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13 June 2024

Association between the Anatomical Location of Glioblastoma and Its Evaluation with Clinical Considerations: A Systematic Review and Meta-Analysis

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1
Departamento de Ciencias Química y Biológicas, Facultad de Ciencias de la Salud, Universidad Bernardo O’Higgins, Santiago 8320000, Chile
2
Departament de Morfología, Facultad de Medicina, Universidad Andrés Bello, Santiago 8370146, Chile
3
Escuela de Medicina, Facultad Ciencias de la Salud, Universidad del Alba, Santiago 8320000, Chile
4
Escuela de Medicina, Universidad Finis Terrae, Santiago 7501015, Chile
J. Clin. Med.2024, 13(12), 3460;https://doi.org/10.3390/jcm13123460
This article belongs to the Section Clinical Neurology
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Abstract

Background: Glioblastoma is a primary malignant brain tumor; it is aggressive with a high degree of malignancy and unfavorable prognosis and is the most common type of malignant brain tumor. Glioblastomas can be located in the brain, cerebellum, brainstem, and spinal cord, originating from glial cells, particularly astrocytes. Methods: The databases MEDLINE, Scopus, Web of Science, Google Scholar, and CINAHL were researched up to January 2024. Two authors independently performed the search, study selection, and data extraction. Methodological quality was evaluated with an assurance tool for anatomical studies (AQUA). The statistical mean, standard deviation, and difference of means calculated with the Student’s t-test for presence between hemispheres and presence in the frontal and temporal lobes were analyzed. Results: A total of 123 studies met the established selection criteria, with a total of 6224 patients. In relation to the mean, GBM between hemispheres had a mean of 33.36 (SD 58.00) in the right hemisphere and a mean of 34.70 (SD 65.07) in the left hemisphere, due to the difference in averages between hemispheres. There were no statistically significant differences, p = 0.35. For the comparison between the presence of GBM in the frontal lobe and the temporal lobe, there was a mean in the frontal lobe of 23.23 (SD 40.03), while in the temporal lobe, the mean was 22.05 (SD 43.50), and for the difference in means between the frontal lobe and the temporal lobe, there was no statistically significant difference for the presence of GBM, p = 0.178. Conclusions: We believe that before a treatment, it will always be correct to know where the GBM is located and how it behaves clinically, in order to generate correct conservative or surgical treatment guidelines for each patient. We believe that more detailed studies are also needed to show why GBM is associated more with some regions than others, despite the brain structure being homologous to other regions in which GMB occurs less frequently, which is why knowing its predominant presence in brain regions is very important.

1. Introduction

Glioblastoma is a primary malignant brain tumor. It is aggressive with a high degree of malignancy and unfavorable prognosis, and it is also the most common type of malignant brain tumor. This tumor of the central nervous system can be in the brain, cerebellum, brainstem, or spinal cord. It originates from glial cells, specifically the astrocyte. Based on their histological characteristics, astrocytomas are classified into grades ranging from I to IV. Grade IV corresponds to glioblastomas multiforme (GBM), which is the most unfavorable grade with a mean survival of 12–16 months even with treatment. Even with great advances in the pathogenesis and molecular biology of this tumor, it continues to have a poor prognosis once diagnosed.
According to the literature, the prevalence of GBM ranges from 0.59 to 5 per 100,000 inhabitants, being slightly higher for males and increasing in older patients. This disease is increasing in several countries, which may be attributed to various factors such as aging populations, air pollution, and technological advances associated with the use of more precise neurological examinations. However, the latter may lead to overdiagnosis of this pathology [1,2,3,4,5].
Depending on the mutation in the enzyme isocitrate dehydrogenase (IDH), a mutation that consists of a state of hypermethylation, astrocytoma can continue with a less aggressive or more aggressive clinical course, the latter being associated with wild-type IDH and linked to a primary tumor. Its progression has been described, with the presence of astrocyte-like stem cells in the astrocytic ribbon found in the frontal and temporal lobes close to the subventricular zone [4,6,7,8,9].
The objective of this review was to assess the location of GBM in the brain in terms of lobes, area, and hemisphere, and how it is related to specific clinical behaviors according to its location.

2. Methods

2.1. Protocol and Registration

This systematic review and meta-analysis were performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [9]. The registration number in the International Prospective Register of Systematic Reviews (PROSPERO) is CRD42022224066.

2.2. Eligibility Criteria

Studies on the presence of GBM in different encephalic regions and its association with any clinical condition were considered eligible for inclusion if the following criteria were fulfilled: (1) population: sample of dissections or images of the presence of GBM; (2) outcomes: GBM presence, variants, and their correlation with symptomatology of the brain and other encephalic structures. Additionally, anatomical variants were classified and described based on normal anatomy and classifications and description proposed in the literature; (3) studies: this systematic review included research articles, research reports, and original research published in English in peer-reviewed journals and indexed in any of the databases reviewed. Conversely, the exclusion criteria were as follows: (1) population: animal studies; (2) studies that performed analysis of other involucred structures and pathologies outside the encephalic region; (3) studies: letters to the editor or comments.

2.3. Electronic Search

We systematically searched MEDLINE (via PubMed), Web of Science, Google Scholar, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Scopus, from inception until January 2024. The search strategy included a combination of the following terms: “glioblastoma” (mesh), “encephalic glioblastoma” (no mesh), “brain lobe” (no mesh), and “clinical anatomy” (no mesh) using the Boolean connectors AND, OR, and NOT. The search strategies for each database are available in the Table 1.
Table 1. Characteristics of the included studies.

2.4. Study Selection

Two authors (LM and VS) independently screened the titles and abstracts of references retrieved from the searches. We obtained the full text for references that either author considered to be potentially relevant. We involved a third reviewer (DM) if consensus could not be reached.

2.5. Data Collection Process

Two authors (JS and MO) independently extracted data on the outcomes of each study. The following data were extracted from the original reports: (i) authors and year of publication, (ii) type of study and subject numbers, (iii) incidence and anatomical location of GBM, (iv) statistical data, (v) geographical location, (vi) laterality, (vii) gender, (viii) relevant clinical considerations.

2.6. Assessment of the Methodological Quality of the Included Studies

The quality assessment was performed using the methodological quality assurance tool for anatomical studies (AQUA) proposed by the International Evidence-Based Anatomy Working Group (IEBA) [131,132,133]. Data extraction and quality assessment were independently performed by two reviewers (JJV and PN). We involved a third reviewer (JSG) if consensus could not be reached. The agreement rate between the reviewers was calculated using kappa statistics.

2.7. Statistical Methods

Regarding the statistical analysis, we calculated the mean and standard deviation of the subjects with GBM, grouped according to hemisphere and lobes. Then we collected the data and entered them into Excel software (v.12.1.3) for better tabulation. Statistical analysis was subsequently performed using the Student’s t-test to evaluate whether the population was normally distributed. This method is suitable for small groups, as in our research, and was carried out to assess the predominance of hemispheres and the predominance between the appearance of GBM in the frontal lobe and in the temporal lobe.

3. Results

In this review, 121 studies were included with a sample size of 6224 patients with glioblastoma, of which 64 studies corresponding to 52.89% of the total were retrospective studies [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73], 27 (22.31%) prospective studies [74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100], 24 (19.83%) case reports [101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124], 3 (2.48%) cadaveric studies [125,126,127], 2 (1.65%) cross-sectional studies [128,129], and finally, only 1 (0.83%) study that presented a case report along with a literature review [130]. The samples included in the studies were distributed across all continents with the exception of Africa; the Asian continent was represented by a total of 56 studies corresponding to 46.28% of the total studies and a sample of 2801 corresponding to 45.00% of the total patients [10,12,16,17,19,20,21,24,27,30,31,32,35,36,39,40,41,42,43,44,46,49,51,53,59,65,66,67,68,69,71,73,75,76,77,83,86,87,88,91,93,94,97,99,101,103,105,107,109,111,116,119,120,122,124], the European continent accounted for a total of 34 (28.10%) studies and 1281 (20.58%) patients [15,18,28,29,45,47,48,50,57,61,63,70,72,74,78,79,80,81,82,84,85,89,90,95,96,102,104,112,113,117,123,125,130]; North America contributed 28 (23.14%) studies and 2030 (32.62%) patients [11,13,22,23,33,34,37,52,54,55,56,58,60,64,87,92,98,100,106,108,110,114,115,118,126,127,128,129]; and South America provided only 3 (2.48%) studies and a total of 112 (1.89%) patients [14,38,121]. With respect to sex, 104 studies detailed the sex of patients with glioblastoma and 17 studies only mentioned the total number of patients without differentiating them by sex and/or differentiating some only some or a few specific cases from the total presented [13,15,25,34,39,41,45,50,53,64,66,68,74,84,86,88,112]. Therefore, 1949 patients were counted for the female sex, corresponding to 31.31% of the total, and2873 patients were counted for the male sex, corresponding to 46.16% of the total. In the same way, the number of patients who were not differentiated by sex was counted, obtaining 1402 undetermined patients, corresponding to 22.53% of the total (Figure 1).
Figure 1. Search diagram.

