measurements as predictor for clinical outcome in glioblastoma patients. study date of publication n predictor age KPS/ECOG molecular parameters EOR/RTV adjuvant treatment measurement Furtner et al. (progressive disease) 2019 308 + no no no no Yes* TMT Cinkir et al. 2019 47 - yes no no no no TMT An et al. 2020 177 + yes yes no yes no TMT Liu et al. 2020 130 + yes no no no yes TMT Furtner et al. 2021 755 + yes yes* no no no TMT Huq et al. (newly diagnosed) 2021 - yes yes yes yes yes TMT Huq et al. (progressive disease) 2021 - yes yes yes yes yes TMT Muglia et al. 2021 51 - yes yes yes* yes no TMT Wende et al. 2021 335 - yes yes yes yes yes TMT Broen et al. 2022 328 + no yes yes no no TMT Mi et al. 2022 96 + yes no no no no CSA Pasqualetti et al. 2022 52 + yes* no no yes yes TMT Sütcüoglu et al. 2023 74 - yes yes no no no TMT/CSA Tang et al. 2024 102 + yes yes no no yes TMT this study 2025 - yes yes yes yes yes TMT/TMV KPS – Karnofsky Performance Scale; ECOG – Eastern Cognitive Oncology Group Performance Scale; EOR – extent-of-resection; RT V – residual tumor volume; TMT – temporal muscle thickness; CSA – cross sectional area; TMV – temporal muscle volume. Furtner et al. published a prospectively monitored cohort study analyzing the value of TMT in newly diagnosed molecularly-undefined glioblastoma patients.[ 6 ] In this first study on the topic, they used data from the CENTRIC EORTC and CORE trials in which the added value of Cilengitide to standard-of-care in MGMT promotor methylated (CENTRIC) and unmethylated patients (CORE) was examined. In both trials, only patients with ECOG 0/1 were eligible for inclusion. About half of the patients in both trials underwent gross total resection of their tumor. IDH-mutation analysis was not available. The authors used sex-specific TMT values to dichotomize their cohorts as proposed by Steindl et al.[ 17 ] In the subgroup with MGMT promotor methylated tumors, the authors found an inferior outcome in patients “at risk for sarcopenia” (TMT below sex-specific cut-off) when corrected for age at diagnosis, cognitive performance and EOR. Interestingly, no IDH-mutation analysis was performed within the CENTRIC-trial.[ 19 ] Given the somewhat younger mean age of the patient cohort (58 years), median overall survival time of 26,3 months and exclusive inclusion of patients with MGMT promoter methylated tumors, it should be assumed a significant number of grade 4 IDH-mutant astrocytoma patients were included in this trial. Finally, around 25% of patients in the CENTRIC trial did not complete their course of radiation therapy and more than 10% did not complete full adjuvant treatment with temozolomide due to early progression or treatment toxicity.[ 19 ] Adjuvant treatment and IDH-mutation status were, however, not included in the multivariate Cox regression model or at least not significant.[ 6 ] In the MGMT promoter unmethylated group, the authors found an inferior outcome in patients “at risk for sarcopenia” when corrected for age at diagnosis, steroid use at baseline, ECOG-status and RPA class.[ 6 ] Broen and colleagues published a large multicentric retrospective cohort study analyzing 328 IDH-wildtype glioblastoma patients.[ 1 ] They used similar sex-specific cut-off values for TMT to identify patients “at risk for sarcopenia”.[ 17 ] A significant inferior survival in the patient cohort with lower TMT values was found using a multivariate Cox regression model including adjuvant treatment, ECOG at baseline, surgery and MGMT promoter methylation.[ 1 ] Nevertheless some methodological remarks should be made. First of all, the mean age of the at-risk group was significantly higher at baseline (67,1 vs 62,3 years); age at diagnosis was nevertheless not included in the final Cox regression model. Furthermore, surgery was dichotomized into biopsy versus resection and nor extent of resection, nor residual tumor volume were evaluated. Finally, the completion of adjuvant treatment was not incorporated in the model although patients with lower TMT-values showed increased risk of early discontinuation of the adjuvant treatment.[ 1 ] Huq et al. performed a retrospective cohort study including 378 newly-diagnosed grade 4 glioma patients, including 42 cases of IDH-mutant grade 4 astrocytoma.[ 9 ] Using a TMT cut-off value of 7,1mm, they identified 9% of cases as patients with low TMT at baseline. Using a well-built multivariate Cox regression analysis, including age at diagnosis, KPS, adjuvant treatment, extent of resection and molecular characteristics, the authors could not identify TMT as negative prognostic parameter for outcome. The authors identified their optimal TMT cut-off value using maximally selected rank statistics.[ 9 ] Finally, Wende et al. published a large retrospective cohort study with 335 IDH-wildtype glioblastoma patients.[ 21 ] They could not show a negative prognostic significance of TMT thickness using a well-built multivariate Cox regression analysis including age at diagnosis, KPS, MGMT promoter methylation, extent of resection and adjuvant treatment. The authors did not use cut off points for TMT to identify patients at risk for sarcopenia but used TMT as a continuous variable instead.[ 21 ] Overall, the presented papers use a very similar methodology to measure TMT on preoperative MR-imaging, as was first proposed by Furtner and colleagues in 2017.[ 4 ] Nevertheless, these values were applied differently in the subsequent statistical analysis. TMT values were used as a continuous variable or dichotomized using different cut-off points. These cut-off points were sex-specific as determined by Steindl et al. or based on the Younden index or log rank statistics. Furthermore, multivariate Cox regression analysis was used in all aforementioned papers to examine the prognostic significance of TMT. The covariates included in these analyses, or at least mentioned in the individual papers, are unfortunately very heterogeneous. It therefore seems rather difficult to directly compare the different papers or to process them in a meta-analysis. Nonetheless, the papers with a more robust methodology and thoroughly built multivariate Cox regression analysis tend to show no clear correlation of TMT and clinical outcome in newly diagnosed IDH-wildtype glioblastoma patients. The results of the current retrospective cohort study are in line with these findings. In 2022, a systematic review and meta-analysis on TMT as predictor for outcome in glioblastoma was published by Sadhwani et al.[ 16 ] They concluded TMT is associated with shorter overall and progression free survival in glioblastoma patients based on a pooled hazard ratio. These findings are nevertheless the result of inclusion of very diverse and difficult to compare retrospective cohort studies, as mentioned earlier. These studies do not only use inconsistent covariates in their Cox regression analysis, they include different patient cohorts (including grade 3 astrocytomas) and use varying methods to measure TMT as well. Due to this heterogeneity, the results of the meta-analysis seem therefore flawed and difficult to generalize. Temporal muscle measurements and outcome in progressive glioblastoma patients Some years before Furtner et al. published their results on the value of TMT in newly diagnosed glioblastoma patients, the authors analyzed a patient cohort with progressive glioblastoma first.[ 5 ] For this, they used the prospectively gathered clinical data of the EORTC 26101 trial.