Glioma outcome

Glioma outcome

In order to set up a reliable prediction system for the tumor grade and glioma outcome, Li et al. clarified the complicated crosstalk of Annexin A2 (ANXA2) with Glypican 1 (GPC1) and demonstrate whether combined indexes of ANXA2 and GPC1 could improve the prognostic evaluation for glioma patients. Li et al. found that ANXA2-induced glioma cell proliferation in a c-Myc-dependent manner. ANXA2 increased the expression of GPC1 via c-Myc and the upregulated GPC1 further promoted the c-Myc level, forming a positive feedback loop, which eventually led to enhanced proliferation of glioma cells. Both mRNA and protein levels of ANXA2 were upregulated in glioma tissues and coincided with the overexpression of GPC1. Besides, they utilized tissue microarrays (TMAs) and immunohistochemistry to demonstrate that glioma patients with both high expressions of ANXA2 and GPC1 tended to have a higher rate of tumor recurrence and shorter overall survival (OS). In conclusion, the overexpression of ANXA2 promotes proliferation of glioma cells by forming a GPC1/c-Myc positive feedback loop, and ANXA2 together with its downstream target GPC1 could be a potential “combination biomarker” for predicting the prognosis of glioma patients 1).

see Glioma Quality of Life.


The ability to resume professional activities following brain tumor surgery is an important patient-oriented outcome parameter. Senft et al. found that the majority of patients with gliomas were able to return to work following surgical and adjuvant treatment. Preservation of neurological function is of utmost relevance for individual patients quality of life 2)


Patients with IDH and TERTp glioma mutations have the best prognosis, and only IDH mutation patients and only TERTp mutation patients have the worst prognosis. Moreover, the molecular classification of gliomas by mutations of IDH and TERTp is not suitable for pediatric patients 3).

Also the O6 methylguanine DNA methyltransferase (MGMT) promoter methylation status seem to be the most important predictors of survival.


Infiltrative gliomas invade the brain, relentlessly recur, transform into higher-grade gliomas, and are invariably lethal 4) 5) 6). , mostly due to the poor glioblastoma outcome (Grade IV glioma).

Gliomas are considered incurable due to recurrence as demonstrated in a series of five patients who underwent hemispherectomies in 1928 7).

The prognosis improves as the amount of glioma removed increases 8) 9) 10) 11) 12).

Older age (>40 years), high pathological grade, invasion of the corpus callosum and high levels of Ki67 expression were risk factors associated with the intracranial dissemination of gliomas 13).


1)

Li X, Nie S, Lv Z, Ma L, Song Y, Hu Z, Hu X, Liu Z, Zhou G, Dai Z, Song T, Liu J, Wang S. Overexpression of Annexin A2 promotes proliferation by forming a Glypican 1/c-Myc positive feedback loop: prognostic significance in human glioma. Cell Death Dis. 2021 Mar 12;12(3):261. doi: 10.1038/s41419-021-03547-5. PMID: 33712571.
2)

Senft C, Behrens M, Lortz I, Wenger K, Filipski K, Seifert V, Forster MT. The ability to return to work: a patient-centered outcome parameter following glioma surgery. J Neurooncol. 2020 Sep 22. doi: 10.1007/s11060-020-03609-2. Epub ahead of print. PMID: 32960402.
3)

Qu CX, Ji HM, Shi XC, Bi H, Zhai LQ, Han DW. Characteristics of the isocitrate dehydrogenase gene and telomerase reverse transcriptase promoter mutations in gliomas in Chinese patients. Brain Behav. 2020 Mar 8:e01583. doi: 10.1002/brb3.1583. [Epub ahead of print] PubMed PMID: 32146731.
4)

DeAngelis LM (2001) Brain tumors. N Engl J Med 344:114–123.
5)

Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507.
6)

Behin A, Hoang-Xuan K, Carpentier AF, et al.(2003) Primary brain tumours in adults. Lancet 361:323–331.
7)

Dandy WE. Removal of right cerebral hemisphere for certain tumors with hemiplegia: preliminary report. JAMA. 1928;90:823–825.
8)