3.1. Statistical Results

Reviewing the articles consulted, a total of 1768 cases were reported, with a mean of 33.36 and a standard deviation of 58.00, for those who presented glioblastoma in the right hemisphere of the brain, while for the left hemisphere, there were 1664 cases, with a mean of 34.70 and a standard deviation of 65.07. It is worth mentioning that the table refers to certain cases where the number of patients diagnosed with glioblastoma did not coincide with the data on the number of hemispheres affected, due to the following possible reasons: the articles considered patients who did not present glioblastoma, they did not detail all patients with glioblastoma, laterality was not specified, or the classification was imprecise [13,15,17,26,32,37,64,66,74,83,125,130] (Table 2 and Table 3).
Table 2. Presence of glioblastoma in cerebral hemispheres.
Table 3. Mean and standard deviation in the presence of glioblastoma per cerebral hemisphere.
For the statistical values found between the differences in the presence of GBM in the right hemisphere (HR) and the left hemisphere (HL), we employed the Shapiro–Wilk test to examine the normal distribution of GBM presence in each hemisphere. The test gave a p-value of <0.001, indicating a statistically significant difference between the means of the samples included for the analysis. It is important to note that this refers only to the sample means and not to the presence of GBM itself. For the statistical difference of means between the presence of GBM between hemispheres, the scores for the HR were a mean of 35.5 and a SD of 68.9, which were higher than those for HL, which were a mean of 22.6 and a SD of 37.0. Although the presence of GBM in the HR was higher, the Student’s t-test showed that there was no statistically significant difference for the presence of GBM in the interhemispheric comparison p = 0.352.
The total number of cases reported as affected for the following brain regions were as follows: frontal lobe in 1812 cases, with a mean of 23.23 and a standard deviation of 40.03; parietal lobe in 874 cases, with a mean of 14.81 and a standard deviation of 28.56; temporal lobe in 1609 cases, with a mean of 22.04 and a standard deviation of 43.50; occipital lobe in 388 cases, with a mean of 8.62 and a standard deviation of 17.52; insula in 101 cases, with a mean of 9.18 and a standard deviation of 14.86; diencephalon in 46 cases, with a mean of 3.06 and a standard deviation of 2.69; brainstem in 21 cases, with a mean of 1.62 and a standard deviation of 1.12; cerebellum in 38 cases, with a mean of 2.71 and a standard deviation of 2.58; and other structures in 1275 cases, with a mean of 21.61 and a standard deviation of 37.25. However, differences were observed between the number of patients diagnosed with glioblastoma and the number of regions affected by the tumor, which may be due to any of the following reasons: locations of patients with different types of gliomas were reported without making a difference; of those who presented glioblastoma, it was detailed or assumed that patients manifested more than one affected area; or not all of the regions involved were reported [13,15,17,26,32,37,64,66,74,83,125,130] (Table 4) and Figure 2 and Figure 3.
Table 4. Presence of glioblastoma in cerebral lobes and brain regions.
Figure 2. (a,b): Numbers of subjects with glioblastoma per brain lobe.
Figure 3. Numbers of subjects with glioblastoma per cerebral hemisphere.
Regarding the statistical values found between the differences in the presence of GBM in different lobes, we considered only the frontal lobe and the temporal lobe for the analysis since in our study, these two were the lobes with the highest proportions of cases. It was more correct to analyze them according to the number of appearances in the population of each study. In relation to the above, for the frontal lobe and the temporal lobe, we used the data to calculate the normal distribution between the presence of GBM in the frontal lobe and GBM in the parietal lobe. The result was <0.180, showing that there was no statistically significant difference between the means of the samples included for the analysis. For the statistical difference of means between the presence of GBM between frontal and parietal lobes, where the values obtained for the frontal lobe were a mean of 23.0 and an SD of 23.3, which were lower than those of the temporal lobe, which were a mean of 27.5 and an SD of 24.8, although the presence of GBM in the temporal lobe was greater, the Student’s t-test result of 0.178 showed no statistically significant difference for the presence of GBM between lobes.

3.2. Risk of Bias of Included Studies

In total, 123 articles were evaluated with the AQUA Checklist to analyze the risk of bias in five domains. For the first domain, which covers the description of the objectives and characteristics of the study, all studies presented a low risk of bias. The second domain is the correct reporting of the study design; 120 studies presented a low risk of bias in this domain, and 3 presented a high risk since they did not clearly report the design of their studies [12,29,42]. For the third domain, which analyzes the study’s methodological characteristics, 119 studies presented a low risk of bias, while 4 presented a high risk since their methodology was unclear [29,39,67,69]. The fourth domain is the correct description of anatomy; 116 studies presented a low risk of bias in this domain, while 7 studies presented a higher risk since they did not include an anatomical description of the variant but instead merely named it [15,19,31,37,47,69,73]. In the final domain, which involves reporting results, 110 studies presented a low risk of bias, and 13 studies presented a high risk of bias since their results were presented diffusely in tables or in discussion sections [17,20,22,23,24,37,40,52,54,58,69,71,75] (Figure 4).
Figure 4. AQUA Checklist.

3.3. Clinical Implications

The clinical implications associated with the anatomical location of glioblastoma were analyzed in different articles, with a total of 107 articles in which a relationship was not made between the region of glioblastoma and a clinical implication [10,11,13,18,19,20,21,22,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,70,71,72,73,74,75,77,78,79,80,81,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,104,105,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,123,124,125,126,127,128,129,130]. Regarding tumor location being considered an important factor, it has been suggested that depending on the specific area affected, the tumor’s aggression and its impact on patient survival can be understood. Furthermore, it is considered that the most frequently involved areas are the frontal and temporal lobes, both of which have different clinical properties. In the review of the articles, it was found that among patients diagnosed with glioblastoma, those who were advanced in age had a greater likelihood of the affected area(s) being the bilateral temporal lobe, while in young patients, the tumor was predominantly located in the left lower frontal region [14,23,42,69].
The possible locations of glioblastoma include the infratentorial region. These tumors are considered unusual, complex, and rare, due to the low life expectancy of patients who present them. In adults, the incidence is in the range of 1.2% of all patients with glioblastoma, however, in infants, the infratentorial location is predominant. This type of glioblastoma can be difficult to diagnose and even misleading due to nonspecific symptoms and radiological characteristics. Nevertheless, a common clinical characteristic described is a rapid deterioration of ataxia and dysmetria, and likewise, compression of segments of the spinal cord can lead to lower extremity movement disorders, pain, and abnormal urination. It is worth mentioning that based on what was described by Stark [15], it is considered that from a pathological point of view, infratentorial glioblastoma has similar behavior to the supratentorial type [15,16,17,101,103].
Regarding the symptoms and signs reported in the review, an apparent relationship can be established between glioblastoma located in the frontal lobe and mood symptoms; however, it is considered not significant. On the other hand, it has been described that the spinal metastatic dissemination of intracranial glioblastoma, which is usually simultaneous or sequential to the progression of the latter, does not present symptoms in most patients or these are of late onset. Moreover, these symptoms are often masked by intracranial progression of the tumor, which leads to motor disorders and sensory anomalies. Another observation made is regarding the location in the pineal region, which in addition to being considered very rare and with an unfavorable prognosis, is associated with a high frequency of hydrocephalus and Parinaud syndrome [17,102,106].
Finally, when comparing glioblastoma to other pathologies, primary brain lymphoma is mentioned, presenting important differences in terms of the anatomical location of the tumor. Regarding solitary brain metastases, there appears to be no difference between sex. However, patients with solitary brain metastases are older than those with glioblastoma [12,16,76,86].