[ 22 ] In this phase 3 trial, patients with progressive but molecularly-undefined glioblastoma were randomized to receive second-line chemotherapy (Lomustine/CCNU) with or without anti-VEGF targeted therapy (Bevacizumab). Overall, 23% of included patients showed MGMT promoter methylation, 28,6% were unmethylated and 47,6% had missing data. After further progression, more than half of the included patients received further treatment with various combinations of repeat surgery, repeat radiotherapy and/or rechallenge chemotherapy.[ 22 ] The authors used 308 patients of the phase III trial to investigate TMT as prognostic parameter for outcome. They determined 7,2mm of thickness using the Younden index as an optimal cut-off value to dichotomize their patient cohort in a proof-of-concept test cohort.[ 5 ] Using this value in a multivariate Cox regression model with steroid use, MGMT promotor methylation status, tumor diameter and localization, the authors found a significant inferior outcome in patients with TMT below 7,2mm.[ 5 ] These findings are in line with a high-quality retrospective cohort study performed by Huq et al.[ 9 ] They analyzed 149 patients with progressive glioblastoma. In a multivariable Cox regression analysis they found a TMT value below 7,1mm predictive for inferior outcome.[ 9 ] The literature concerning progressive glioblastoma is limited to these two reports. Both draw the same conclusion from their respective patient database which is probably even more heterogeneous compared to studies concerning newly diagnosed patients. Huq et al. however found, interestingly, no prognostic value of TMT in their cohort with only newly diagnosed patients. The validity of these results is difficult to appraise at this moment due to the limited number of studies and strong heterogeneity of the patient population after disease progression. However, TMT values seem to decline during treatment in a subgroup of patients (60% in the study cohort of newly diagnosed glioblastoma patients of Furtner et al.).[ 6 ] It remains uncertain if this subgroup might experience systemic consequences of temozolomide treatment, rendering these patients less resilient to second-line treatment. Further analysis could shed more light on this topic. Limitations and strengths The retrospective design of this cohort implies an increased risk for bias in the clinical information gathering. For example, a significant number of patients had to be excluded due to incompleteness of the medical files with missing radiographical or molecular data. Due to this smaller patient cohort, statistical power might be lacking to identify a statistically significant but small prognostic role of temporal muscle measurements in IDH-wildtype glioblastoma patients. On the other hand, this is the first study to use temporal muscle volume as a covariate. Although this parameter might seem more representative for the real “muscle health” in patients, no clear prognostic role could be identified. Conclusion This retrospective cohort study could not find any prognostic value of temporal muscle measurements in newly diagnosed IDH-wildtype glioblastoma patients. These findings are in line with some other well executed cohort studies. The literature concerning the prognostic value of temporal muscle measurements uses heterogenous statistical methods to analyze TMT as predictor for outcome making firm conclusions unconvincing. Overall, the evidence in favor for TMT as an individual prognostic factor in glioblastoma seems rather controversial at best. Declarations Funding There was no funding obtained for this research. Author Contribution HP: study design, statistics, manuscript writing.CDR, LDB and AVS: data collection, manuscript review.TB and GH: study design, manuscript review. Acknowledgements None. Conflict of interests None. References Broen MPG, Beckers R, Willemsen ACH, Huijs SMH, Pasmans R, Eekers DBP et al (2022) Temporal muscle thickness as an independent prognostic imaging marker in newly diagnosed glioblastoma patients: A validation study. Neuro-oncology Adv 4(1):vdac038. 10.1093/noajnl/vdac038 Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T et al (2019) Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(4):601. 10.1093/ageing/afz046 Davis MP, Panikkar R (2019) Sarcopenia associated with chemotherapy and targeted agents for cancer therapy. Annals Palliat Med 8(1):86–101. 10.21037/apm.2018.08.02 Furtner J, Berghoff AS, Albtoush OM, Woitek R, Asenbaum U, Prayer D et al (2017) Survival prediction using temporal muscle thickness measurements on cranial magnetic resonance images in patients with newly diagnosed brain metastases. Eur Radiol 27(8):3167–3173. 10.1007/s00330-016-4707-6 Furtner J, Genbrugge E, Gorlia T, Bendszus M, Nowosielski M, Golfinopoulos V et al (2019) Temporal muscle thickness is an independent prognostic marker in patients with progressive glioblastoma: translational imaging analysis of the EORTC 26101 trial. Neurooncology 21(12):1587–1594. 10.1093/neuonc/noz131 Furtner J, Weller M, Weber M, Gorlia T, Nabors B, Reardon DA et al (2022) Temporal Muscle Thickness as a Prognostic Marker in Patients with Newly Diagnosed Glioblastoma: Translational Imaging Analysis of the CENTRIC EORTC 26071 – 22072 and CORE Trials. Clin cancer research: official J Am Association Cancer Res 28(1):129–136. 10.1158/1078-0432.CCR-21-1987 Grabowski MM, Recinos PF, Nowacki AS, Schroeder JL, Angelov L, Barnett GH et al (2014) Residual tumor volume versus extent of resection: predictors of survival after surgery for glioblastoma. J Neurosurg 121(5):1115–1123. 10.3171/2014.7.JNS132449 Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352(10):997–1003. 10.1056/NEJMoa043331 Huq S, Khalafallah AM, Ruiz-Cardozo MA, Botros D, Oliveira LAP, Dux H et al (2021) A novel radiographic marker of sarcopenia with prognostic value in glioblastoma. Clin Neurol Neurosurg 207:106782. 10.1016/j.clineuro.2021.106782 Karschnia P, Vogelbaum MA, van den Bent M, Cahill DP, Bello L, Narita Y et al (2021) Evidence-based recommendations on categories for extent of resection in diffuse glioma. Eur J cancer (Oxford England: 1990) 149:23–33. 10.1016/j.ejca.2021.03.002 Lamborn KR, Chang SM, Prados MD (2004) Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neurooncology 6(3):227–235. 10.1215/S1152851703000620 Leitner J, Pelster S, Schopf V, Berghoff AS, Woitek R, Asenbaum U et al (2018) High correlation of temporal muscle thickness with lumbar skeletal muscle cross-sectional area in patients with brain metastases. PLoS ONE 13(11):e0207849. 10.1371/journal.pone.0207849 Malmstrom A, Gronberg BH, Marosi C, Stupp R, Frappaz D, Schultz H et al (2012) Temozolomide versus standard 6-week radiotherapy versus hypofractionated radiotherapy in patients older than 60 years with glioblastoma: the Nordic randomised, phase 3 trial. Lancet Oncol 13(9):916–926. 10.1016/S1470-2045(12)70265-6 Perry JR, Laperriere N, O'Callaghan CJ, Brandes AA, Menten J, Phillips C et al (2017) Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma. N Engl J Med 376(11):1027–1037. 10.1056/NEJMoa1611977 Pinson H, Silversmit G, Vanhauwaert D, Vanschoenbeek K, Okito JK, De Vleeschouwer S et al (2024) Epidemiology and survival of adult-type diffuse glioma in Belgium during the molecular era. Neurooncology 26(1):191–202. 