Automated algorithm aided capacity and confidence boost in surgical decision-making training for inferior clivus.

Objective: To assess the impact of automated algorithms on the trainees' decision-making capacity and confidence for individualized surgical planning. Methods: At Chinese PLA General Hospital, trainees were enrolled to undergo decision-making capacity and confidence training through three alternative visual tasks of the inferior clivus model formed from an automated algorithm and given consecutively in three exemplars. The rationale of automated decision-making was used to instruct each trainee. Results: Following automated decision-making calculation in 50 skull base models, we screened out three optimal plans, infra-tubercle approach (ITA), trans-tubercle approach (TTA), and supra-tubercle approach (STA) for 41 (82.00%), 8 (16.00%), and 1 (2.00%) subject, respectively. From September 1, 2023, through November 17, 2023, 62 trainees (median age [range]: 27 [26-28]; 28 [45.16%] female; 25 [40.32%] neurosurgeons) made a decision among the three plans for the three typical models (ITA, TTA, and STA exemplars). The confidence ratings had fine test-retest reliability (Spearman's rho: 0.979; 95% CI: 0.970 to 0.988) and criterion validity with time spent (Spearman's rho: -0.954; 95%CI: -0.963 to -0.945). Following instruction of automated decision-making, time spent (initial test: 24.02 vs. 7.13 in ITA; 30.24 vs. 7.06 in TTA; 34.21 vs. 12.82 in STA) and total hits (initial test: 30 vs. 16 in ITA; 37 vs. 17 in TTA; 42 vs. 28 in STA) reduced significantly; confidence ratings (initial test: 2 vs. 4 in ITA; 2 vs. 4 in TTA; 1 vs. 3 in STA) increased correspondingly. Statistically significant differences (P < 0.05) were observed for the above comparisons. Conclusions: The education tool generated by automated decision-making considers surgical freedom and injury risk for the individualized risk-benefit assessment, which may provide explicit information to increase trainees' decision-making capacity and confidence.

MatBED_B&C: A 3-dimensional biologically effective dose analytic approach for the retrospective study of gamma knife radiosurgery in a B&C model.

The biological effect of irradiation is not solely determined by the physical dose. Gamma knife radiosurgery may be influenced by dose rate, beam-on-time, numbers of iso-centers, the gap between the individual iso-centers, and the dose‒response of various tissues. The biologically effective dose (BED) for radiosurgery considers these issues. Millions of patients treated with Models B and C provide a vast database to mine BED-related information. This research aims to develop MatBED_B&C, a 3-dimensional (3D) BED analytic approach, to generate a BED for individual voxels in the calculation matrix with related parameters extracted from Gammaplan. This approach calculates the distribution profiles of the BED in radiosurgical targets and organs at risk. A BED calculated on a voxel-by-voxel basis can be used to show the 3D morphology of the iso-BED surface and visualize the BED spatial distribution in the target. A 200 × 200 × 200 matrix can cover a greater range of the organ at risk. The BED calculated by MatBED_B&C can also be used to form BED-volume histograms to generate plan quality metrics, which will be studied in a retrospective study of gamma knife radiosurgery to guide future BED planning.•We develop MatBED_B&C to calculate the 3D BED in radiosurgical targets.•The BED of MatBED_B&C can visualize the BED spatial distribution profiles.•The BED of MatBED_B&C will generate plan quality metrics studied in a retrospective study.

Phosphorylation of Oligopeptides: Design of Ultra-Hydrophilic Zwitterionic Peptides for Anti-Fouling Detection of Nucleic Acids in Saliva.