4. Discussion

This systematic review and meta-analysis aimed to explore the different locations of GBM and their association with various clinical considerations. For this purpose, a review of different studies related to the presence of glioblastoma was conducted, covering topics such as anatomical location, descriptions, characteristics, and prevalence within the different areas of the brain and spinal cord. Based on our inclusion and exclusion criteria, a total of 121 articles were identified. The main finding of this review is that GBM can occur in various regions of the brain and depending on the location, it may present with varied symptomatology leading to diverse differential diagnoses.
In relation to previous review studies that have analyzed the anatomical location of glioblastoma, we did not find any that report the same relationship as presented in this meta-analysis. Therefore, this study represents a first-time effort in filtering and analyzing information on this manner, making it a novel and up-to-date study. However, when searching the databases with the search string “brain” OR “encephalic” location of glioblastoma, we found three studies that mentioned some type of information in their sections.
The study by Zarnet [134], which mentions among its results that one of the main locations of GBM is the premotor cortex but focuses mainly on treatment through radiotherapy, did not make a detailed study of the anatomy and location of the GBM, thus presenting very low similarity to our review. The study by Maslehaty [130], which showed as its main results that GBMs could be located not only in specific encephalic structures but also in leptomeninges, without specifying which of these meninges, and also proposed radiotherapy as the gold standard for possible treatment. Again, our review differs due to the characteristics previously mentioned. Lastly, the review by Corr [135] showed as its main results that there has been a lack of prospective studies analyzing the prognostic characteristics of glioblastoma together with its anatomical and radiological characteristics, which would eventually facilitate the early identification of glioblastoma recurrence, thus supporting a more personalized treatment and follow-up strategy. Although that study does not make the case specifically, it supports the findings of the current review and highlights the importance of specific knowledge and location of the GBM which can lead to better management and treatment of patients with this pathology.
The characteristics of the included studies were as follows. For this review, after applying our inclusion and exclusion criteria, 121 were included with a total sample of 6224 subjects with GBM. Regarding the geographical location, the distribution was primarily across the continents of Asia, Europe, and North America. Therefore, it cannot be inferred that the presence of GBM is associated with any geographic region or that it is linked to any ethnicity or race. Moreover, the gender distribution showed a higher occurrence in men than in women; however, the difference was not statistically significant, indicating that the presence of GBM cannot be associated with a specific sex. We believe all these characteristics should be validated and supported by further studies; although the sample of this review is representative, new studies could confirm or refute these exposed results. One of the strengths we believe this review contributes is the statistical report we present regarding the characteristics and location of GBM. Concerning the hemisphere where GBM was found, there were no differences between the left and right hemispheres of the brain. In the right hemisphere, the mean was 33.36 subjects, while for the left hemisphere, it was 34.7. Although the mean occurrence of interhemispheric GBM presence was higher in the right hemisphere, the Student’s t-test for GBM presence being greater in one hemisphere than the other showed no statistically significant difference, leading us to believe that there is no predisposition for the appearance of GBM in a hemisphere, independent of the subject’s motor dominance. Thus, our results reject the supposition that the appearance is associated with motor predominance. Regarding the location of GBM in relation to lobes, the main lobes where GBM was located were the frontal and temporal lobes.
Therefore, we performed statistical calculations comparing only these two lobes, understanding that if we compared them with other lobes, these two would always show a statistically higher presence of GBM. The comparison between the presence of GBM in the frontal and temporal lobes showed no statistically significant differences between the frontal and temporal lobe samples. In the comparison between its presence in one lobe over the other, GBM was more frequent in the temporal lobe, but the difference in its presence in this lobe compared with the frontal lobe was not statistically significant. Therefore, these data indicate that, while mainly located in these lobes, there was no result stating that GBM was specifically located more in one lobe than in the other. In the bias analysis of the included studies, the majority presented a moderate and low risk of bias, meaning the results can be correctly interpreted. However, caution is always advised since there were some domains that were not correctly met, which could alter the primary reported results to some extent.
Among our main objectives was to find evidence on the relationship between the region affected by glioblastoma and the clinical considerations it may entail. For this purpose, of the total sources consulted, 20 were used. However, we consider that the studies most relevant in support of our purpose were those that described the following: ataxia and dysmetria for infratentorial glioblastomas; the relationship between spinal segment disorders and disorders of lower limb movement, pain, and abnormal urination; a non-significant relationship between tumors in the frontal lobe and mood alteration; movement disorders and sensory anomalies with the progression of intracranial glioblastoma; and finally, hydrocephalus and Parinaud syndrome with glioblastoma in the pineal region.
In the analysis of the clinical considerations associated with infratentorial glioblastomas, such as those located in the cerebellum or spinal cord, we believe that the effects presented by patients with these conditions are due to alterations in the pathways that conduct sensorimotor information preventing, for example, adequate coordination of the lower extremities when walking, or simply their individual movement. Likewise, depending on the spinal segment that is affected by the glioblastoma, essentially the lumbar and/or sacral region, it could influence the innervation of the urinary system and, therefore, normal urination. We believe that intracranial progression can lead to such alterations because the brain regions responsible for developing and coordinating movements may be affected, along with areas for integration of sensory information. On the other hand, given the limited information known about the functioning of the pineal gland, we are unsure of its relationship with the conditions it generates when a tumor and/or glioblastoma develops in that location. Finally, regarding the relationship between the frontal lobe and mood, we consider that it should continue to be studied because, although the literature mentions that there is a relationship, it has been described as non-significant and, furthermore, no reference has been made to the limbic system, the main structure associated with the emotional state.
Although an assumption can be made about the explanation of the relationship between the anatomical region affected by glioblastoma and its clinical implications, we consider that it is important that future studies give it greater focus, to better understand the signs and symptoms that patients diagnosed with the pathology present or may present, with the aim of being able to provide adequate care and treatment and, at the same time, expanding knowledge in the area.

5. Limitations

This review was limited by the publication and authorship bias of the included studies. Firstly, studies with different results that were in non-indexed literature in the selected databases may have been excluded. Secondly, there may have been limitations in the sensitivity and specificity of the searches. Finally, the authors personally selected articles. All of this increases the probability of excluding potential cases from countries outside of Asia and North America that have not been reported in the scientific community.