10.1093/neuonc/noad158 Sadhwani N, Aggarwal A, Mishra A, Garg K (2022) Temporal muscle thickness as an independent prognostic marker in glioblastoma patients-a systematic review and meta-analysis. Neurosurg Rev 45(6):3619–3628. 10.1007/s10143-022-01892-3 Steindl A, Leitner J, Schwarz M, Nenning KH, Asenbaum U, Mayer S et al (2020) Sarcopenia in Neurological Patients: Standard Values for Temporal Muscle Thickness and Muscle Strength Evaluation. J Clin Med 9(5). 10.3390/jcm9051272 Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996. 10.1056/NEJMoa043330 Stupp R, Hegi ME, Gorlia T, Erridge SC, Perry J, Hong YK et al (2014) Cilengitide combined with standard treatment for patients with newly diagnosed glioblastoma with methylated MGMT promoter (CENTRIC EORTC 26071 – 22072 study): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol 15(10):1100–1108. 10.1016/S1470-2045(14)70379-1 Ten Cate C, Huijs SMH, Willemsen ACH, Pasmans R, Eekers DBP, Zegers CML et al (2022) Correlation of reduced temporal muscle thickness and systemic muscle loss in newly diagnosed glioblastoma patients. J Neurooncol 160(3):611–618. 10.1007/s11060-022-04180-8 Wende T, Kasper J, Prasse G, Glass A, Kriesen T, Freiman TM et al (2021) Newly Diagnosed IDH-Wildtype Glioblastoma and Temporal Muscle Thickness: A Multicenter Analysis. Cancers 13(22). 10.3390/cancers13225610 Wick W, Gorlia T, Bendszus M, Taphoorn M, Sahm F, Harting I et al (2017) Lomustine and Bevacizumab in Progressive Glioblastoma. N Engl J Med 377(20):1954–1963. 10.1056/NEJMoa1707358 Additional Declarations No competing interests reported. Supplementary Files TMTsupplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5767366","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399431000,"identity":"36cd3038-1d45-49c2-8cf7-5c4adbb66826","order_by":0,"name":"Harry Pinson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACgwMQmgdMfoCKMhOthXEGVANRWiBqeYjScvzss888DHdkzPkPH3ts21Zn1y/df4C5oAK3Fvsz6cazeRie8VjOSEs3zm1jS5455zAD84wz+ByWxgx0z2Eegxs8ZtK5bTzJBjeSGZh52/BoOf8MquX8GTNpyzaJZHuwln94tNyA2XIgx0yasc3AzkACpKUBn5ZnzIxzDJ4BHZaWJtlzLiFB4kayweEZx/A5LI2Z4U3FHXuD84ePSfwoq7Pnn5H48HFBDW4tIMDEgxQ7iSAnHcChEg4YfyCpsSekehSMglEwCkYeAACfhUw7wpJAcgAAAABJRU5ErkJggg==","orcid":"","institution":"Ghent University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Harry","middleName":"","lastName":"Pinson","suffix":""},{"id":399431002,"identity":"24829d47-bfd9-4d28-9f49-d3903c0601f9","order_by":1,"name":"Cara De Rudder","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Cara","middleName":"","lastName":"De Rudder","suffix":""},{"id":399431004,"identity":"79df6ffc-9ef2-468b-9d50-81a3ef2be0d1","order_by":2,"name":"Louis De Backer","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Louis","middleName":"","lastName":"De Backer","suffix":""},{"id":399431005,"identity":"acfe78f8-a69c-4dda-bf38-a3cb964a55e9","order_by":3,"name":"Amber Van Sinay","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Amber","middleName":"Van","lastName":"Sinay","suffix":""},{"id":399431008,"identity":"d2d36b86-62f3-428c-931c-4d98a1d6abe1","order_by":4,"name":"Tom Boterberg","email":"","orcid":"","institution":"Ghent University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Boterberg","suffix":""},{"id":399431009,"identity":"329f0c55-f42a-4887-b520-f131a75614c3","order_by":5,"name":"Giorgio Hallaert","email":"","orcid":"","institution":"AZ Maria Middelares","correspondingAuthor":false,"prefix":"","firstName":"Giorgio","middleName":"","lastName":"Hallaert","suffix":""}],"badges":[],"createdAt":"2025-01-05 10:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5767366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5767366/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73674091,"identity":"e99dbad9-1327-44ed-a4da-34894c2fa83c","added_by":"auto","created_at":"2025-01-13 12:54:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79679,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of temporal muscle measurements according to sex.\u003c/p\u003e","description":"","filename":"TMTfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5767366/v1/f975621b7396e19ab850c273.jpg"},{"id":73674085,"identity":"6882538b-29c7-42f2-8591-e98a8bc63132","added_by":"auto","created_at":"2025-01-13 12:54:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61472,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve for patients “at risk for sarcopenia”.\u003c/p\u003e","description":"","filename":"TMTfigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5767366/v1/d62a1468f1f41f934380a59e.jpg"},{"id":77544145,"identity":"3f767872-a595-435c-abcd-75295c77f4fb","added_by":"auto","created_at":"2025-03-03 02:31:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":988072,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5767366/v1/75a14007-2851-4e87-b433-7028abc71716.pdf"},{"id":73674086,"identity":"793e3977-6f0a-4a81-9e4d-0fe63b87bd9f","added_by":"auto","created_at":"2025-01-13 12:54:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14997,"visible":true,"origin":"","legend":"","description":"","filename":"TMTsupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5767366/v1/cea881f751540a018b937aaf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal muscle measurements as predictor for outcome in a cohort of IDH-wildtype glioblastoma patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIDH-wildtype glioblastoma is the most frequently occurring malignant intrinsic brain tumor in adults affecting 4\u0026ndash;6 patients per 100.000 person-years.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Its prognosis is dismal with a median overall survival time of 14,6 months after optimal treatment.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] In real world registries however, survival of glioblastoma patients outside trial protocol seems far inferior with an estimated median overall survival time of only 9,3 months.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Current standard-of-care treatment for glioblastoma includes maximal safe surgical resection followed by radiotherapy with concomitant and adjuvant temozolomide.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDifferent prognostic factors for outcome in glioblastoma patients have been identified. Age and clinical performance scale at diagnosis are important patient related predictors.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Surgically, the aim is to perform a maximal safe resection of the tumor leaving as little contrast enhancing tumor as possible. Extent-of-resection (EOR) is a well-known predictor for outcome where a gross total or even supratotal resection of the tumor implies better survival.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Grabowski et al. furthermore demonstrated residual tumor volume (RTV) to be a superior predictor for outcome compared to EOR.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Finally, molecular characteristics of the tumor have prognostic relevance. Epigenetic methylation of the MGMT (O6-methylguanine-DNA methyltransferase) gene promoter is associated with improved efficacy of alkylating chemotherapy in high-grade glioma and therefore results in superior outcome.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSarcopenia is defined as a progressive and generalized skeletal muscle disorder associated with adverse outcome in the general population.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] The disorder is primarily characterized by low muscle strength, quantity or quality. In cancer patients, sarcopenia at diagnosis has been associated with adverse clinical outcome due to increased chemotherapy toxicity and reduced therapy response, ultimately leading to inferior survival.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Various biomarkers for clinical and scientific use have been proposed.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Grip strength is an easy to use and strong predictor for sarcopenia.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] It is however difficult to assess in retrospective cohort studies when not routinely performed and registered. Muscle quantity can be assessed alternatively by X-ray absorptiometry or using more routinely performed imaging modalities such as CT- or MR-imaging.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Many modalities have been tested for their reliability and accuracy.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTemporalis muscle thickness (TMT) for example correlates well with the cross-sectional area of lumbar vertebral muscles.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] TMT might therefore be used as a surrogate biomarker for skeletal muscle mass in glioblastoma patients to identify frail patients \u0026ldquo;at risk for sarcopenia\u0026rdquo;.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Furtner et al. were the first to show reduced TMT as an independent negative prognostic parameter in patients with recurrent glioblastoma.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] More recently, a meta-analysis of various cohort studies seemed to confirm TMT as a predictive parameter for clinical outcome in glioblastoma patients.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Nevertheless, various other studies could not find a negative prognostic value of TMT measurements.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The value of TMT as a clinically relevant prognostic factor therefore remains uncertain.\u003c/p\u003e \u003cp\u003eThis study aims to advance the literature concerning the prognostic value of temporal muscle measurements in glioblastoma patients. It focusses not only on temporal muscle thickness but explores if temporal muscle volume (TMV) correlates better with outcome in molecularly defined glioblastoma patients. Finally, these results are corelated with a critical review of the literature.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection and characteristics\u003c/h2\u003e \u003cp\u003eA retrospective patient cohort study was performed. Patients newly diagnosed with an IDH-wildtype glioblastoma between 2005 and 2023 were included. All cases were histologically confirmed after obtaining surgical specimen. Patients were treated afterwards in accordance with the Stupp protocol.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Hypofractionated adjuvant radiotherapy, as proposed for older and/or frail patients, was allowed for inclusion.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Relevant clinical parameters were retrieved from the patient files: age at diagnosis, functional status at baseline (Karnofsky Performance Scale), molecular parameters of the tumor (IDH-mutation and MGMT promoter methylation status), residual tumor volume (RTV), completion of concomitant radiotherapy (45 Gy or 60 Gy) and number of adjuvant temozolomide cycles. Patients were excluded if: (1) not all clinical or radiological parameters were available for review; (2) they received experimental treatment within a clinical trial.\u003c/p\u003e \u003cp\u003e This study was approved by the ethics committee of Ghent University Hospital (reference: THE-2022-0069). The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTumor and temporal muscle measurements\u003c/h3\u003e\n\u003cp\u003eResidual tumor volume (RTV) was determined on postoperative Gd-enhanced T1 magnetization-prepared rapid acquisition gradient echo (MPRAGE) images obtained within 72h after surgery using semi-automated segmentation software available on the Medtronic Stealthstation S7 (Medtronic, Louisville, CO, US).\u003c/p\u003e \u003cp\u003eTemporal muscle thickness (TMT) and volume (TMV) were measured on preoperative T1-MPRAGE images using the same software. In short, muscle thickness was measured perpendicular to the skull on an axial slice parallel to the AC-PC plane. The orbital roof and Sylvian fissure were used as landmarks.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Muscle thickness was measured bilaterally in each patient and the mean value was used for statistical analysis. If the patient was operated on one side before, this side was not measured to reduce the impact of postsurgical muscle atrophy. TMV was measured on the side with the thickest muscle.\u003c/p\u003e \u003cp\u003eTo identify patients \u0026ldquo;at risk for sarcopenia\u0026rdquo; sex-specific cut-off values for TMT were used. These values were introduced before by Steindl et al. who examined TMT values in a healthy Caucasian population.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Cut-off points to identify patients \u0026ldquo;at risk for sarcopenia\u0026rdquo; were determined 6,3 mm for male and 5,2 mm for female persons.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Pearson correlation coefficient was used to determine linear correlation between the various continuous variables. Multivariate Cox regression analysis was performed to investigate the prognostic relevance of the different temporal muscle measurements. Means were statistically compared using the independent-samples student t-test. For all statistical test, a two-sided p-value of 0,05 was used to determine significance. Statistical analysis was performed using the SPSS software v29 (IBM, New York, US).\u003c/p\u003e \u003cp\u003eLiterature review\u003c/p\u003e \u003cp\u003eIn order to compare the results of this study with the current literature a review of the literature was performed. The Medline database was searched using the following Mesh terms: glioblastoma, temporal muscle, sarcopenia, survival rate, prognosis, survival analysis and treatment outcome were used. The precise search query is illustrated in supplementary material 1. Only manuscripts using a similar methodology were included. Furthermore, temporal muscle measurements should have been performed on preoperative MR-imaging in a glioblastoma only patient cohort.