Chan-Seng E, Moritz-Gasser S, Duffau H. Awake mapping for low-grade gliomas involving the left sagittal stratum: Anatomofunctional and surgical considerations. J Neurosurg. 2014;120:1069–1077. doi: 10.3171/2014.1.JNS132015.
9)

Sanai N, Berger MS. Glioma extent of resection and its impact on patient outcome. Neurosurgery. 2008;62:753–764. doi: 10.1227/01.neu.0000318159.21731.cf.
10)

Han SJ, Sughrue ME. The rise and fall of ‘biopsy and radiate’: A history of surgical nihilism in glioma treatment. Neurosurg Clin N Am. 2012;23:207–214. doi: 10.1016/j.nec.2012.02.002.
11)

Giussani C, Roux FE, Ojemann J, Sganzerla EP, Pirillo D, Papagno C. Is preoperative functional magnetic resonance imaging reliable for language areas mapping in brain tumor surgery? Review of language functional magnetic resonance imaging and direct cortical stimulation correlation studies. Neurosurgery. 2010;66:113–120. doi: 10.1227/01.NEU.0000360392.15450.C9.
12)

Choi BD, Mehta AI, Batich KA, Friedman AH, Sampson JH. The use of motor mapping to aid resection of eloquent gliomas. Neurosurg Clin N Am. 2012;23:215–225. doi: 10.1016/j.nec.2012.01.013.
13)

Cai X, Qin JJ, Hao SY, Li H, Zeng C, Sun SJ, Yu LB, Gao ZX, Xie J. Clinical characteristics associated with the intracranial dissemination of gliomas. Clin Neurol Neurosurg. 2018 Feb 1;166:141-146. doi: 10.1016/j.clineuro.2018.01.038. [Epub ahead of print] PubMed PMID: 29427894.

Skull base meningioma outcome

Skull base meningioma outcome

Peritumoral edema (PTE) in skull base meningiomas correlates to the absence of an arachnoid plane and difference in outcome.

A subset of benign (WHO grade I) skull base meningiomas shows early progression/recurrence (P/R) in the first years after surgical resection.


Though various predictors of adverse postoperative outcomes among meningioma patients have been established, research has yet to develop a method for consolidating these findings to allow for predictions of adverse healthcare outcomes for patients diagnosed with skull base meningiomas.


The objective of a study was to develop three predictive algorithms that can be used to estimate an individual patient’s probability of extended length of stay (LOS), experiencing a nonroutine discharge disposition, or incurring high hospital charges following surgical resection of a skull base meningioma.

The study utilized data from patients who underwent surgical resection for skull base meningiomas at a single academic institution between 2017-2019. Multivariate logistic regression analysis was used to predict extended LOS, nonroutine discharge, and high hospital charges, and 2000 bootstrapped samples were used to calculate an optimism-corrected c-statistic. The Hosmer-Lemeshow test was used to assess model calibration, and p<0.05 was considered statistically significant.

A total of 245 patients were included in our analysis. Our cohort was majority female (77.6%) and Caucasian (62.4%). Our models predicting extended LOS, nonroutine discharge, and high hospital charges had optimism-corrected c-statistics of 0.768, 0.784, and 0.783, respectively. All models demonstrated adequate calibration (p>0.05), and were deployed an open-access, online calculator: https://neurooncsurgery3.shinyapps.io/high_value_skull_base_calc/.

Following external validation, these predictive models have the potential to aid clinicians in providing patients with individualized risk-estimation for healthcare outcomes following meningioma surgery 1).


Ko et al. retrospectively investigated the preoperative CT and MR imaging features for the prediction of P/R in skull base meningiomas, with emphasis on quantitative ADC values. Only patients had postoperative MRI follow-ups for more than 1 year (at least every 6 months) were included. From October 2006 to December 2015, total 73 patients diagnosed with benign (WHO grade I) skull base meningiomas were included (median follow-up time 41 months), and 17 (23.3%) patients had P/R (median time to P/R 28 months). Skull base meningiomas with spheno-orbital location, adjacent bone invasion, high DWI, and lower ADC value/ratio were significantly associated with P/R (P < 0.05). The cut-off points of ADC value and ADC ratio for prediction of P/R are 0.83 × 10- 3 mm2/s and 1.09 respectively, with excellent area under curve (AUC) values (0.86 and 0.91) (P < 0.05). In multivariate logistic regression, low ADC values (< 0.83 × 10- 3 mm2/s) and adjacent bone invasion are high-risk factors of P/R (P < 0.05), with odds ratios of 31.53 and 17.59 respectively. The preoperative CT and MRI features for prediction of P/R offered clinically vital information for the planning of treatment in skull base meningiomas 2).