6. Conclusions

The presence of a GBM will always be detrimental to the correct functioning of the brain structures. Knowledge of the specific location and area of the cortex that is affected by the GBM can teach us to better understand the clinical implications and avoid some types of differential diagnoses. This knowledge can help develop appropriate conservative or surgical treatment strategies for each patient. Future studies that can address the behavior of glial cells and the types of conditions that could contribute to the formation of GBM, with investigation of why this occurs more in some regions than in others, may be crucial.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [Google Scholar] [CrossRef] [PubMed]
  2. Grech, N.; Dalli, T.; Mizzi, S.; Meilak, L.; Calleja, N.; Zrinzo, A. Rising Incidence of Glioblastoma Multiforme in a Well-Defined Population. Cureus 2020, 12, e8195. [Google Scholar] [CrossRef] [PubMed]
  3. Tan, A.C.; Ashley, D.M.; López, G.Y.; Malinzak, M.; Friedman, H.S.; Khasraw, M. Management of glioblastoma: State of the art and future directions. CA Cancer J. Clin. 2020, 70, 299–312. [Google Scholar] [CrossRef] [PubMed]
  4. Verdugo, E.; Puerto, I.; Medina, M.Á. An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment. Cancer Commun. 2022, 42, 1083–1111. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  5. Angom, R.S.; Nakka, N.M.R.; Bhattacharya, S. Advances in Glioblastoma Therapy: An Update on Current Approaches. Brain Sci. 2023, 13, 1536. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  6. Wlodarczyk, A.; Grot, D.; Stoczynska-Fidelus, E.; Rieske, P. Gaps and Doubts in Search to Recognize Glioblastoma Cellular Origin and Tumor Initiating Cells. J. Oncol. 2020, 2020, 6783627. [Google Scholar] [CrossRef] [PubMed]
  7. Yoon, S.J.; Park, J.; Jang, D.S.; Kim, H.J.; Lee, J.H.; Jo, E.; Choi, R.J.; Shim, J.-K.; Moon, J.H.; Kim, E.-H.; et al. Glioblastoma Cellular Origin and the Firework Pattern of Cancer Genesis from the Subventricular Zone. J. Korean Neurosurg. Soc. 2020, 63, 26–33. [Google Scholar] [CrossRef]
  8. Montemurro, N.; Pahwa, B.; Tayal, A.; Shukla, A.; De Jesus Encarnacion, M.; Ramirez, I.; Nurmukhametov, R.; Chavda, V.; De Carlo, A. Macrophages in Recurrent Glioblastoma as a Prognostic Factor in the Synergistic System of the Tumor Microenvironment. Neurol. Int. 2023, 15, 595–608. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  9. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71, Erratum in Rev. Esp. Cardiol. 2022, 75, 192. [Google Scholar]
  10. Hashiguchi, M.; Tanaka, K.; Nagashima, H.; Fujita, Y.; Tanaka, H.; Kohta, M.; Nakai, T.; Uozumi, Y.; Maeyama, M.; Somiya, Y.; et al. Glutamic Acid and Total Creatine as Predictive Markers for Epilepsy in Glioblastoma by Using Magnetic Resonance Spectroscopy Before Surgery. World Neurosurg. 2022, 160, e501–e510. [Google Scholar] [CrossRef] [PubMed]
  11. Drabycz, S.; Roldán, G.; de Robles, P.; Adler, D.; McIntyre, J.B.; Magliocco, A.M.; Cairncross, J.G.; Mitchell, J.R. An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging. Neuroimage 2010, 49, 1398–1405. [Google Scholar] [CrossRef] [PubMed]
  12. Ko, C.C.; Tai, M.H.; Li, C.F.; Chen, T.Y.; Chen, J.H.; Shu, G.; Kuo, Y.T.; Lee, Y.C. Differentiation between Glioblastoma Multiforme and Primary Cerebral Lymphoma: Additional Benefits of Quantitative Diffusion-Weighted MR Imaging. PLoS ONE 2016, 11, e0162565. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  13. Abecassis, I.J.; Cordy, B.; Durfy, S.; Andre, J.B.; Levitt, M.R.; Ellenbogen, R.G.; Silbergeld, D.L.; Ko, A.L. Evaluating angioarchitectural characteristics of glial and metastatic brain tumors with conventional magnetic resonance imaging. J. Clin. Neurosci. 2020, 76, 46–52. [Google Scholar] [CrossRef] [PubMed]
  14. Ferreira, N.F.; Barbosa, M.; Amaral, L.L.; Mendonça, R.A.; Lima, S.S. Estudo através da ressonância magnética de 67 casos de glioblastoma multiforme e a ocorrência de metástases [Magnetic resonance imaging in 67 cases of glioblastoma multiforme and occurrence of metastases]. Arq Neuropsiquiatr. 2004, 62, 695–700. (In Portuguese) [Google Scholar] [CrossRef] [PubMed]
  15. Stark, A.M.; Maslehaty, H.; Hugo, H.H.; Mahvash, M.; Mehdorn, H.M. Glioblastoma of the cerebellum and brainstem. J. Clin. Neurosci. 2010, 17, 1248–1251. [Google Scholar] [CrossRef] [PubMed]
  16. Liu, S.; Fan, X.; Zhang, C.; Wang, Z.; Li, S.; Wang, Y.; Qiu, X.; Jiang, T. MR imaging based fractal analysis for differentiating primary CNS lymphoma and glioblastoma. Eur. Radiol. 2019, 29, 1348–1354. [Google Scholar] [CrossRef] [PubMed]
  17. Chen, J.; Shi, Q.; Li, S.; Zhao, Y.; Huang, H. Clinical characteristics of glioblastoma with metastatic spinal dissemination. Ann. Palliat. Med. 2022, 11, 506–512. [Google Scholar] [CrossRef] [PubMed]
  18. Jaskólski, D.J.; Fortuniak, J.; Majos, A.; Gajewicz, W.; Papierz, W.; Liberski, P.P.; Sikorska, B.; Stefańczyk, L. Magnetic resonance spectroscopy in intracranial tumours of glial origin. Neurol. Neurochir. Pol. 2013, 47, 438–449. [Google Scholar] [CrossRef] [PubMed]
  19. Kim, J.Y.; Yoon, M.J.; Park, J.E.; Choi, E.J.; Lee, J.; Kim, H.S. Radiomics in peritumoral non-enhancing regions: Fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma. Neuroradiology 2019, 61, 1261–1272. [Google Scholar] [CrossRef] [PubMed]
  20. Hatakeyama, J.; Ono, T.; Takahashi, M.; Oda, M.; Shimizu, H. Differentiating between Primary Central Nervous System Lymphoma and Glioblastoma: The Diagnostic Value of Combining 18F-fluorodeoxyglucose Positron Emission Tomography with Arterial Spin Labeling. Neurol. Med. Chir. 2021, 61, 367–375. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  21. Yamashita, K.; Hatae, R.; Hiwatashi, A.; Togao, O.; Kikuchi, K.; Momosaka, D.; Yamashita, Y.; Kuga, D.; Hata, N.; Yoshimoto, K.; et al. Predicting TERT promoter mutation using MR images in patients with wild-type IDH1 glioblastoma. Diagn. Interv. Imaging 2019, 100, 411–419. [Google Scholar] [CrossRef] [PubMed]
  22. Kuroiwa, T.; Numaguchi, Y.; Rothman, M.I.; Zoarski, G.H.; Morikawa, M.; Zagardo, M.T.; Kristt, D.A. Posterior fossa glioblastoma multiforme: MR findings. Am. J. Neuroradiol. 1995, 16, 583–589. [Google Scholar] [PubMed] [PubMed Central]
  23. Awad, A.W.; Karsy, M.; Sanai, N.; Spetzler, R.; Zhang, Y.; Xu, Y.; Mahan, M.A. Impact of removed tumor volume and location on patient outcome in glioblastoma. J. Neurooncol. 2017, 135, 161–171. [Google Scholar] [CrossRef] [PubMed]
  24. Quan, G.; Wang, T.; Ren, J.L.; Xue, X.; Wang, W.; Wu, Y.; Li, X.; Yuan, T. Prognostic and predictive impact of abnormal signal volume evolution early after chemoradiotherapy in glioblastoma. J. Neurooncol. 2023, 162, 385–396. [Google Scholar] [CrossRef] [PubMed]
  25. Onuma, K.; Ishikawa, E.; Matsuda, M.; Hirata, K.; Osuka, S.; Yamamoto, T.; Masumoto, T.; Zaboronok, A.; Matsumura, A. Clinical characteristics and neuroimaging findings in 12 cases of recurrent glioblastoma with communicating hydrocephalus. Neurol. Med. Chir. 2013, 53, 474–481. [Google Scholar] [CrossRef] [PubMed]
  26. Cui, M.; Chen, H.; Sun, G.; Liu, J.; Zhang, M.; Lin, H.; Sun, C.; Ma, X. Combined use of multimodal techniques for the resection of glioblastoma involving corpus callosum. Acta Neurochir. 2022, 164, 689–702. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  27. Chen, L.; Li, T.; Li, Y.; Zhang, J.; Li, S.; Zhu, L.; Qin, J.; Tang, L.; Zeng, Z. Combining amide proton transfer-weighted and arterial spin labeling imaging to differentiate solitary brain metastases from glioblastomas. Magn. Reson. Imaging 2023, 102, 96–102. [Google Scholar] [CrossRef] [PubMed]