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEpidemiology\u003c/h2\u003e \u003cp\u003eIn total, 137 patients were included for statistical analysis (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mean age at diagnosis was 61,9 years; 36,5% were female. Median overall survival time for the study population was 12 months. Diagnosis was pathologically confirmed after surgery in all cases; 38,7% underwent surgical biopsy without resection. On postoperative imaging, mean residual tumor volume (RTV) was 19,4 ml in the biopsied patients and 1,7 ml after surgical resection. Molecular review confirmed IDH-wildtype status in all patients and MGMT promoter hypermethylation status in 34,3% of cases. After surgery, 94,9% of patients completed their concomitant radiotherapy. The mean number of cycles of adjuvant temozolomide was 4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eepidemiology of study cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy cohort (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (36,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (63,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-at-diagnosis (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKarnofsky Performance Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (17,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge; 70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (82,5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMGMT promoter methylation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunmethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (65,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (34,3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiopsy only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (38,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (61,3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRTV (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompletion of radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 Gy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117/124 (94,4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 Gy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13/13 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of TMZ cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall survival time (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemporal muscle measurements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMV (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of temporal muscle thickness and volume\u003c/h2\u003e \u003cp\u003eThe mean thickness of the temporal muscle in the study cohort was 7,6 mm; the mean volume 21,5 cm\u003csup\u003e3\u003c/sup\u003e. Using sex-specific cut-off values for temporalis muscle thickness, 11 female patients (22,9%) and 16 male patients (19%) were identified \u0026ldquo;at risk for sarcopenia\u0026rdquo;. There was no correlation between age at diagnosis and temporal muscle thickness (r = -0,023), nor muscle volume (r\u0026thinsp;=\u0026thinsp;0,04). On the other hand, there seemed moderate to strong positive correlation between muscle thickness and volume (r\u0026thinsp;=\u0026thinsp;0,6; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation of temporal muscle measurements and clinical outcome\u003c/h3\u003e\n\u003cp\u003eSurvival analysis was performed using a multivariate Cox regression analysis using all known prognostic risk factors in glioblastoma as covariates (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Absolute temporal muscle thickness was not associated with inferior outcome after correction for age at diagnosis, clinical status of the patient (KPS), MGMT promoter methylation status, residual contrast-enhancing tumor volume on postoperative imaging (in ml), completion of concomitant radiotherapy (45 Gy or 60 Gy) and number of adjuvant cycles of Temozolomide (HR 0,982; P\u0026thinsp;=\u0026thinsp;0,687). The same was true for temporal muscle volume after correction for the same covariates (HR 1,024; P\u0026thinsp;=\u0026thinsp;0,054). Finally, the risk for sarcopenia was not associated with inferior outcome as determined by a multivariate Cox regression analysis (HR 0,873; P\u0026thinsp;=\u0026thinsp;0,582) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emultivariate Cox regression analysis for TMT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eparameter\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage at diagnosis (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarnofsky Performance Scale (\u0026lt;\u0026thinsp;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGMT promoter methylation status (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Tumor Volume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletion of adjuvant radiotherapy (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of adjuvant cycles of Temozolomide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal muscle thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTemporal muscle measurements and outcome in newly diagnosed glioblastoma patients\u003c/h2\u003e \u003cp\u003eAn overview of the relevant literature is presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The papers are listed in chronologically and their statistical methodology is presented. The inclusion of known prognostic factors as covariates in the multivariate Cox regression model is reviewed for each paper: age at diagnosis, functional status at baseline (KPS or ECOG), molecular parameters of the tumor (IDH-mutation status and MGMT promoter methylation status), extent of resection or residual tumor volume and administration of adjuvant radio- and chemotherapy. The larger and/or statistically more robust studies are discussed below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the literature concerning temporal muscle measurements as predictor for clinical outcome in glioblastoma patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003estudy\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003edate of publication\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003epredictor\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eage\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eKPS/ECOG\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003emolecular parameters\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eEOR/RTV\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eadjuvant\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003etreatment\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003emeasurement\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFurtner et al.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(progressive disease)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCinkir et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFurtner et al.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHuq et al.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(newly diagnosed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHuq et al.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(progressive disease)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuglia et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWende et al.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBroen et al.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasqualetti et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026uuml;tc\u0026uuml;oglu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT/CSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ethis study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTMT/TMV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eKPS \u0026ndash; Karnofsky Performance Scale; ECOG \u0026ndash; Eastern Cognitive Oncology Group Performance Scale; EOR \u0026ndash; extent-of-resection; RT V \u0026ndash; residual tumor volume; TMT \u0026ndash; temporal muscle thickness; CSA \u0026ndash; cross sectional area; TMV \u0026ndash; temporal muscle volume.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurtner et al. published a prospectively monitored cohort study analyzing the value of TMT in newly diagnosed molecularly-undefined glioblastoma patients.