The construction of low-fouling biosensors for assaying biomarkers in complex biological samples remains a challenge, and the key limitation is the lack of effective anti-fouling materials. Inspired by the biomimetic process of protein phosphorylation, we herein designed a new phosphorylated peptide modified with the dihydrogen phosphate (-PO4H2) group, which significantly increased the hydrophilicity and anti-fouling capability of the peptide when compared with natural and normal peptides. Molecular simulation (MS) illustrated that, compared with the -COOH and -NH2 groups, the -PO4H2 group formed the most numbers of hydrogen bonds and stronger hydrogen bonds with water molecules. As a result, the PO4H2-oligopeptide was proved by MS to be able to attract the greatest number of water molecules, so as to form a compact layer of H2O to resist further adsorption of nonspecific biomolecules. The modification of electrodes with the designed PO4H2-oligopeptides, in addition to the adoption of neutral peptide nucleic acids (PNAs) as the sensing probes, ensured the fabrication of anti-fouling electrochemical biosensors capable of detecting nucleic acids in complex saliva. The constructed anti-fouling biosensor was able to detect the nucleic acid of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in undiluted saliva, with a wide linear response range (0.01 pM-0.01 µM) and a low limit of detection (LOD) of 3.4 fM (S/N = 3). The phosphorylation of oligopeptides offers an effective strategy to designing ultra-hydrophilic peptides suitable for the construction of promising anti-biofouling biosensors and bioelectronics.

Conservation of pyramidal tract in radiosurgery for brain metastases of lung adenocarcinoma: Three-dimensional analysis of biologically effective dose.

BACKGROUND: Gamma knife surgery (GKS) for brain metastases (BMs) adjacent to the pyramidal tract (PT) is still a challenge to conduct. PT visualization and biologically effective dose (BED) calculation on a voxel-by-voxel basis may provide data to establish clinically safe values. We aimed to assess the relationship of parameters extracted from the BED-volume histogram with outcomes of PT after GKS-treating target (adjacent BM of lung adenocarcinoma). METHODS: We formed BED-volume histograms for 672 BMs in a retrospective cohort, using 3-dimensional (3D) coordinate values of PT, target, and each iso-centre to calculate the 3D BED distribution in a 200 × 200 × 200 matrix. PT conservation failure (PTCF) was judged clinically and radiologically and classified as lesion progression and radionecrosis. Cox proportional hazards models were used to analyse 3D BED parameters. Internal validation of models was performed by bootstrapping. RESULTS: There were 116 (17.3 %) subjects with PTCF in the cohort, of which 74 (11.0 %) and 42 (6.3 %) were caused by lesion progression and radionecrosis, respectively. Multivariate analysis showed that DLesion_min BED and DLesion_90% BED significantly predicted lesion progression (P <.001). DPT_Max BED and VPT_ BED40 significantly predicted radionecrosis (P <.001). The model predicting PTCF showed fair discrimination and calibration of DLesion_min BED + DLesion_90% BED and DPT_Max BED + VPT_ BED40. CONCLUSIONS: The conservation of PT in GKS for BMs of lung adenocarcinoma depends on the combination of PT-tolerated BED and target effective control BED. Therefore, a BED-volume histogram with a 3D BED algorithm is proposed to assess plan quality.

WES data from 286 diffuse gliomas under the 2021 WHO Classification of Tumors of the Central Nervous System.

Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient's clinical and pathological information and used it to annotate their genetic data. All patients were diagnosed and classified by neuro-pathologist under the 2021 WHO classification. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.

Intrathecal methotrexate in combination with systemic chemotherapy in glioblastoma patients with leptomeningeal dissemination: A retrospective analysis.