  28. Wach, J.; Hamed, M.; Schuss, P.; Güresir, E.; Herrlinger, U.; Vatter, H.; Schneider, M. Impact of initial midline shift in glioblastoma on survival. Neurosurg. Rev. 2021, 44, 1401–1409. [Google Scholar] [CrossRef] [PubMed]
  29. Steidl, E.; Filipski, K.; Hattingen, E.; Steinbach, J.P.; Maurer, G.D. Longitudinal study on MRI and neuropathological findings: Neither DSC-perfusion derived rCBVmax nor vessel densities correlate between newly diagnosed and progressive glioblastoma. PLoS ONE 2023, 18, e0274400. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  30. Park, M.; Lee, S.K.; Chang, J.H.; Kang, S.G.; Kim, E.H.; Kim, S.H.; Song, M.K.; Ma, B.G.; Ahn, S.S. Elderly patients with newly diagnosed glioblastoma: Can preoperative imaging descriptors improve the predictive power of a survival model? J. Neurooncol. 2017, 134, 423–431. [Google Scholar] [CrossRef] [PubMed]
  31. Ideguchi, M.; Kajiwara, K.; Goto, H.; Sugimoto, K.; Nomura, S.; Ikeda, E.; Suzuki, M. MRI findings and pathological features in early-stage glioblastoma. J. Neurooncol. 2015, 123, 289–297. [Google Scholar] [CrossRef] [PubMed]
  32. Wang, T.; Gao, T.; Niu, X.; Xing, X.; Yang, Y.; Liu, Y.; Mao, Q. Clinical Characteristics and Prognostic Analysis of Glioma in Human Immunodeficiency Virus-Infected Patients. World Neurosurg. 2018, 114, e218–e223. [Google Scholar] [CrossRef] [PubMed]
  33. Müller, D.M.J.; Robe, P.A.J.T.; Eijgelaar, R.S.; Witte, M.G.; Visser, M.; de Munck, J.C.; Broekman, M.L.D.; Seute, T.; Hendrikse, J.; Noske, D.P.; et al. Comparing Glioblastoma Surgery Decisions Between Teams Using Brain Maps of Tumor Locations, Biopsies, and Resections. JCO Clin. Cancer Inform. 2019, 3, 1–12. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  34. Thomas, J.G.; Rao, G.; Kew, Y.; Prabhu, S.S. Laser interstitial thermal therapy for newly diagnosed and recurrent glioblastoma. Neurosurg. Focus 2016, 41, E12. [Google Scholar] [CrossRef] [PubMed]
  35. Koike, H.; Morikawa, M.; Ishimaru, H.; Ideguchi, R.; Uetani, M.; Hiu, T.; Matsuo, T.; Miyoshi, M. Amide proton transfer MRI differentiates between progressive multifocal leukoencephalopathy and malignant brain tumors: A pilot study. BMC Med. Imaging 2022, 22, 227. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  36. Xing, Z.; Zhang, H.; She, D.; Lin, Y.; Zhou, X.; Zeng, Z.; Cao, D. IDH genotypes differentiation in glioblastomas using DWI and DSC-PWI in the enhancing and peri-enhancing region. Acta Radiol. 2019, 60, 1663–1672. [Google Scholar] [CrossRef] [PubMed]
  37. Smedley, N.F.; Ellingson, B.M.; Cloughesy, T.F.; Hsu, W. Longitudinal Patterns in Clinical and Imaging Measurements Predict Residual Survival in Glioblastoma Patients. Sci. Rep. 2018, 8, 14429. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  38. Miquelini, L.A.; Pérez Akly, M.S.; Funes, J.A.; Besada, C.H. Usefulness of the apparent diffusion coefficient for the evaluation of the white matter to differentiate between glioblastoma and brain metastases. Radiologia 2016, 58, 207–213. (In Spanish) [Google Scholar] [CrossRef] [PubMed]
  39. Han, Y.; Yan, L.F.; Wang, X.B.; Sun, Y.Z.; Zhang, X.; Liu, Z.C.; Nan, H.Y.; Hu, Y.C.; Yang, Y.; Zhang, J.; et al. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: A region of interest based analysis. BMC Cancer 2018, 18, 215. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  40. Mathew, B.S.; Kaliyath, S.B.; Krishnan, J.; Bhasi, S. Impact of subventricular zone irradiation on outcome of patients with glioblastoma. J. Cancer Res. Ther. 2018, 14, 1202–1206. [Google Scholar] [CrossRef] [PubMed]
  41. Jiang, S.; Rui, Q.; Wang, Y.; Heo, H.Y.; Zou, T.; Yu, H.; Zhang, Y.; Wang, X.; Du, Y.; Wen, X.; et al. Discriminating MGMT promoter methylation status in patients with glioblastoma employing amide proton transfer-weighted MRI metrics. Eur. Radiol. 2018, 28, 2115–2123. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  42. Li, H.Y.; Sun, C.R.; He, M.; Yin, L.C.; Du, H.G.; Zhang, J.M. Correlation between Tumor Location and Clinical Properties of Glioblastomas in Frontal and Temporal Lobes. World Neurosurg. 2018, 112, e407–e414. [Google Scholar] [CrossRef] [PubMed]
  43. Utsuki, S.; Oka, H.; Suzuki, S.; Shimizu, S.; Tanizaki, Y.; Kondo, K.; Tanaka, S.; Kawano, N.; Fujii, K. Pathological and clinical features of cystic and noncystic glioblastomas. Brain Tumor Pathol. 2006, 23, 29–34. [Google Scholar] [CrossRef] [PubMed]
  44. Fan, X.; Qi, C.; Liu, X.; Wang, Y.; Liu, S.; Li, S.; Wang, L.; Wang, Y. Regional specificity of matrix metalloproteinase-9 expression in the brain: Voxel-level mapping in primary glioblastomas. Clin. Radiol. 2018, 73, 283–289. [Google Scholar] [CrossRef] [PubMed]
  45. Hart, M.G.; Price, S.J.; Suckling, J. Connectome analysis for pre-operative brain mapping in neurosurgery. Br. J. Neurosurg. 2016, 30, 506–517. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  46. Wang, Y.; Fan, X.; Zhang, C.; Zhang, T.; Peng, X.; Li, S.; Wang, L.; Ma, J.; Jiang, T. Anatomical specificity of O6-methylguanine DNA methyltransferase protein expression in glioblastomas. J. Neurooncol. 2014, 120, 331–337. [Google Scholar] [CrossRef] [PubMed]
  47. Smets, T.; Lawson, T.M.; Grandin, C.; Jankovski, A.; Raftopoulos, C. Immediate post-operative MRI suggestive of the site and timing of glioblastoma recurrence after gross total resection: A retrospective longitudinal preliminary study. Eur. Radiol. 2013, 23, 1467–1477. [Google Scholar] [CrossRef] [PubMed]
  48. Eoli, M.; Menghi, F.; Bruzzone, M.G.; De Simone, T.; Valletta, L.; Pollo, B.; Bissola, L.; Silvani, A.; Bianchessi, D.; D’Incerti, L.; et al. Methylation of O6-methylguanine DNA methyltransferase and loss of heterozygosity on 19q and/or 17p are overlapping features of secondary glioblastomas with prolonged survival. Clin. Cancer Res. 2007, 13, 2606–2613. [Google Scholar] [CrossRef] [PubMed]
  49. Sugimoto, K.; Ideguchi, M.; Kimura, T.; Kajiwara, K.; Imoto, H.; Sadahiro, H.; Ishii, A.; Kawano, H.; Ikeda, E.; Suzuki, M. Epithelioid/rhabdoid glioblastoma: A highly aggressive subtype of glioblastoma. Brain Tumor Pathol. 2016, 33, 137–146. [Google Scholar] [CrossRef] [PubMed]
  50. Seidel, C.; Dörner, N.; Osswald, M.; Wick, A.; Platten, M.; Bendszus, M.; Wick, W. Does age matter?—A MRI study on peritumoral edema in newly diagnosed primary glioblastoma. BMC Cancer 2011, 11, 127. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  51. Cho, H.R.; Jeon, H.; Park, C.K.; Park, S.H.; Choi, S.H. Radiogenomics Profiling for Glioblastoma-related Immune Cells Reveals CD49d Expression Correlation with MRI parameters and Prognosis. Sci. Rep. 2018, 8, 16022. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  52. Olar, A.; Raghunathan, A.; Albarracin, C.T.; Aldape, K.D.; Cahill, D.P., 3rd; Powell, S.Z.; Goodman, J.C.; Fuller, G.N. Absence of IDH1-R132H mutation predicts rapid progression of nonenhancing diffuse glioma in older adults. Ann. Diagn. Pathol. 2012, 16, 161–170. [Google Scholar] [CrossRef] [PubMed]
  53. Woo, P.Y.M.; Ho, J.M.K.; Tse, T.P.K.; Lam, S.W.; Mak, C.H.K.; Chan, D.T.M.; Lee, M.W.Y.; Wong, S.T.; Chan, K.Y.; Poon, W.S. Determining a cut-off residual tumor volume threshold for patients with newly diagnosed glioblastoma treated with temozolomide chemoradiotherapy: A multicenter cohort study. J. Clin. Neurosci. 2019, 63, 134–141. [Google Scholar] [CrossRef] [PubMed]
  54. Ali, S.; Joseph, N.M.; Perry, A.; Barajas, R.F., Jr.; Cha, S. Apparent diffusion coefficient in glioblastoma with PNET-like components, a GBM variant. J. Neurooncol. 2014, 119, 353–360. [Google Scholar] [CrossRef] [PubMed]