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] In this first study on the topic, they used data from the CENTRIC EORTC and CORE trials in which the added value of Cilengitide to standard-of-care in MGMT promotor methylated (CENTRIC) and unmethylated patients (CORE) was examined. In both trials, only patients with ECOG 0/1 were eligible for inclusion. About half of the patients in both trials underwent gross total resection of their tumor. IDH-mutation analysis was not available. The authors used sex-specific TMT values to dichotomize their cohorts as proposed by Steindl et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn the subgroup with MGMT promotor methylated tumors, the authors found an inferior outcome in patients \u0026ldquo;at risk for sarcopenia\u0026rdquo; (TMT below sex-specific cut-off) when corrected for age at diagnosis, cognitive performance and EOR. Interestingly, no IDH-mutation analysis was performed within the CENTRIC-trial.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Given the somewhat younger mean age of the patient cohort (58 years), median overall survival time of 26,3 months and exclusive inclusion of patients with MGMT promoter methylated tumors, it should be assumed a significant number of grade 4 IDH-mutant astrocytoma patients were included in this trial. Finally, around 25% of patients in the CENTRIC trial did not complete their course of radiation therapy and more than 10% did not complete full adjuvant treatment with temozolomide due to early progression or treatment toxicity.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Adjuvant treatment and IDH-mutation status were, however, not included in the multivariate Cox regression model or at least not significant.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] In the MGMT promoter unmethylated group, the authors found an inferior outcome in patients \u0026ldquo;at risk for sarcopenia\u0026rdquo; when corrected for age at diagnosis, steroid use at baseline, ECOG-status and RPA class.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBroen and colleagues published a large multicentric retrospective cohort study analyzing 328 IDH-wildtype glioblastoma patients.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] They used similar sex-specific cut-off values for TMT to identify patients \u0026ldquo;at risk for sarcopenia\u0026rdquo;.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] A significant inferior survival in the patient cohort with lower TMT values was found using a multivariate Cox regression model including adjuvant treatment, ECOG at baseline, surgery and MGMT promoter methylation.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Nevertheless some methodological remarks should be made. First of all, the mean age of the at-risk group was significantly higher at baseline (67,1 vs 62,3 years); age at diagnosis was nevertheless not included in the final Cox regression model. Furthermore, surgery was dichotomized into biopsy versus resection and nor extent of resection, nor residual tumor volume were evaluated. Finally, the completion of adjuvant treatment was not incorporated in the model although patients with lower TMT-values showed increased risk of early discontinuation of the adjuvant treatment.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHuq et al. performed a retrospective cohort study including 378 newly-diagnosed grade 4 glioma patients, including 42 cases of IDH-mutant grade 4 astrocytoma.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Using a TMT cut-off value of 7,1mm, they identified 9% of cases as patients with low TMT at baseline. Using a well-built multivariate Cox regression analysis, including age at diagnosis, KPS, adjuvant treatment, extent of resection and molecular characteristics, the authors could not identify TMT as negative prognostic parameter for outcome. The authors identified their optimal TMT cut-off value using maximally selected rank statistics.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFinally, Wende et al. published a large retrospective cohort study with 335 IDH-wildtype glioblastoma patients.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] They could not show a negative prognostic significance of TMT thickness using a well-built multivariate Cox regression analysis including age at diagnosis, KPS, MGMT promoter methylation, extent of resection and adjuvant treatment. The authors did not use cut off points for TMT to identify patients at risk for sarcopenia but used TMT as a continuous variable instead.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOverall, the presented papers use a very similar methodology to measure TMT on preoperative MR-imaging, as was first proposed by Furtner and colleagues in 2017.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Nevertheless, these values were applied differently in the subsequent statistical analysis. TMT values were used as a continuous variable or dichotomized using different cut-off points. These cut-off points were sex-specific as determined by Steindl et al. or based on the Younden index or log rank statistics. Furthermore, multivariate Cox regression analysis was used in all aforementioned papers to examine the prognostic significance of TMT. The covariates included in these analyses, or at least mentioned in the individual papers, are unfortunately very heterogeneous. It therefore seems rather difficult to directly compare the different papers or to process them in a meta-analysis. Nonetheless, the papers with a more robust methodology and thoroughly built multivariate Cox regression analysis tend to show no clear correlation of TMT and clinical outcome in newly diagnosed IDH-wildtype glioblastoma patients. The results of the current retrospective cohort study are in line with these findings.\u003c/p\u003e \u003cp\u003e In 2022, a systematic review and meta-analysis on TMT as predictor for outcome in glioblastoma was published by Sadhwani et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] They concluded TMT is associated with shorter overall and progression free survival in glioblastoma patients based on a pooled hazard ratio. These findings are nevertheless the result of inclusion of very diverse and difficult to compare retrospective cohort studies, as mentioned earlier. These studies do not only use inconsistent covariates in their Cox regression analysis, they include different patient cohorts (including grade 3 astrocytomas) and use varying methods to measure TMT as well. Due to this heterogeneity, the results of the meta-analysis seem therefore flawed and difficult to generalize.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTemporal muscle measurements and outcome in progressive glioblastoma patients\u003c/h2\u003e \u003cp\u003eSome years before Furtner et al. published their results on the value of TMT in newly diagnosed glioblastoma patients, the authors analyzed a patient cohort with progressive glioblastoma first.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] For this, they used the prospectively gathered clinical data of the EORTC 26101 trial.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] In this phase 3 trial, patients with progressive but molecularly-undefined glioblastoma were randomized to receive second-line chemotherapy (Lomustine/CCNU) with or without anti-VEGF targeted therapy (Bevacizumab). Overall, 23% of included patients showed MGMT promoter methylation, 28,6% were unmethylated and 47,6% had missing data. After further progression, more than half of the included patients received further treatment with various combinations of repeat surgery, repeat radiotherapy and/or rechallenge chemotherapy.