BACKGROUND: Glioblastoma (GBM) is one of the most common and aggressive primary malignant brain tumors with severe symptoms and a poor prognosis. Leptomeningeal dissemination (LMD) is a serious complication of GBM that often results in dire outcomes. There is currently no effective treatment. AIM: To estimate the clinical outcomes of combination therapy in GBM patients with LMD. METHODS: A retrospective analysis was conducted using data collected from GBM patients diagnosed with LMD from January 2012 to December 2019 at our institution. All these patients had received at least one cycle of a combination therapy consisting of intrathecal methotrexate (MTX) and systemic chemotherapy. Clinical and pathological data were analyzed to explore the outcome of GBM patients with LMD and to determine the most effective treatment. RESULTS: Twenty-six patients were enrolled in this study. The median time from GBM diagnosis to LMD development was 9.3 mo (range: 2-59 mo). The median overall survival of LMD patients from diagnosis to after receiving systemic chemotherapy in combination with intrathecal MTX was 10.5 mo (range: 2-59 mo). In the Cox univariate analysis, gross resection of tumor (P = 0.022), Karnofsky performance status (KPS) > 60 (P = 0.002), and Ommaya reservoir implant (P < 0.001) were correlated with survival. Multivariate analysis showed that KPS > 60 (P = 0.037) and Ommaya reservoir implant (P = 0.014) were positive factors correlated with survival. Myelotoxicity and gastrointestinal reactions were the common toxicities of this combination therapy. According to Common Terminology Criteria of Adverse Events 4.03, most of the patients presented with toxicity less than grade 3. CONCLUSION: Intrathecal MTX administration combined with systemic chemotherapy is a potentially effective treatment for patients with GBM and LMD, with mild treatment-related side effects.

LRRFIP1, an epigenetically regulated gene, is a prognostic biomarker and predicts malignant phenotypes of glioma.

AIMS: Glioblastoma (GBM) is the most common malignant brain tumor with an adverse prognosis in the central nervous system. Traditional histopathological diagnosis accompanied by subjective deviations cannot accurately reflect tumor characteristics for clinical guidance. DNA methylation plays a critical role in GBM genesis. The focus of this project was to identify an effective methylation point for the classification of gliomas, the interactions between DNA methylation and potential epigenetic targeted therapies for clinical treatments. METHODS: Three online (TCGA, CGGA, and REMBRANDT) databases were employed in this study. T-test, Venn analysis, univariate cox analysis, and Pearson's correlation analysis were adopted to screen significant prognostic methylation genes. Clinical samples were collected to determine the distributions of LRRFIP1 (Leucine Rich Repeat of Flightless-1 Interacting Protein) protein by immunohistochemistry assay. Kaplan-Meier survival and Cox analysis were adopted to evaluate the prognostic value of LRRFIP1. Nomogram model was used to construct a prediction model. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway were performed to explore functions and related mechanisms of LRRFIP1 in gliomas. RESULTS: Our results showed that 16 genes were negatively connected with their methylation level and correlated with clinical prognosis of GBM patients. Among them, LRRFIP1 expression showed the highest correlation with its methylation level. LRRFIP1 was highly expressed in WHO IV, mesenchymal, and IDH wild-type subtype. LRRFIP1 expression was an independent risk factor for OS (overall survival) in gliomas. CONCLUSION: LRRFIP1 is an epigenetically regulated gene and a potential prognostic biomarker for glioma. Our research may be beneficial to evaluate clinical efficacy, assess the prognosis, and provide individualized treatment for gliomas.

An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T2-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T2-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T2-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T2-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T2-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.

Molecular Characterization and Clinical Relevance of ANXA1 in Gliomas via 1,018 Chinese Cohort Patients.

Annexin A1 (ANXA1) is a calcium-dependent phospholipid-binding protein and has been implicated in multiple functions essential in cancer, including cell proliferation, apoptosis, chemosensitivity, metastasis, and invasion. However, the biological role and clinical behavior of ANXA1 in glioma remain unclear. In this study, RNA-seq (n = 1018 cases) and whole-exome sequencing (WES) (n = 286 cases) data on a Chinese cohort, RNA-seq data with different histological regions of glioblastoma blocks (n = 270 cases), and scRNA-seq data (n = 7630 cells) were used. We used the R software to perform statistical calculations and graph rendering. We found that ANXA1 is closely related to the malignant progression in gliomas. Meanwhile, ANXA1 is significantly associated with clinical behavior. Furthermore, the mutational profile revealed that glioma subtypes classified by ANXA1 expression showed distinct genetic features. Functional analyses suggest that ANXA1 correlates with the immune-related function and cancer hallmark. At a single-cell level, we found that ANXA1 is highly expressed in M2 macrophages and tumor cells of the mesenchymal subtype. Importantly, our result suggested that ANXA1 expression is significant with the patient's survival outcome. Our study revealed that ANXA1 was closely related to immune response. ANXA1 plays a key factor in M2 macrophages and MES tumor cells. Patients with lower ANXA1 expression levels tended to experience improved survival. ANXA1 may become a valuable factor for the diagnosis and treatment of gliomas in clinical practice.

Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.

[This corrects the article DOI: 10.3389/fonc.2021.699789.].

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.

OBJECTIVE: To identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM). MATERIALS AND METHODS: This retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.9 years; range, 16-77 years) and 100 patients with GBM (58 men, 42 women; mean age, 53.4 ± 14.1 years; range, 12-77 years) and divided them into a training and validation set randomly. Radiomics features were extracted from the tumor mass and peritumoral edema. Three feature selection and classification methods were evaluated in terms of their performance in distinguishing GSM and GBM: the least absolute shrinkage and selection operator (LASSO), Relief, and Random Forest (RF); and adaboost classifier (Ada), support vector machine (SVM), and RF; respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of each method were analyzed. RESULTS: Based on tumor mass features, the selection method LASSO + classifier SVM was found to feature the highest AUC (0.85) and ACC (0.77) in the validation set, followed by Relief + RF (AUC = 0.84, ACC = 0.72) and LASSO + RF (AUC = 0.82, ACC = 0.75). Based on peritumoral edema features, Relief + SVM was found to have the highest AUC (0.78) and ACC (0.73) in the validation set. Regardless of the method, tumor mass features significantly outperformed peritumoral edema features in the differentiation of GSM from GBM (P < 0.05). Furthermore, the sensitivity, specificity, and accuracy of the best radiomics model were superior to those obtained by the neuroradiologists. CONCLUSION: Our radiomics study identified the selection method LASSO combined with the classifier SVM as the optimal method for differentiating GSM from GBM based on tumor mass features.

Clinical characteristics and surgical outcomes of ependymomas in the upper cervical spinal cord: a single-center experience of 155 consecutive patients.

Ependymomas occurring in the upper cervical spinal cord (above the level of the C4 segment) are rare entities with great therapeutic challenges. This study was aimed to investigate the clinicoradiological characteristics and the prognosis in a large cohort of upper cervical ependymomas from a single institution. This retrospective study enrolled 155 patients with primary ependymomas in the upper cervical spinal cord. The pre- and post-operative clinical and magnetic resonance imaging profiles were collected. The neurological outcomes and survival events were evaluated, and potential independent risk factors were analyzed. There were 82 females and 73 males, with an average age of 43.1 ± 11.3 years. Immediately post-operatively, 118 (76.1%) patients experienced neurological deterioration and 32 (20.7%) patients remained unchanged. Three months after surgery, 61 (39.4%) patients showed deteriorated neurological functions compared to the pre-operative baseline levels. After an average follow-up period of 56.0 ± 24.7 months, the neurological functions were worse than the baseline status in 37 (23.9%) patients and improved in 33 (21.3%) patients, respectively. Logistic regression analysis identified that lower age (≤ 42 years) and lower pre-operative MMS (I-II) were independent protective factors for predicting favorable neurological functions. Multivariate Cox regression analysis revealed that incomplete resection was the only independent risk factor associated with a shorter progression-free survival. Age and pre-operative functional status affect the long-term neurological outcomes, and incomplete resection was associated with a shorter survival. Our findings indicate that gross total resection should be the goal of surgical treatment of upper cervical ependymomas.

Comparative efficacy of antiangiogenic treatment for newly diagnosed glioblastoma: A protocol for systematic review and network meta-analysis.