  55. Mohan, S.; Wang, S.; Coban, G.; Kural, F.; Chawla, S.; O’Rourke, D.M.; Poptani, H. Detection of occult neoplastic infiltration in the corpus callosum and prediction of overall survival in patients with glioblastoma using diffusion tensor imaging. Eur. J. Radiol. 2019, 112, 106–111. [Google Scholar] [CrossRef] [PubMed]
  56. Mangla, R.; Singh, G.; Ziegelitz, D.; Milano, M.T.; Korones, D.N.; Zhong, J.; Ekholm, S.E. Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology. 2010, 256, 575–584. [Google Scholar] [CrossRef] [PubMed]
  57. Adeberg, S.; Bostel, T.; König, L.; Welzel, T.; Debus, J.; Combs, S.E. A comparison of long-term survivors and short-term survivors with glioblastoma, subventricular zone involvement: A predictive factor for survival? Radiat. Oncol. 2014, 9, 95. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  58. Wright, J.; Chugh, J.; Wright, C.H.; Alonso, F.; Hdeib, A.; Gittleman, H.; Barnholtz-Sloan, J.; Sloan, A.E. Laser interstitial thermal therapy followed by minimal-access transsulcal resection for the treatment of large and difficult to access brain tumors. Neurosurg. Focus 2016, 41, E14. [Google Scholar] [CrossRef] [PubMed]
  59. Shibahara, I.; Saito, R.; Osada, Y.; Kanamori, M.; Sonoda, Y.; Kumabe, T.; Mugikura, S.; Watanabe, M.; Tominaga, T. Incidence of initial spinal metastasis in glioblastoma patients and the importance of spinal screening using MRI. J. Neurooncol. 2019, 141, 337–345. [Google Scholar] [CrossRef] [PubMed]
  60. Tykocinski, E.S.; Grant, R.A.; Kapoor, G.S.; Krejza, J.; Bohman, L.E.; Gocke, T.A.; Chawla, S.; Halpern, C.H.; Lopinto, J.; Melhem, E.R.; et al. Use of magnetic perfusion-weighted imaging to determine epidermal growth factor receptor variant III expression in glioblastoma. Neuro Oncol. 2012, 14, 613–623. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  61. Kanas, V.G.; Zacharaki, E.I.; Thomas, G.A.; Zinn, P.O.; Megalooikonomou, V.; Colen, R.R. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput. Methods Programs Biomed. 2017, 140, 249–257. [Google Scholar] [CrossRef] [PubMed]
  62. Reimer, C.; Deike, K.; Graf, M.; Reimer, P.; Wiestler, B.; Floca, R.O.; Kickingereder, P.; Schlemmer, H.P.; Wick, W.; Bendszus, M.; et al. Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps. PLoS ONE 2017, 12, e0174620. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  63. Jiguet-Jiglaire, C.; Boissonneau, S.; Denicolai, E.; Hein, V.; Lasseur, R.; Garcia, J.; Romain, S.; Appay, R.; Graillon, T.; Mason, W.; et al. Plasmatic MMP9 released from tumor-infiltrating neutrophils is predictive for bevacizumab efficacy in glioblastoma patients: An AVAglio ancillary study. Acta Neuropathol. Commun. 2022, 10, 1. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  64. Senders, J.T.; Cho, L.D.; Calvachi, P.; McNulty, J.J.; Ashby, J.L.; Schulte, I.S.; Almekkawi, A.K.; Mehrtash, A.; Gormley, W.B.; Smith, T.R.; et al. Automating Clinical Chart Review: An Open-Source Natural Language Processing Pipeline Developed on Free-Text Radiology Reports From Patients With Glioblastoma. JCO Clin. Cancer Inform. 2020, 4, 25–34. [Google Scholar] [CrossRef] [PubMed]
  65. Zhang, M.; Chen, H.Z.; Cui, Y.Y.; Zhang, Z.Z.; Ma, X.D. The Associations Between Preoperative Conventional MRI Features and Genetic Biomarkers Status in Newly Diagnosed GBMs: A Clinical Summary and Prognostic Analysis. Turk. Neurosurg. 2021, 31, 880–887. [Google Scholar] [CrossRef] [PubMed]
  66. Liu, H.; Zhang, L.; Tan, Y.; Jiang, Y.; Lu, H. Observation of the delineation of the target volume of radiotherapy in adult-type diffuse gliomas after temozolomide-based chemoradiotherapy: Analysis of recurrence patterns and predictive factors. Radiat. Oncol. 2023, 18, 16. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  67. Cohen, O.; Biran, I.; Steiner, I. Cerebrospinal fluid oligoclonal IgG bands in patients with spinal arteriovenous malformation and structural central nervous system lesions. Arch. Neurol. 2000, 57, 553–557. [Google Scholar] [CrossRef] [PubMed]
  68. Nishio, S.; Morioka, T.; Suzuki, S.; Takeshita, I.; Fukui, M. Thalamic gliomas: A clinicopathologic analysis of 20 cases with reference to patient age. Acta Neurochir. 1997, 139, 336–342. [Google Scholar] [CrossRef] [PubMed]
  69. Wang, Y.; Liu, S.; Fan, X.; Li, S.; Wang, R.; Wang, L.; Ma, J.; Jiang, T.; Ma, W. Age-associated brain regions in gliomas: A volumetric analysis. J. Neurooncol. 2015, 123, 299–306. [Google Scholar] [CrossRef] [PubMed]
  70. Simonet Redondo, M.; Auger Acosta, C.; Rovira-Gols, A.; Toledo Argany, M.; Sarria Estrada, S.; Rovira Cañellas, A. Neuroimaging findings in the initial phase of development of high grade cortical astrocytomas. Radiologia 2012, 54, 424–431, English, Spanish. [Google Scholar] [CrossRef] [PubMed]
  71. Sunwoo, L.; Choi, S.H.; Yoo, R.E.; Kang, K.M.; Yun, T.J.; Kim, T.M.; Lee, S.H.; Park, C.K.; Kim, J.H.; Park, S.W.; et al. Paradoxical perfusion metrics of high-grade gliomas with an oligodendroglioma component: Quantitative analysis of dynamic susceptibility contrast perfusion MR imaging. Neuroradiology 2015, 57, 1111–1120. [Google Scholar] [CrossRef] [PubMed]
  72. Friese, S.A.; Bitzer, M.; Freudenstein, D.; Voigt, K.; Küker, W. Classification of acquired lesions of the corpus callosum with MRI. Neuroradiology 2000, 42, 795–802. [Google Scholar] [CrossRef] [PubMed]
  73. Okamoto, K.; Ito, J.; Takahashi, N.; Ishikawa, K.; Furusawa, T.; Tokiguchi, S.; Sakai, K. MRI of high-grade astrocytic tumors: Early appearance and evolution. Neuroradiology 2002, 44, 395–402. [Google Scholar] [CrossRef] [PubMed]
  74. Stummer, W.; Reulen, H.J.; Meinel, T.; Pichlmeier, U.; Schumacher, W.; Tonn, J.C.; Rohde, V.; Oppel, F.; Turowski, B.; Woiciechowsky, C.; et al. ALA-Glioma Study Group. Extent of resection and survival in glioblastoma multiforme: Identification of and adjustment for bias. Neurosurgery 2008, 62, 564–576. [Google Scholar] [CrossRef] [PubMed]
  75. Fudaba, H.; Momii, Y.; Matsuta, H.; Onishi, K.; Kawasaki, Y.; Sugita, K.; Shimomura, T.; Fujiki, M. Perfusion Parameter Obtained on 3-Tesla Magnetic Resonance Imaging and the Ki-67 Labeling Index Predict the Overall Survival of Glioblastoma. World Neurosurg. 2021, 149, e469–e480. [Google Scholar] [CrossRef] [PubMed]
  76. Wang, B.T.; Liu, M.X.; Chen, Z.Y. Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma. Chin. Med. Sci. J. 2019, 34, 10–17. [Google Scholar] [CrossRef] [PubMed]
  77. Mizumoto, M.; Yamamoto, T.; Ishikawa, E.; Matsuda, M.; Takano, S.; Ishikawa, H.; Okumura, T.; Sakurai, H.; Matsumura, A.; Tsuboi, K. Proton beam therapy with concurrent chemotherapy for glioblastoma multiforme: Comparison of nimustine hydrochloride and temozolomide. J. Neurooncol. 2016, 130, 165–170. [Google Scholar] [CrossRef] [PubMed]
  78. Isoardo, G.; Morra, I.; Chiarle, G.; Audrito, V.; Deaglio, S.; Melcarne, A.; Junemann, C.; Naddeo, M.; Cogoni, M.; Valentini, M.C.; et al. Different aquaporin-4 expression in glioblastoma multiforme patients with and without seizures. Mol. Med. 2012, 18, 1147–1151. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  79. Stumpo, V.; Sebök, M.; van Niftrik, C.H.B.; Seystahl, K.; Hainc, N.; Kulcsar, Z.; Weller, M.; Regli, L.; Fierstra, J. Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge. MAGMA 2022, 35, 29–44. [Google Scholar] [CrossRef] [PubMed]
