Rongeur

Rongeur

A rongeur is a strongly constructed instrument with a sharp-edged, scoop-shaped tip, used for gouging out bone. Rongeur is a French word that means rodent or ‘gnawer’.

A rongeur can be used to open a window in bone, often in the skull. It is used in neurosurgery, podiatric surgery, and orthopedic surgery to expose areas for operation.


The Stille-Luer Horsley and Leksell rongeur point straight from the handle, while the duckbill points to the side.


A common example of a surgical rongeur is the Kerrison rongeur, in which its first design was created by Dr. Robert Masters Kerrison (1776–1847), an English physician, but it took more than 100 years before the Kerrison rongeur was modified and took its current form.

Micro straight pituitary rongeur

Hemorrhagic transformation

Hemorrhagic transformation

Large areas of hemorrhagic transformation within an ischemic infarct may be more indicative of cardiogenic brain embolism (CBE) due to thrombolysis of the clot and reperfusion of infarcted brain with the subsequent hemorrhagic conversion. Hemorrhagic transformation most often occurs within 48 hrs of a CBE stroke, and is more common with larger strokes.


Intraarterial thrombolysis within 6 hours of stroke onset may increase recanalization rates to 37–100% and clinical improvement to 53–94% without a significant increase in the hemorrhagic transformation when compared with intravenous thrombolytic therapy alone.

see also Spontaneous Intracerebral Hemorrhage Risk Factors.


Identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction.


Decompressive craniectomy for a malignant stroke, after reperfusion, corresponding to an endovascular thrombectomy failure, increases the risk of severe hemorrhagic transformations in a ischemic stroke model in mice. This result support the need of clinical studies to evaluate the added value of DC at the era of endovascular thrombectomy 1).

The purpose of a study of Wang et al. was to develop and validate a model to predict a patient’s risk of Hemorrhagic transformation within 30 days of initial ischemic stroke.

They utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia.

In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78.

A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke 2).

Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis.


1)

Borha A, Lebrun F, Touzé E, Emery E, Vivien D, Gaberel T. Impact of Decompressive Craniectomy on Hemorrhagic Transformation in Malignant Ischemic Stroke in Mice. Stroke. 2022 Dec 7. doi: 10.1161/STROKEAHA.122.041365. Epub ahead of print. PMID: 36475467.
2)

Wang Q, Reps JM, Kostka KF, Ryan PB, Zou Y, Voss EA, Rijnbeek PR, Chen R, Rao GA, Morgan Stewart H, Williams AE, Williams RD, Van Zandt M, Falconer T, Fernandez-Chas M, Vashisht R, Pfohl SR, Shah NH, Kasthurirathne SN, You SC, Jiang Q, Reich C, Zhou Y. Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network. PLoS One. 2020 Jan 7;15(1):e0226718. doi: 10.1371/journal.pone.0226718. eCollection 2020. PubMed PMID: 31910437.

Glioblastoma

Glioblastoma

J.Sales-Llopis

Neurosurgery Service, Alicante University General Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL – FISABIO Foundation), Alicante, Spain.

While glioblastoma was historically classified as isocitrate dehydrogenase (IDH)-wildtype and IDH-mutant groups, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) and the World Health Organization Classification of Tumors of the Central Nervous System 2021 clearly updated the nomenclature to reflect glioblastoma to be compatible with wildtype IDH status only. Therefore, glioblastoma is now defined as “a diffuse, astrocytic glioma that is IDH-wildtype and H3-wildtype and has one or more of the following histological or genetic features: microvascular proliferationnecrosisTERT promoter mutationEpidermal growth factor receptor gene amplification, +7/-10 chromosome copy-number changes (CNS WHO grade 4) 1).

see Glioblastoma epidemiology.

Prior malignancies in patients harboring glioblastoma

Patients who develop Glioblastoma following a prior malignancy constitute ~8% of patients with Glioblastoma. Despite significant molecular differences these two cohorts appear to have a similar overall prognosis and clinical course. Thus, whether or not a patient harbors a malignancy prior to diagnosis of Glioblastoma should not exclude him or her from aggressive treatment or for consideration of novel investigational therapies 2).

Genome-wide profiling studies have shown remarkable genomic diversity among glioblastomas.

Molecular studies have helped identify at least 3 different pathways in the development of glioblastomas.