BACKGROUND: Glioblastoma is the most common malignant primary brain tumor which has highly expressed vascular endothelial growth factor. To date, various antiangiogenic drugs have been investigated in clinical trials but with no overall conclusion, especially for newly diagnosed glioblastoma (nGBM). In this study, Bayesian network meta-analysis will be used to conduct a comprehensive analysis of the results of different clinical trials, and assess the efficacy of different antiangiogenic drugs on nGBM. METHODS: In order to find more comprehensive information about the application of antiangiogenic drugs in nGBM patients, we searched the MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials for relevant randomized controlled trials. We also reviewed their reference lists to avoid omissions. Cochrane risk of bias tool (V.1.4.3) and Stata (V.15.0) will be used to assess the methodological quality of this review. RESULTS: This study will provide reliable evidence for different antiangiogenic therapies in nGBM patients. CONCLUSION: We will evaluate the relative effectiveness of different antiangiogenic drugs and rank each intervention in nGBM patients through prognosis to provide decision-making reference on which method to choose for clinicians. PROTOCOL REGISTRATION NUMBER: CRD42019146537.

Hemangiopericytomas: Spatial Intracranial Location in a Voxel-Based Mapping Study.

BACKGROUND AND PURPOSE: To investigate the preferred location of intracranial hemangiopericytomas (IHPCs) with voxel-based mapping and 3-dimensional reconstruction from MRI data. METHODS: Gadolinium-enhanced tumors of 258 primary and single IHPCs were segmented semi-automatically, followed by manual checking and editing of boundaries. The lesions were registered to Montreal Neurological Institute standard anatomical space, and heat-map and 3-dimensional rendered frequency images were generated. All tumors were then superimposed on the Anatomical Automatic Labeling (AAL) template to further investigate the difference in the tumor location based on the voxel-wise frequency of occurrence with respect to laterality, sex, age, and pathologic grade. RESULTS: The 3-dimensional rendered images show that the tumors commonly located in the posterior cranial cavity, surrounding the tentorium. The posterior third of the superior sagittal sinus and the confluence of sinuses were commonly affected. According to the analysis of tumor occurrence frequency in the AAL template, IHPCs were mainly observed in the limbic lobe, occipital lobe, and cerebellum. Tumors in younger patients preferentially located in the right occipital region (P = .027), whereas those with higher pathological grade more often located in the left parietal lobe (P = .034). CONCLUSIONS: This is the first voxel-based study to explore the predilection site of IHPCs. Our study suggests that these tumors commonly affect the posterior cranial cavity, adjoining the tentorium and venous sinus. Further research is needed to investigate the possible factors underlying these topographic preferences.

Surveying brain tumor heterogeneity by single-cell RNA-sequencing of multi-sector biopsies.

Brain tumors are among the most challenging human tumors for which the mechanisms driving progression and heterogeneity remain poorly understood. We combined single-cell RNA-seq with multi-sector biopsies to sample and analyze single-cell expression profiles of gliomas from 13 Chinese patients. After classifying individual cells, we generated a spatial and temporal landscape of glioma that revealed the patterns of invasion between the different sub-regions of gliomas. We also used single-cell inferred copy number variations and pseudotime trajectories to inform on the crucial branches that dominate tumor progression. The dynamic cell components of the multi-region biopsy analysis allowed us to spatially deconvolute with unprecedented accuracy the transcriptomic features of the core and those of the periphery of glioma at single-cell level. Through this rich and geographically detailed dataset, we were also able to characterize and construct the chemokine and chemokine receptor interactions that exist among different tumor and non-tumor cells. This study provides the first spatial-level analysis of the cellular states that characterize human gliomas. It also presents an initial molecular map of the cross-talks between glioma cells and the surrounding microenvironment with single-cell resolution.