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe authors used 308 patients of the phase III trial to investigate TMT as prognostic parameter for outcome. They determined 7,2mm of thickness using the Younden index as an optimal cut-off value to dichotomize their patient cohort in a proof-of-concept test cohort.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Using this value in a multivariate Cox regression model with steroid use, MGMT promotor methylation status, tumor diameter and localization, the authors found a significant inferior outcome in patients with TMT below 7,2mm.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThese findings are in line with a high-quality retrospective cohort study performed by Huq et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] They analyzed 149 patients with progressive glioblastoma. In a multivariable Cox regression analysis they found a TMT value below 7,1mm predictive for inferior outcome.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe literature concerning progressive glioblastoma is limited to these two reports. Both draw the same conclusion from their respective patient database which is probably even more heterogeneous compared to studies concerning newly diagnosed patients. Huq et al. however found, interestingly, no prognostic value of TMT in their cohort with only newly diagnosed patients. The validity of these results is difficult to appraise at this moment due to the limited number of studies and strong heterogeneity of the patient population after disease progression. However, TMT values seem to decline during treatment in a subgroup of patients (60% in the study cohort of newly diagnosed glioblastoma patients of Furtner et al.).[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] It remains uncertain if this subgroup might experience systemic consequences of temozolomide treatment, rendering these patients less resilient to second-line treatment. Further analysis could shed more light on this topic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and strengths\u003c/h2\u003e \u003cp\u003eThe retrospective design of this cohort implies an increased risk for bias in the clinical information gathering. For example, a significant number of patients had to be excluded due to incompleteness of the medical files with missing radiographical or molecular data. Due to this smaller patient cohort, statistical power might be lacking to identify a statistically significant but small prognostic role of temporal muscle measurements in IDH-wildtype glioblastoma patients. On the other hand, this is the first study to use temporal muscle volume as a covariate. Although this parameter might seem more representative for the real \u0026ldquo;muscle health\u0026rdquo; in patients, no clear prognostic role could be identified.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis retrospective cohort study could not find any prognostic value of temporal muscle measurements in newly diagnosed IDH-wildtype glioblastoma patients. These findings are in line with some other well executed cohort studies. The literature concerning the prognostic value of temporal muscle measurements uses heterogenous statistical methods to analyze TMT as predictor for outcome making firm conclusions unconvincing. Overall, the evidence in favor for TMT as an individual prognostic factor in glioblastoma seems rather controversial at best.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThere was no funding obtained for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHP: study design, statistics, manuscript writing.CDR, LDB and AVS: data collection, manuscript review.TB and GH: study design, manuscript review.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eConflict of interests\u003c/span\u003e \u003c/p\u003e \u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBroen MPG, Beckers R, Willemsen ACH, Huijs SMH, Pasmans R, Eekers DBP et al (2022) Temporal muscle thickness as an independent prognostic imaging marker in newly diagnosed glioblastoma patients: A validation study. Neuro-oncology Adv 4(1):vdac038. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/noajnl/vdac038\u003c/span\u003e\u003cspan address=\"10.1093/noajnl/vdac038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T et al (2019) Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(4):601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ageing/afz046\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afz046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis MP, Panikkar R (2019) Sarcopenia associated with chemotherapy and targeted agents for cancer therapy. Annals Palliat Med 8(1):86\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/apm.2018.08.02\u003c/span\u003e\u003cspan address=\"10.21037/apm.2018.08.02\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurtner J, Berghoff AS, Albtoush OM, Woitek R, Asenbaum U, Prayer D et al (2017) Survival prediction using temporal muscle thickness measurements on cranial magnetic resonance images in patients with newly diagnosed brain metastases. Eur Radiol 27(8):3167\u0026ndash;3173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-016-4707-6\u003c/span\u003e\u003cspan address=\"10.1007/s00330-016-4707-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurtner J, Genbrugge E, Gorlia T, Bendszus M, Nowosielski M, Golfinopoulos V et al (2019) Temporal muscle thickness is an independent prognostic marker in patients with progressive glioblastoma: translational imaging analysis of the EORTC 26101 trial. Neurooncology 21(12):1587\u0026ndash;1594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/neuonc/noz131\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/noz131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurtner J, Weller M, Weber M, Gorlia T, Nabors B, Reardon DA et al (2022) Temporal Muscle Thickness as a Prognostic Marker in Patients with Newly Diagnosed Glioblastoma: Translational Imaging Analysis of the CENTRIC EORTC 26071\u0026thinsp;\u0026ndash;\u0026thinsp;22072 and CORE Trials. Clin cancer research: official J Am Association Cancer Res 28(1):129\u0026ndash;136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-21-1987\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-21-1987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabowski MM, Recinos PF, Nowacki AS, Schroeder JL, Angelov L, Barnett GH et al (2014) Residual tumor volume versus extent of resection: predictors of survival after surgery for glioblastoma. J Neurosurg 121(5):1115\u0026ndash;1123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/2014.7.JNS132449\u003c/span\u003e\u003cspan address=\"10.3171/2014.7.JNS132449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352(10):997\u0026ndash;1003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa043331\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa043331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuq S, Khalafallah AM, Ruiz-Cardozo MA, Botros D, Oliveira LAP, Dux H et al (2021) A novel radiographic marker of sarcopenia with prognostic value in glioblastoma. Clin Neurol Neurosurg 207:106782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clineuro.2021.106782\u003c/span\u003e\u003cspan address=\"10.1016/j.clineuro.2021.106782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarschnia P, Vogelbaum MA, van den Bent M, Cahill DP, Bello L, Narita Y et al (2021) Evidence-based recommendations on categories for extent of resection in diffuse glioma. Eur J cancer (Oxford England: 1990) 149:23\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejca.2021.