  80. Iliadis, G.; Kotoula, V.; Chatzisotiriou, A.; Televantou, D.; Eleftheraki, A.G.; Lambaki, S.; Misailidou, D.; Selviaridis, P.; Fountzilas, G. Volumetric and MGMT parameters in glioblastoma patients: Survival analysis. BMC Cancer 2012, 12, 3. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  81. Galldiks, N.; Langen, K.J.; Holy, R.; Pinkawa, M.; Stoffels, G.; Nolte, K.W.; Kaiser, H.J.; Filss, C.P.; Fink, G.R.; Coenen, H.H.; et al. Assessment of treatment response in patients with glioblastoma using O-(2-18F-fluoroethyl)-L-tyrosine PET in comparison to MRI. J. Nucl. Med. 2012, 53, 1048–1057, Erratum in J. Nucl. Med. 2013, 54, 1846. [Google Scholar] [CrossRef] [PubMed]
  82. Henker, C.; Kriesen, T.; Fürst, K.; Goody, D.; Glass, Ä.; Pützer, B.M.; Piek, J. Effect of 10 different polymorphisms on preoperative volumetric characteristics of glioblastoma multiforme. J. Neurooncol. 2016, 126, 585–592. [Google Scholar] [CrossRef] [PubMed]
  83. Najafi, M.; Soltanian-Zadeh, H.; Jafari-Khouzani, K.; Scarpace, L.; Mikkelsen, T. Prediction of glioblastoma multiform response to bevacizumab treatment using multi-parametric MRI. PLoS ONE 2012, 7, e29945. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  84. Coburger, J.; Scheuerle, A.; Kapapa, T.; Engelke, J.; Thal, D.R.; Wirtz, C.R.; König, R. Sensitivity and specificity of linear array intraoperative ultrasound in glioblastoma surgery: A comparative study with high field intraoperative MRI and conventional sector array ultrasound. Neurosurg. Rev. 2015, 38, 499–509. [Google Scholar] [CrossRef] [PubMed]
  85. Hakyemez, B.; Erdogan, C.; Ercan, I.; Ergin, N.; Uysal, S.; Atahan, S. High-grade and low-grade gliomas: Differentiation by using perfusion MR imaging. Clin. Radiol. 2005, 60, 493–502. [Google Scholar] [CrossRef] [PubMed]
  86. Yu, H.; Lou, H.; Zou, T.; Wang, X.; Jiang, S.; Huang, Z.; Du, Y.; Jiang, C.; Ma, L.; Zhu, J.; et al. Applying protein-based amide proton transfer MR imaging to distinguish solitary brain metastases from glioblastoma. Eur. Radiol. 2017, 27, 4516–4524. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  87. Laule, C.; Bjarnason, T.A.; Vavasour, I.M.; Traboulsee, A.L.; Wayne Moore, G.R.; Li, D.K.B.; MacKay, A.L. Characterization of brain tumours with spin-spin relaxation: Pilot case study reveals unique T 2 distribution profiles of glioblastoma, oligodendroglioma and meningioma. J. Neurol. 2017, 264, 2205–2214. [Google Scholar] [CrossRef] [PubMed]
  88. Makino, K.; Hirai, T.; Nakamura, H.; Murakami, R.; Kitajima, M.; Shigematsu, Y.; Nakashima, R.; Shiraishi, S.; Uetani, H.; Iwashita, K.; et al. Does adding FDG-PET to MRI improve the differentiation between primary cerebral lymphoma and glioblastoma? Observer performance study. Ann. Nucl. Med. 2011, 25, 432–438. [Google Scholar] [CrossRef] [PubMed]
  89. Schneider, J.P.; Trantakis, C.; Rubach, M.; Schulz, T.; Dietrich, J.; Winkler, D.; Renner, C.; Schober, R.; Geiger, K.; Brosteanu, O.; et al. Intraoperative MRI to guide the resection of primary supratentorial glioblastoma multiforme--a quantitative radiological analysis. Neuroradiology 2005, 47, 489–500. [Google Scholar] [CrossRef] [PubMed]
  90. Weber, F.W.; Floeth, F.W.; Reifenberger, G.; Felsberg, J.; Aulich, A.; Kiwit, J.C.; Bock, W.J. MRI characteristics and histological changes in glioblastoma after gene therapy. Front. Radiat. Ther. Oncol. 1999, 33, 244–252. [Google Scholar] [CrossRef] [PubMed]
  91. Oriuchi, N.; Tomiyoshi, K.; Inoue, T.; Ahmad, K.; Sarwar, M.; Tokunaga, M.; Suzuki, H.; Watanabe, N.; Hirano, T.; Horikoshi, S.; et al. Independent thallium-201 accumulation and fluorine-18-fluorodeoxyglucose metabolism in glioma. J. Nucl. Med. 1996, 37, 457–462. [Google Scholar] [PubMed]
  92. Anzai, Y.; Lufkin, R.; DeSalles, A.; Hamilton, D.R.; Farahani, K.; Black, K.L. Preliminary experience with MR-guided thermal ablation of brain tumors. Am. J. Neuroradiol. 1995, 16, 39–48, discussion 49–52. [Google Scholar] [PubMed] [PubMed Central]
  93. Todo, T.; Ito, H.; Ino, Y.; Ohtsu, H.; Ota, Y.; Shibahara, J.; Tanaka, M. Intratumoral oncolytic herpes virus G47∆ for residual or recurrent glioblastoma: A phase 2 trial. Nat. Med. 2022, 28, 1630–1639. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  94. Nakai, T.; Muraki, S.; Bagarinao, E.; Miki, Y.; Takehara, Y.; Matsuo, K.; Kato, C.; Sakahara, H.; Isoda, H. Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter. Neuroimage 2004, 21, 251–260. [Google Scholar] [CrossRef] [PubMed]
  95. Doknic, M.; Gasic, V.; Stojanovic, M.; Pavlovic, S.; Marinkovic, S.; Miljic, D.; Pekic, S.; Manojlovic-Gacic, E.; Damjanovic, D.; Soldatovic, I.; et al. Hypopituitarism in five PROP1 mutation siblings: Long-lasting natural course and the effects of growth hormone replacement introduction in middle adulthood. Pituitary 2020, 23, 400–408. [Google Scholar] [CrossRef] [PubMed]
  96. Verburg, N.; Koopman, T.; Yaqub, M.M.; Hoekstra, O.S.; Lammertsma, A.A.; Barkhof, F.; Pouwels, P.J.W.; Reijneveld, J.C.; Heimans, J.J.; Rozemuller, A.J.M.; et al. Improved detection of diffuse glioma infiltration with imaging combinations: A diagnostic accuracy study. Neuro Oncol. 2020, 22, 412–422. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  97. Nishio, S.; Morioka, T.; Inamura, T.; Takeshita, I.; Fukui, M.; Sasaki, M.; Nakamura, K.; Wakisaka, S. Radiation-induced brain tumours: Potential late complications of radiation therapy for brain tumours. Acta Neurochir. 1998, 140, 763–770. [Google Scholar] [CrossRef] [PubMed]
  98. Ballester, L.Y.; Huse, J.T.; Tang, G.; Fuller, G.N. Molecular classification of adult diffuse gliomas: Conflicting IDH1/IDH2, ATRX, and 1p/19q results. Hum. Pathol. 2017, 69, 15–22. [Google Scholar] [CrossRef] [PubMed]
  99. Kim, H.; Lim, K.Y.; Park, J.W.; Kang, J.; Won, J.K.; Lee, K.; Shim, Y.; Park, C.K.; Kim, S.K.; Choi, S.H.; et al. Sporadic and Lynch syndrome-associated mismatch repair-deficient brain tumors. Lab. Investig. 2022, 102, 160–171. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  100. Prasanna, P.; Mitra, J.; Beig, N.; Nayate, A.; Patel, J.; Ghose, S.; Thawani, R.; Partovi, S.; Madabhushi, A.; Tiwari, P. Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study. Sci. Rep. 2019, 9, 1145. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  101. Shen, C.X.; Wu, J.F.; Zhao, W.; Cai, Z.W.; Cai, R.Z.; Chen, C.M. Primary spinal glioblastoma multiforme: A case report and review of the literature. Medicine 2017, 96, e6634. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  102. Petzold, J.; Severus, E.; Meyer, S.; Bauer, M.; Daubner, D.; Krex, D.; Juratli, T.A. Glioblastoma multiforme presenting as postpartum depression: A case report. J. Med. Case Rep. 2018, 12, 374. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  103. Yan, C.; Kong, X.; Yin, H.; Wang, Y.; He, H.; Zhang, H.; Gao, J.; Li, Y.; Ma, W. Glioblastoma multiforme in conus medullaris with intracranial metastasis after postoperative adjuvant therapy. Medicine 2017, 96, e6500. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  104. Faguer, R.; Tanguy, J.Y.; Rousseau, A.; Clavreul, A.; Menei, P. Early presentation of primary glioblastoma. Neurochirurgie 2014, 60, 188–193. [Google Scholar] [CrossRef] [PubMed]
  105. Karthigeyan, M.; Ahuja, C.K.; Chatterjee, D.; Salunke, P. Radiologic Dilemma in an Extra-Axial Petroclival Lesion: Low Grade versus High Grade. World Neurosurg. 2017, 100, 713.e1–713.e3. [Google Scholar] [CrossRef] [PubMed]
  106. Amini, A.; Schmidt, R.H.; Salzman, K.L.; Chin, S.S.; Couldwell, W.T. Glioblastoma multiforme of the pineal region. J. Neurooncol. 2006, 79, 307–314. [Google Scholar] [CrossRef] [PubMed]
  107. Kajitani, T.; Kanamori, M.; Saito, R.; Watanabe, Y.; Suzuki, H.; Watanabe, M.; Kure, S.; Tominaga, T. Three case reports of radiation-induced glioblastoma after complete remission of acute lymphoblastic leukemia. Brain Tumor Pathol. 2018, 35, 114–122. [Google Scholar] [CrossRef] [PubMed]