1)

Jimenez AE, Khalafallah AM, Lam S, Horowitz MA, Azmeh O, Rakovec M, Patel P, Porras JL, Mukherjee D. Predicting High-Value Care Outcomes Following Surgery for Skull Base Meningiomas. World Neurosurg. 2021 Feb 7:S1878-8750(21)00188-1. doi: 10.1016/j.wneu.2021.02.007. Epub ahead of print. PMID: 33567369.
2)

Ko CC, Lim SW, Chen TY, Chen JH, Li CF, Shiue YL. Prediction of progression in skull base meningiomas: additional benefits of apparent diffusion coefficient value. J Neurooncol. 2018 Jan 20. doi: 10.1007/s11060-018-2769-9. [Epub ahead of print] PubMed PMID: 29353434.

Severe traumatic brain injury outcome

Severe traumatic brain injury outcome

Younger age, modified Fisher scale (mFS) score, and Intracerebral hemorrhage volume are associated with Intracranial pressure elevation in patients with a severe traumatic brain injury. Imaging features may stratify patients by their risk of subsequent ICP elevation 1).


There has been a secular trend towards reduced incidence of severe traumatic brain injury in the first world, driven by public health interventions such as seatbelt legislation, helmet use, and workplace health and safety regulations. This has paralleled improved outcomes following TBI delivered in a large part by the widespread establishment of specialised neurointensive care 2).

The impact of a moderate to severe brain injury depends on the following:

Severity of initial injury

Rate/completeness of physiological recovery

Functions affected

Meaning of dysfunction to the individual

Resources available to aid recovery

Areas of function not affected by TBI

see Effect of trauma center designation in severe traumatic brain injury outcome


Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation severe traumatic brain injury guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. Lazaridis et al., and others, have developed predictive models based on machine learning from continuous time series of intracranial pressure and partial pressure of brain tissue oxygen. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In a article, Lazaridis et al., review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithms 3).


Determining the prognostic significance of clinical factors for patients with severe head injury can lead to an improved understanding of the pathophysiology of head injury and to improvement in therapy. A technique known as the sequential Bayes method has been used previously for the purpose of prognosis. The application of this method assumes that prognostic factors are statistically independent. It is now known that they are not. Violation of the assumption of independence may produce errors in determining prognosis. As an alternative technique for predicting the outcome of patients with severe head injury, a logistic regression model is proposed. A preliminary evaluation of the method using data from 115 patients with head injury shows the feasibility of using early data to predict outcome accurately and of being able to rank input variables in order of their prognostc significance. 4).


A prospective and consecutive series of 225 patients with severe head injuries who were managed in a uniform way was analyzed to relate outcome to several clinical variables. Good recovery or moderate disability were achieved by 56% of the patients, 10% remained severely disabled or vegetative, and 34% died. Factors important in predicting a poor outcome included the presence of intracranial hematoma, increasing age, motor impairment, impaired or absent eye movements or pupillary light reflexes, early hypotension, hypoxemia or hypercarbia, and raised intracranial pressure over 20 mm Hg despite artificial ventilation. Most of these predictive factors were assessed on admission, but a subset of 158 patients was identified in whom coma was present on admission and was known to have persisted at least until the following day. Although the mortality in this subset (40%) was higher than in the total series, it was lower than in several comparable reported series of patients with severe head injury. Predictive correlations were equally strong in the entire series and in the subset of 158 patients with coma. A plea is made for inclusion in the definition of “severe head injury” of all patients who do not obey commands or utter recognizable words on admission to the hospital after early resuscitation 5).


Survival rate of isolated severe TBI patients who required an emergent neurosurgical intervention could be time dependent. These patients might benefit from expedited process (computed tomographic scan, neurosurgical consultation, etc.) to shorten the time to surgical intervention 6).