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 solitary brain MET) were divided into training (n = 227) and test (n = 185) cohorts. Radiomic features extraction was performed with PyRadiomics software. In the training cohort, twelve feature selection methods and seven classification methods were evaluated to construct favorable radiomic machine-learning classifiers. The performance of the classifiers was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). In the training cohort, thirteen classifiers had favorable predictive performances (AUC≥0.95 and RSD ≤6). In the test cohort, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM) + least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy. Furthermore, the clinical performance of the best classifier was superior to neuroradiologists in accuracy, sensitivity, and specificity. In conclusion, employing radiomic machine-learning technology could help neuroradiologist in differentiating GBM from solitary brain MET preoperatively.

Quantification of Microsurgical Anatomy in Three-Dimensional Model: Transfrontal Approach for Anterior Portion of the Thalamus.

The thalamus located in the deep site of cerebrum with the risk of internal capsule injury during operation. The purpose of this study was to compare the anatomy for exposure and injury using simulative surgical corridor of 3-dimensional model. The 3-dimensional anatomy model of thalamus in cerebrum was created based on magnetic resonance imaging performed for 15 patients with trigeminal neuralgia. The midpoint of line between anterior edge and top of thalamus was the target exposed. Axis connecting the target with the anterior edge and top of caudate head was used to outline the cylinder, respectively, simulating surgical corridors 1 and 2 of transfrontal approach. Cerebral tissues involved in the corridors were observed, measured, and compared. Incision of cortex was made on the anterior portion of inferior frontal gyrus through corridor 1 and middle frontal gyrus through corridor 2. Both of the 2 corridors passed the caudate nucleus, the anterior limb and genu of internal capsule, ultimately reached the upper anterior portion of thalamus. The volumes of white matter, caudate head, and thalamus in the corridor 1 were more than those in corridor 2. Conversely, the volumes of cortex, internal capsule in corridor 2 were more than those in corridor 1. In conclusion, surgical anatomy-specific volume is helpful to postulate the intraoperative injury of transfrontal approach exposing anterior portion of the thalamus. The detailed information in the quantification of microsurgical anatomy will be used to develop minimally invasive operation.

Gridding Microsurgical Anatomy of Far Lateral Approach in the Three-Dimensional Model.

OBJECTIVE: The far lateral craniotomy involves osteotomy of various portions of occipital condyle. Intracranial operation exposing clivus encounters complicated neurovascular anatomy. The aim of the present study was to make refinement for the anatomy of far lateral approach by gridding route in the 3-dimensional model. METHODS: Computed tomography and magnetic resonance imaging data were used to construct 3-dimensional model containing osseous and neurovascular structures of skull base. Then, far lateral approach was simulated by triangular prism and divided into gridding surgical route. The relationship of surgical route and osseous and neurovascular structures was observed. Measurement of volume was performed to evaluate surgical exposure. RESULTS: Observation of 3-dimensional model showed bony drilling of far lateral approach started with the occipital condyle and passed through the lateral edge of foramen magnum. The cerebellum and medulla oblongata were exempted from the surgical route exposing clivus. The anatomy variances of operative space, osseous, and neurovascular structures in the gridding route were displayed clearly and compared objectively. CONCLUSION: The gridding operative spaces for the far lateral approach are useful to disclose the detailed discrepancy in the different surgical region. The volumetric measurement provides quantified information to facilitate a better understanding of the anatomy variance.

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor.

Low-grade gliomas almost invariably progress into secondary glioblastoma (sGBM) with limited therapeutic option and poorly understood mechanism. By studying the mutational landscape of 188 sGBMs, we find significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and is present in ∼14% of cases with significantly worse prognosis. Subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. Furthermore, we describe a MET kinase inhibitor, PLB-1001, that demonstrates remarkable potency in selectively inhibiting MET-altered tumor cells in preclinical models. Importantly, this compound also shows blood-brain barrier permeability and is subsequently applied in a phase I clinical trial that enrolls MET-altered chemo-resistant glioma patients. Encouragingly, PLB-1001 achieves partial response in at least two advanced sGBM patients with rarely significant side effects, underscoring the clinical potential for precisely treating gliomas using this therapy.

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.

OBJECTIVE: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. METHODS: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction. RESULTS: The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts. CONCLUSIONS: Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.