  108. Roemer, S.F.; Scheithauer, B.W.; Varnavas, G.G.; Lucchinetti, C.F. Tumefactive demyelination and glioblastoma: A rare collision lesion. Clin. Neuropathol. 2011, 30, 186–191. [Google Scholar] [CrossRef] [PubMed]
  109. Kiang, K.M.; Chan, A.A.; Leung, G.K. Secondary gliosarcoma: The clinicopathological features and the development of a patient-derived xenograft model of gliosarcoma. BMC Cancer 2021, 21, 265. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  110. Boikov, A.S.; Schweitzer, A.D.; Young, R.J.; Lavi, E.; Tsiouris, A.J.; Gupta, A. Glioblastoma-arteriovenous fistula complex: Imaging characteristics and treatment considerations. Clin. Imaging. 2014, 38, 187–190. [Google Scholar] [CrossRef] [PubMed]
  111. Takahashi, K.; Tsuda, M.; Kanno, H.; Murata, J.; Mahabir, R.; Ishida, Y.; Kimura, T.; Tanino, M.; Nishihara, H.; Nagashima, K.; et al. Differential diagnosis of small cell glioblastoma and anaplastic oligodendroglioma: A case report of an elderly man. Brain Tumor Pathol. 2014, 31, 118–123. [Google Scholar] [CrossRef] [PubMed]
  112. Colombo, M.C.; Giverso, C.; Faggiano, E.; Boffano, C.; Acerbi, F.; Ciarletta, P. Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model. PLoS ONE 2015, 10, e0132887. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  113. Nestler, U.; Schmidinger, A.; Schulz, C.; Huegens-Penzel, M.; Gamerdinger, U.A.; Koehler, A.; Kuchelmeister, K.W. Glioblastoma simultaneously present with meningioma--report of three cases. Zentralbl. Neurochir. 2007, 68, 145–150. [Google Scholar] [CrossRef] [PubMed]
  114. Park, D.; Lobbous, M.; Nabors, L.B.; Markert, J.M.; Kim, J. Undesired impact of iron supplement on MRI assessment of post-treatment glioblastoma. CNS Oncol. 2022, 11, CNS90. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  115. Gu, S.; Chakraborty, G.; Champley, K.; Alessio, A.M.; Claridge, J.; Rockne, R.; Muzi, M.; Krohn, K.A.; Spence, A.M.; Alvord, E.C., Jr.; et al. Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images. Math. Med. Biol. 2012, 29, 31–48. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  116. Soleman, J.; Roth, J.; Ram, Z.; Yalon, M.; Constantini, S. Malignant transformation of a conservatively managed incidental childhood cerebral mass lesion: Controversy regarding management paradigm. Childs Nerv. Syst. 2017, 33, 2169–2175. [Google Scholar] [CrossRef] [PubMed]
  117. Lrhezzioui, J.; Emery, E.; Chapon, F. Glioblastome frontal à cellules géantes: Tumeur radio-induite ? A propos d’un cas, avec revue de la littérature [Frontal giant cell glioblastoma: Radio-induced tumor? Case report and literature review]. Neurochirurgie 2007, 53, 486–490. (In French) [Google Scholar] [CrossRef] [PubMed]
  118. Cohen-Gadol, A.A.; DiLuna, M.L.; Bannykh, S.I.; Piepmeier, J.M.; Spencer, D.D. Non-enhancing de novo glioblastoma: Report of two cases. Neurosurg. Rev. 2004, 27, 281–285. [Google Scholar] [CrossRef] [PubMed]
  119. Lam, W.W.; Ng, D.C.; Wong, W.Y.; Ong, S.C.; Yu, S.W.; See, S.J. Promising role of [18F] fluorocholine PET/CT vs [18F] fluorodeoxyglucose PET/CT in primary brain tumors-early experience. Clin. Neurol. Neurosurg. 2011, 113, 156–161. [Google Scholar] [CrossRef] [PubMed]
  120. Wu, W.C.; Chen, C.Y.; Chung, H.W.; Juan, C.J.; Hsueh, C.J.; Gao, H.W. Discrepant MR spectroscopic and perfusion imaging results in a case of malignant transformation of cerebral glioma. Am. J. Neuroradiol. 2002, 23, 1775–1778. [Google Scholar] [PubMed] [PubMed Central]
  121. Franco, C.M.; Malheiros, S.M.; Nogueira, R.G.; Batista, M.A.; Santos, A.J.; Abdala, N.; Stávale, J.N.; Ferraz, F.A.; Gabbai, A.A. Gliomas múltiplos: Casos ilustrativos de quatro formas de apresentação [Multiple gliomas. Illustrative cases of 4 different presentations]. Arq. Neuropsiquiatr. 2000, 58, 150–156. [Google Scholar] [CrossRef] [PubMed]
  122. Li, Y.; Zeng, T.; Li, B.; Luo, B.; Li, Z. Clinicopathologic characteristics of unusual rhabdoid glioblastoma. Zhonghua Bing Li Xue Za Zhi 2015, 44, 772–777. (In Chinese) [Google Scholar] [PubMed]
  123. Dilber, B.; Havalı, C.; Eroglu, N.; Aydın, K.; Şahin, S.; Cansu, A. Cerebral neoplasm in L-2-hydroxyglutaric aciduria: Two different presentations. Childs Nerv. Syst. 2020, 36, 1545–1548. [Google Scholar] [CrossRef] [PubMed]
  124. Ishikawa, M.; Ota, Y.; Nagai, M.; Kusaka, G.; Tanaka, Y.; Naritaka, H. Ultrasonography Monitoring with Superb Microvascular Imaging Technique in Brain Tumor Surgery. World Neurosurg. 2017, 97, 749.e11–749.e20. [Google Scholar] [CrossRef] [PubMed]
  125. Roetzer, T.; Leskovar, K.; Peter, N.; Furtner, J.; Muck, M.; Augustin, M.; Lichtenegger, A.; Nowosielski, M.; Hainfellner, J.A.; Baumann, B.; et al. Evaluating cellularity and structural connectivity on whole brain slides using a custom-made digital pathology pipeline. J. Neurosci. Methods 2019, 311, 215–221. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  126. Nguyen, H.S.; Milbach, N.; Hurrell, S.L.; Cochran, E.; Connelly, J.; Bovi, J.A.; Schultz, C.J.; Mueller, W.M.; Rand, S.D.; Schmainda, K.M.; et al. Progressing Bevacizumab-Induced Diffusion Restriction Is Associated with Coagulative Necrosis Surrounded by Viable Tumor and Decreased Overall Survival in Patients with Recurrent Glioblastoma. Am. J. Neuroradiol. 2016, 37, 2201–2208. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  127. Schiff, D.; Rosenblum, M.K. Herpes simplex encephalitis (HSE) and the immunocompromised: A clinical and autopsy study of HSE in the settings of cancer and human immunodeficiency virus-type 1 infection. Hum. Pathol. 1998, 29, 215–222. [Google Scholar] [CrossRef] [PubMed]
  128. Ellingson, B.M.; Cloughesy, T.F.; Lai, A.; Nghiemphu, P.L.; Liau, L.M.; Pope, W.B. High order diffusion tensor imaging in human glioblastoma. Acad. Radiol. 2011, 18, 947–954. [Google Scholar] [CrossRef] [PubMed]
  129. Jallo, G.I.; Friedlander, D.R.; Kelly, P.J.; Wisoff, J.H.; Grumet, M.; Zagzag, D. Tenascin-C expression in the cyst wall and fluid of human brain tumors correlates with angiogenesis. Neurosurgery 1997, 41, 1052–1059. [Google Scholar] [CrossRef] [PubMed]
  130. Maslehaty, H.; Cordovi, S.; Hefti, M. Symptomatic spinal metastases of intracranial glioblastoma: Clinical characteristics and pathomechanism relating to GFAP expression. J. Neurooncol. 2011, 101, 329–333. [Google Scholar] [CrossRef] [PubMed]
  131. Tomaszewski, K.A.; Henry, B.M.; Kumar Ramakrishnan, P.; Roy, J.; Vikse, J.; Loukas, M.; Tubbs, R.S.; Walocha, J.A. Development of the Anatomical Quality Assurance (AQUA) checklist: Guidelines for reporting original anatomical studies. Clin. Anat. 2017, 30, 14–20. [Google Scholar] [CrossRef] [PubMed]
  132. Winkelmann, A. Guidelines for reporting original anatomical studies-Quality and ethics. Clin. Anat. 2017, 30, 425–426. [Google Scholar] [CrossRef] [PubMed]
  133. Mishra, P.; Singh, U.; Pandey, C.M.; Mishra, P.; Pandey, G. Application of student’s t-test, analysis of variance, and covariance. Ann. Card. Anaesth. 2019, 22, 407–411. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  134. Zarnett, O.J.; Sahgal, A.; Gosio, J.; Perry, J.; Berger, M.S.; Chang, S.; Das, S. Treatment of elderly patients with glioblastoma: A systematic evidence-based analysis. JAMA Neurol. 2015, 72, 589–596. [Google Scholar] [CrossRef] [PubMed]
  135. Corr, F.; Grimm, D.; Saß, B.; Pojskić, M.; Bartsch, J.W.; Carl, B.; Nimsky, C.; Bopp, M.H.A. Radiogenomic Predictors of Recurrence in Glioblastoma-A Systematic Review. J. Pers. Med. 2022, 12, 402. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
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