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ejca.2021.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamborn KR, Chang SM, Prados MD (2004) Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neurooncology 6(3):227\u0026ndash;235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1215/S1152851703000620\u003c/span\u003e\u003cspan address=\"10.1215/S1152851703000620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeitner J, Pelster S, Schopf V, Berghoff AS, Woitek R, Asenbaum U et al (2018) High correlation of temporal muscle thickness with lumbar skeletal muscle cross-sectional area in patients with brain metastases. PLoS ONE 13(11):e0207849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0207849\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0207849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalmstrom A, Gronberg BH, Marosi C, Stupp R, Frappaz D, Schultz H et al (2012) Temozolomide versus standard 6-week radiotherapy versus hypofractionated radiotherapy in patients older than 60 years with glioblastoma: the Nordic randomised, phase 3 trial. Lancet Oncol 13(9):916\u0026ndash;926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1470-2045(12)70265-6\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(12)70265-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerry JR, Laperriere N, O'Callaghan CJ, Brandes AA, Menten J, Phillips C et al (2017) Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma. N Engl J Med 376(11):1027\u0026ndash;1037. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1611977\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1611977\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinson H, Silversmit G, Vanhauwaert D, Vanschoenbeek K, Okito JK, De Vleeschouwer S et al (2024) Epidemiology and survival of adult-type diffuse glioma in Belgium during the molecular era. Neurooncology 26(1):191\u0026ndash;202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/neuonc/noad158\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/noad158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadhwani N, Aggarwal A, Mishra A, Garg K (2022) Temporal muscle thickness as an independent prognostic marker in glioblastoma patients-a systematic review and meta-analysis. Neurosurg Rev 45(6):3619\u0026ndash;3628. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10143-022-01892-3\u003c/span\u003e\u003cspan address=\"10.1007/s10143-022-01892-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteindl A, Leitner J, Schwarz M, Nenning KH, Asenbaum U, Mayer S et al (2020) Sarcopenia in Neurological Patients: Standard Values for Temporal Muscle Thickness and Muscle Strength Evaluation. J Clin Med 9(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm9051272\u003c/span\u003e\u003cspan address=\"10.3390/jcm9051272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987\u0026ndash;996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa043330\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa043330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStupp R, Hegi ME, Gorlia T, Erridge SC, Perry J, Hong YK et al (2014) Cilengitide combined with standard treatment for patients with newly diagnosed glioblastoma with methylated MGMT promoter (CENTRIC EORTC 26071\u0026thinsp;\u0026ndash;\u0026thinsp;22072 study): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol 15(10):1100\u0026ndash;1108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1470-2045(14)70379-1\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(14)70379-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTen Cate C, Huijs SMH, Willemsen ACH, Pasmans R, Eekers DBP, Zegers CML et al (2022) Correlation of reduced temporal muscle thickness and systemic muscle loss in newly diagnosed glioblastoma patients. J Neurooncol 160(3):611\u0026ndash;618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11060-022-04180-8\u003c/span\u003e\u003cspan address=\"10.1007/s11060-022-04180-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWende T, Kasper J, Prasse G, Glass A, Kriesen T, Freiman TM et al (2021) Newly Diagnosed IDH-Wildtype Glioblastoma and Temporal Muscle Thickness: A Multicenter Analysis. Cancers 13(22). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers13225610\u003c/span\u003e\u003cspan address=\"10.3390/cancers13225610\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWick W, Gorlia T, Bendszus M, Taphoorn M, Sahm F, Harting I et al (2017) Lomustine and Bevacizumab in Progressive Glioblastoma. N Engl J Med 377(20):1954\u0026ndash;1963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1707358\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1707358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"glioblastoma, treatment outcome, temporal muscle thickness, sarcopenia.","lastPublishedDoi":"10.21203/rs.3.rs-5767366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5767366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose.\u003c/h2\u003e \u003cp\u003eTemporal muscle thickness has been suggested as an independent prognostic marker for glioblastoma patient outcome. Various cohort studies show however conflicting results. This study therefore aims to reevaluate the prognostic value of different types of temporal muscle measurements in glioblastoma patients.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was performed including 137 patients diagnosed with IDH-wildtype glioblastoma. Temporal muscle thickness (TMT) and volume (TMV) were measured on preoperative MR-imaging. Next, these measurements were thereafter used in a multivariate Cox survival analysis to identify their possible prognostic value. These results were compared to the literature after systematic review of the Medline database.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eTMT has a moderate to strong linear correlation with total muscle volume (Pearson r\u0026thinsp;=\u0026thinsp;0,6; P\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Glioblastoma patients \u0026ldquo;at risk for sarcopenia\u0026rdquo; show similar outcome compared to controls (median overall survival time: 13 months vs 11 months; P\u0026thinsp;=\u0026thinsp;0,775). In a covariate Cox regression model, none of the temporal muscle measurements (TMT, TMV or sex-specific cut-off points) showed prognostic value for outcome in glioblastoma patients.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eTemporal muscle measurements show no independent relation to clinical outcome in IDH-wildtype glioblastoma patients. There seems adequate linear correlation of temporal muscle thickness and overall muscle volume. The literature on temporal muscle measurements was found to be severely flawed and should be interpreted with caution.\u003c/p\u003e","manuscriptTitle":"Temporal muscle measurements as predictor for outcome in a cohort of IDH-wildtype glioblastoma patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 12:54:33","doi":"10.21203/rs.3.rs-5767366/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9da4e59-5b8f-4b52-b2d3-fc5e4b3ec4db","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T02:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 12:54:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5767366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5767366","identity":"rs-5767366","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}