The impact of a moderate to severe brain injury can include:

Cognitive deficits including difficulties with:

Attention Concentration Distractibility Memory Speed of Processing Confusion Perseveration Impulsiveness Language Processing “Executive functions” Speech and Language

not understanding the spoken word (receptive aphasia) difficulty speaking and being understood (expressive aphasia) slurred speech speaking very fast or very slow problems reading problems writing Sensory

difficulties with interpretation of touch, temperature, movement, limb position and fine discrimination Perceptual

the integration or patterning of sensory impressions into psychologically meaningful data Vision

partial or total loss of vision weakness of eye muscles and double vision (diplopia) blurred vision problems judging distance involuntary eye movements (nystagmus) intolerance of light (photophobia) Hearing

decrease or loss of hearing ringing in the ears (tinnitus) increased sensitivity to sounds Smell

loss or diminished sense of smell (anosmia) Taste

loss or diminished sense of taste Seizures

the convulsions associated with epilepsy that can be several types and can involve disruption in consciousness, sensory perception, or motor movements Physical Changes

Physical paralysis/spasticity Chronic pain Control of bowel and bladder Sleep disorders Loss of stamina Appetite changes Regulation of body temperature Menstrual difficulties Social-Emotional

Dependent behaviors Emotional ability Lack of motivation Irritability Aggression Depression Disinhibition Denial/lack of awareness


Both single predictors from early clinical examination and multiple hospitalization variables/parameters can be used to determine the long-term prognosis of TBI. Predictive models like the IMPACT or CRASH prognosis calculator (based on large sample sizes) can predict mortality and unfavorable outcomes. Moreover, imaging techniques like MRI (Magnetic Resonance Imaging) can also predict consciousness recovery and mental recovery in severe TBI, while biomarkers associated with stress correlate with, and hence can be used to predict, severity and mortality. All predictors have limitations in clinical application. Further studies comparing different predictors and models are required to resolve limitations of current predictors 7).


1)

Murray NM, Wolman DN, Mlynash M, Threlkeld ZD, Christensen S, Heit JJ, Harris OA, Hirsch KG. Early Head Computed Tomography Abnormalities Associated with Elevated Intracranial Pressure in Severe Traumatic Brain Injury. J Neuroimaging. 2020 Nov 4. doi: 10.1111/jon.12799. Epub ahead of print. PMID: 33146933.
2)

Khellaf A, Khan DZ, Helmy A. Recent advances in traumatic brain injury. J Neurol. 2019 Sep 28. doi: 10.1007/s00415-019-09541-4. [Epub ahead of print] PubMed PMID: 31563989.
3)

Lazaridis C, Rusin CG, Robertson CS. Secondary Brain Injury: Predicting and Preventing Insults. Neuropharmacology. 2018 Jun 6. pii: S0028-3908(18)30279-X. doi: 10.1016/j.neuropharm.2018.06.005. [Epub ahead of print] Review. PubMed PMID: 29885419.
4)

Stablein DM, Miller JD, Choi SC, Becker DP. Statistical methods for determining prognosis in severe head injury. Neurosurgery. 1980 Mar;6(3):243-8. PubMed PMID: 6770283.
5)

Miller JD, Butterworth JF, Gudeman SK, Faulkner JE, Choi SC, Selhorst JB, Harbison JW, Lutz HA, Young HF, Becker DP. Further experience in the management of severe head injury. J Neurosurg. 1981 Mar;54(3):289-99. PubMed PMID: 7463128.
6)

Matsushima K, Inaba K, Siboni S, Skiada D, Strumwasser AM, Magee GA, Sung GY, Benjaminm ER, Lam L, Demetriades D. Emergent operation for isolated severe traumatic brain injury: Does time matter? J Trauma Acute Care Surg. 2015 Aug 28. [Epub ahead of print] PubMed PMID: 26317818.
7)

Gao L, Wu X. Prediction of clinical outcome in severe traumatic brain injury. Front Biosci (Landmark Ed). 2015 Jan 1;20:763-771. PubMed PMID: 25553477.
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