● 1st pathway: dysregulation of growth factor signaling through amplification and mutational activation of receptor tyrosine kinase (RTK) genes. RTKs are transmembrane proteins that act as receptors for an epidermal growth factor (EGF), vascular endothelial growth factor (VEGF) & platelet-derived growth factor (PDGF). They can also act as receptors for cytokines, hormones, and other signaling pathways

● 2nd pathway: activation of the Phosphoinositide 3 kinase (PI3K)/AKT/mTOR, which is an intracellular signaling pathway that is essential in regulating cell survival

● 3rd pathway: inactivation of the p53 and retinoblastoma (Rb) tumor suppressor pathways

Glioblastomas are intrinsic brain tumors thought to originate from a neuroglial stem or progenitor cells. More than 90% of glioblastomas are isocitrate dehydrogenase (IDH)-wildtype tumors. Incidence increases with age, males are more often affected. Beyond rare instances of genetic predisposition and irradiation exposure, there are no known glioblastoma risk factors.

Vessels with different microcirculation patterns are required for glioblastoma (Glioblastoma) growth. However, details of the microcirculation patterns in Glioblastoma remain unclear.

Mei et al. examined the microcirculation patterns of Glioblastoma and analyzed their roles in patient prognosis together with two well-known GMB prognosis factors (O6 -methylguanine DNA methyltransferase [MGMT] promoter methylation status and isocitrate dehydrogenase [IDH] mutations).

Eighty Glioblastoma clinical specimens were collected from patients diagnosed between January 2000 and December 2012. The microcirculation patterns, including endothelium-dependent vessels (EDVs), extracellular matrix-dependent vessels (ECMDVs), Glioblastoma cell-derived vessels (GDVs), and mosaic vessels (MVs), were evaluated by immunohistochemistry (IHC) and immunofluorescence (IF) staining in both Glioblastoma clinical specimens and xenograft tissues. Vascular density assessments and three-dimensional reconstruction were performed. MGMT promoter methylation status was determined by methylation-specific PCR, and IDH1/2 mutations were detected by Sanger sequencing. The relationship between the microcirculation patterns and the patient prognosis was analyzed by the Kaplan-Meier method.

All 4 microcirculation patterns were observed in both Glioblastoma clinical specimens and xenograft tissues. EDVs was detected in all tissue samples, while the other three patterns were observed in a small number of tissue samples (ECMDVs in 27.5%, GDVs in 43.8%, and MVs in 52.5% tissue samples). GDV-positive patients had a median survival of 9.56 months versus 13.60 months for GDV-negative patients (P = 0.015). In MGMT promoter-methylated cohort, GDV-positive patients had a median survival of 6.76 months versus 14.23 months for GDV-negative patients (P = 0.022).

GDVs might be a negative predictor for the survival of Glioblastoma patients, even in those with MGMT promoter methylation 3).

It generally presents with epilepsycognitive declineheadachedysphasia, or progressive hemiparesis4).

Seizures as the presenting symptom of glioblastoma predicted longer survival in adults younger than 60 years. The IDH1 R132H mutation and p53 overexpression (>40%) were associated with seizures at presentation. Seizures showed no relationship with the tumor size or proliferation parameters 5).


1)

Chen J, Han P, Dahiya S. Glioblastoma: Changing concepts in the WHO CNS5 classification. Indian J Pathol Microbiol. 2022 May;65(Supplement):S24-S32. doi: 10.4103/ijpm.ijpm_1109_21. PMID: 35562131.
2)

Zacharia BE, DiStefano N, Mader MM, Chohan MO, Ogilvie S, Brennan C, Gutin P, Tabar V. Prior malignancies in patients harboring glioblastoma: an institutional case-study of 2164 patients. J Neurooncol. 2017 May 27. doi: 10.1007/s11060-017-2512-y. [Epub ahead of print] Review. PubMed PMID: 28551847.
3)

Mei X, Chen YS, Zhang QP, Chen FR, Xi SY, Long YK, Zhang J, Cai HP, Ke C, Wang J, Chen ZP. Association between glioblastoma cell-derived vessels and poor prognosis of the patients. Cancer Commun (Lond). 2020 May 2. doi: 10.1002/cac2.12026. [Epub ahead of print] PubMed PMID: 32359215.
4)

Thomas DGT,Graham DI, McKeran RO,Thomas DGT. The clinical study of gliomas. In: Brain tumours: scientific basis, clinical investigation and current therapy. In: Thomas DGT, Graham DI eds. London: Butterworths, 1980:194–230.
5)

Toledo M, Sarria-Estrada S, Quintana M, Maldonado X, Martinez-Ricarte F, Rodon J, Auger C, Aizpurua M, Salas-Puig J, Santamarina E, Martinez-Saez E. Epileptic features and survival in glioblastomas presenting with seizures. Epilepsy Res. 2016 Dec 26;130:1-6. doi: 10.1016/j.eplepsyres.2016.12.013. [Epub ahead of print] PubMed PMID: 28073027.
WhatsApp WhatsApp us
%d bloggers like this: