Image-guided neurosurgery system (IGNS) has become an indispensable component of modern precise neurosurgery. Image-to-patient registration plays a key role in IGNS, as it directly impacts accuracy and security of the surgery. We present a novel surface-based image-to-patient registration method for IGNS, which is feature-free and performs direct registration. First, the bounding boxes of the surfaces are computed, and a convex plane-based approach is developed to obtain an initial posture close to the desired value. Then, a constraint-based dimensionality reduction strategy is proposed to identify the most optimal posture for coarse registration. Finally, the iterative closest point (ICP) algorithm is employed to refine and generate the final transformation. This registration method is evaluated using the phantom models. The results demonstrate that our method produces transformations comparable to the currently widely used method. The proposed feature-free method exhibits the potential to facilitate IGNS with sufficient precision.

Objective. Craniotomy is a critical prerequisite for numerous neuro-surgeries, including intracranial tumor resection and cerebral hemorrhage decompression. However, conventional manual craniotomy methods are often time-consuming, labor-intensive, and associated with limited efficiency and safety. Robotic systems offer significant potential to enhance craniotomy procedures by enabling precise positioning and stable motion control, thereby improving safety, accuracy, and efficiency. In this study, we proposed a novel path planning method for robotic craniotomy that automatically generates surgical paths using solely computed tomography (CT) images.Approach. The craniotomy process is divided into two stages: drilling and subsequent milling to connect the drilled holes. The drilling path is determined by the intersection of the skull structure and surgeon-defined drilling intents. A virtual-center method is introduced to adaptively compute an initial milling path from the drilling path, which is further optimized to minimize invasiveness and smoothed for robotic cranial milling.Results. Validation and evaluation were conducted using 10 skull phantoms and 3 living dogs. The results of high success rates demonstrated that our method generated clinically approved outcomes at both anatomical profile andin vivolevels.Significance. The proposed approach demonstrates seamless integration with force-based robotic surgical systems, highlighting their potential to enhance current craniotomy techniques while establishing a foundation for future developments in autonomous robotic neurosurgery.

Automatic-releasing tools are widely used in neurosurgical procedures to drill holes in the skull while protecting the brain from drilling through. The application of these tools in robotic surgical systems faces challenges due to the difficulty in understanding the skills of experienced surgeons, their complexity, the curved surfaces of the skull, and the need for consistent force control in variable bone layers. By applying machining theory and material removal analysis with real-time surgical force data, the drilling through detection method of bone layers is established based on the thrust force distribution model on cutting edges. Further, the effects of multi-layer and surface misalignment are discussed to explain the determination of the force threshold used in recognition, which translates the surgeon's skills into the control design principle. The robotic-assisted drilling controller is then designed with a switching guidance force based on the admittance controller, which reduces operational burden and enhances surgical efficiency. The experiments of drilling on complex surfaces and bone layers address the consistency, stability, precision, and rapid response of specialized drill bits in skull layers, which results in a safety improvement in robotic-assisted craniotomy.

Robot-assisted surgery encounters critical force control challenges during risky operations like craniotomy skull milling, where collaborative operation demands adaptation to three surgical-specific complexities: multi-scale stiffness variations across biological tissues, abrupt stiffness discontinuities at critical boundaries (e.g. skulldura interface), and unintuitive operator inputs during human-robot interaction. Consequently, controllers must dynamically adapt to this wide spectrum of tissue properties, a capability which exceeds the limits of conventional compliance control frameworks. This work presents a stiffness-observation-based force feedforward compensation controller that monitors the force-feedrate differential relationship to estimate real-time tissue stiffness, discriminating tissue types while compensating real-time force controllers. This controller is integrated into an active-constrained framework, replacing compliance control in the depth direction during milling operations. It establishes a hierarchical force control architecture where stiffness-derived information autonomously steers safety strategies, while surgeon-defined force constraints enable shared autonomy in human-robot interaction. The controller is numerically validated in simulated surgical environments and experimentally tested via in vivo craniotomies, demonstrating effective force tracking and safety assurance during complex milling tasks. By converting stiffness observations into real-time control actions, this approach enhances surgical safety in bone-tissue boundary transitions while maintaining intuitive human-robot collaboration.

Objective. Glioma resection remains one of the most challenging procedures in neurosurgery due to the tumor's high malignancy and prevalence. As a critical step in surgical intervention, craniotomy requires meticulous planning to achieve maximal tumor removal while minimizing neurological damage. However, current automated surgical planning methods face significant limitations in addressing craniotomy design, primarily due to the lack of explicit visual targets (e.g., vascular structures) and standardized geometric constraints for bone flap delineation. In this study, we propose an innovative learning-based framework specifically designed for automated craniotomy planning in glioma resection.Approach. Our approach effectively integrates preoperative imaging data and expert demonstrations into a reinforcement learning (RL) model to determine the optimal bone flap geometry. The key innovations of our method include: (1) a self-supervised learning strategy for implicit quantification of glioma, (2) an encoding method for craniotomy pattern designs, (3) a physics-based simulation engine for craniotomy policy training, and (4) an imitation learning-inspired planner for craniotomy planning. Experimental validation was conducted using a dataset derived from publicly available glioma patient images.Main results. The proposed method presents a success rate of 92.31% ± 3.85% when processing known craniotomy parameters, and a success rate of 80.77% ± 3.14% in end-to-end craniotomy planning from raw preoperative images to definitive surgical plans.Significance. The results demonstrate that our proposed method achieves human-level performance in craniotomy planning, and shows promising potential for end-to-end craniotomy planning from raw preoperative images to definitive surgical plans. Our research provides a valuable reference for the development of intelligent decision-support tools for future neurosurgical procedures.

BACKGROUND:In an unstructured environment where real-time human decision is essential, shared control allows collaboration between humans and robotic systems, combining advantages of both. However, existing control methods are challenged with precision loss, inconsistency and interference from unconscious human inputs. METHODS:An adaptive anisotropic control frame is presented, enabling interaction both operational and tactical levels. Using predefined trajectory, a dynamic weight function is proposed to allow the human operator to override. Movement along preferred direction is encouraged and compensated, providing accurate real-time tracking performance. Haptic feedback during shared control is evaluated and optimised. RESULTS:Experiments validate that the raised method can achieve a tracking precision of ± 0.17 m m $\pm 0.17mm$ under milling payload, with sensible feedback to the operator. The override manipulation can be rapidly made within 0.4 s as the tactical level interaction. CONCLUSION:The proposed approach provides both stability and flexibility in interactive surgical manipulations, maintaining similar precision with autonomous execution.

It has been five years since the last version of the clinical practice guidelines for the management of adult diffuse gliomas was published by the Asian Glioma Genome Atlas (AGGA). Significant progress and revisions have occurred in the diagnosis and treatment of adult diffuse gliomas in recent years. In response to these updates, the joint guideline committee of the Chinese Glioma Cooperative Group (CGCG), the Society for Neuro-Oncology of China (SNO-China), and the Chinese Brain Cancer Association (CBCA) has revised the clinical practice guidelines. This updated guideline emphasizes molecular and pathological diagnostics, as well as the primary treatment modalities of surgery, radiotherapy, chemotherapy, and targeted therapy. Additionally, we have incorporated findings from recent clinical trials of new therapies to align with cutting-edge treatment strategies. This guideline is designed to serve as a practical resource for all professionals involved in managing adult diffuse glioma patients, while also providing valuable information for insurance companies and other institutions responsible for regulating cancer care costs in China and beyond.

Craniotomy is a prerequisite for many neuro-surgeries such as intracranial tumor resection and decompression of cerebral hemorrhage. However, existing manual craniotomy methods are time-consuming and labor-intensive, presenting low-efficiency and security. Robots have shown great potential for safe, precise and efficient craniotomy by introducing accurate positioning and stable motion control. We proposed a method that automatically computes operation path for robotic craniotomy and adapt to the irregular geometric change of the skull, based on the surgeon's surgical plan from computed tomography (CT) images. The drilling path is generated from intersection between the skull entity and surgeon-input drilling intents. A virtual-center (VC) method is developed to adaptively compute an initial milling path through the generated drilling path, which is then improved under both clinical and skull-cutting constrains. The results show that our method works effectively to generate the operation path adapting to the unstructured skull. This research highlights the potential for optimizing current craniotomy procedures by employing the robot, laying the foundation for future autonomous robot-based craniotomy.

Major depressive disorder (MDD) is a prevalent psychiatric condition often accompanied by severe impairments in cognitive and functional capacities. This research was conducted to identify RNA modification-related gene signatures and associated functional pathways in MDD. Differentially expressed RNA modification-related genes in MDD were first identified. And a random forest model was developed and distinct RNA modification patterns were discerned based on signature genes. Then, comprehensive analyses of RNA modification-associated genes in MDD were performed, including functional analyses and immune cell infiltration. The study identified 29 differentially expressed RNA modification-related genes in MDD and two distinct RNA modification patterns. TRMT112, MBD3, NUDT21, and IGF2BP1 of the risk signature were detected. Functional analyses confirmed the involvement of RNA modification in pathways like phosphatidylinositol 3-kinase signaling and nucleotide oligomerization domain (NOD)-like receptor signaling in MDD. NUDT21 displayed a strong positive correlation with type 2 T helper cells, while IGF2BP1 negatively correlated with activated CD8 T cells, central memory CD4 T cells, and natural killer T cells. In summary, further research into the roles of NUDT21 and IGF2BP1 would be valuable for understanding MDD prognosis. The identified RNA modification-related gene signatures and pathways provide insights into MDD molecular etiology and potential diagnostic biomarkers.

Objectives:To develop an intelligent human-machine collaborative control robot-assisted craniotomy system, and test its efficacy by experiments. Methods:The system integrated a UR5 robotic arm (Universal Robots, Denmark), a host computer, a double six-degree-of-freedom force sensor(Nanjing Yuli Instrument Co., Ltd.), a medical drill(AESCULAP®, Germany), a Polaris Optical navigation system(NDI,Canada), with a self-designed navigation procedure and a visual graphical user interface(GUI). According to a preoperative CT and resection plan, the motion of robotic arm can be restricted in a precise and safe area. Through experiments of the 3D-printed skull models and animals (Bama mini pig), we tested the accuracy, efficiency and safety of the robot system. Results:After successfully developed the robot-assisted craniotomy system, we tested the collaborative controlling fluency of robotic arm with the average response time less than 1 s, as well as feedback sensitivity of force sensor with an average result of 60 N and 50 N when drilling on skull models and mini pigs respectively. In addition, compared with "surgeon" group, "robot" group had less average positioning error (1.87 ± 0.66 mm VS 3.14 ± 0.73 mm, P < 0.001) and time spent (6.64 ± 1.15min VS 8.06 ± 1.10min, P = 0.001) in skull model experiments. Also, in mini pig experiments, "robot" group had less average positioning error (3.26 ± 0.51 mm VS 4.39 ± 0.75 mm, P = 0.008) and time spent (11.83 ± 0.92min VS 26.10 ± 1.62min, P < 0.001) compared with "surgeon" group. No matter in skull model experiments or in mini pig experiments, the durations of robot startup and navigation process were not different between the experimental group and control group (3.44 ± 0.98 VS 3.75 ± 1.00min, P = 0.39 [skull model experiments]; 6.42 ± 0.65 VS 7.10 ± 1.12min, P = 0.11 [mini pig experiments]). Because of limited samples, we compared the incidence of tissue injury between "robot" and "surgeon" group jointly (3.8 % [1/26] VS 19.2 % [5/26], P = 0.193). Conclusion:Successfully developed, the human-machine collaborative robot-assisted craniotomy system achieved craniotomy procedure fluently providing a sensitive force feedback to surgeon and did better than manual work by surgeon in accuracy, efficiency and safety. Further experimental research needs to be performed to testify its applicability in neurosurgery in future.

OBJECTIVES:A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.MATERIALS AND METHODS:Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.RESULTS:Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.CONCLUSION:We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.CLINICAL RELEVANCE STATEMENT:Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.KEY POINTS:• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

OBJECTIVE:The classic transopercular or transsylvian approach to insular gliomas removes the tumor laterally through the insular cortex. This study describes a new anteroposterior approach through the frontal isthmus for insular glioma surgery.METHODS:The authors detailed the surgical techniques for resection of insular gliomas through the transfrontal isthmus approach. Fifty-nine insular gliomas with at least Berger-Sanai zone I involvement were removed with the new approach, and extent of resection and postoperative neurological outcomes were assessed.RESULTS:Fifty-nine patients were enrolled in the study, including 35 men and 24 women, with a mean (range) age 44.3 (19-75) years. According to the Berger-Sanai classification system, the most common tumor was a giant glioma (67.8%), followed by involvement of zones I and IV (18.6%). Twenty-two cases were Yaşargil type 3A/B, and 37 cases were Yaşargil type 5A/B. The average angle between the lateral plane of the putamen and sagittal line was 33.53°, and the average width of the isthmus near the anterior insular point was 33.33 mm. The average angle between the lateral plane of the putamen and the sagittal line was positively correlated with the width of the isthmus near the anterior insular point (r = 0.935, p < 0.0001). The median (interquartile range [IQR]) preoperative tumor volume was 67.82 (57.64-92.19) cm3. Of 39 low-grade gliomas, 26 (66.67%) were totally resected; of 20 high-grade gliomas, 19 (95%) were totally resected. The median (IQR) extent of resection of the whole group was 100% (73.7%-100%). Intraoperative diffusion-weighted imaging showed no cases of middle cerebral artery- or lenticulostriate artery-related stroke. Extent of insular tumor resection was positively correlated with the angle of the lateral plane of the putamen and sagittal line (r = -0.329, p = 0.011) and the width of the isthmus near the anterior insular point (r = -0.267, p = 0.041). At 3 months postoperatively, muscle strength grade exceeded 4 in all cases, and all patients exhibited essentially normal speech. The median (IQR) Karnofsky performance score at 3 months after surgery was 90 (80-90).CONCLUSIONS:The transfrontal isthmus approach changes the working angle from lateral-medial to anterior-posterior, allowing for maximal safe removal of insular gliomas.

Background: Glioma is the most common intracranial malignancy. Immune-infiltration and tumour stemness are associated with the prognosis of glioma. Although pleckstrin homology containing family A, number 4 (PLEKHA4) is widely expressed in various human cancers, its role in glioma remains unclear. Methods: We examined the features and clinical significance of PLEKHA4 in gliomas by analysing relevant data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. Gene set enrichment analysis (GSEA) was performed to determine the possible functions and pathways involving PLEKHA4 in glioma. The relationship between PLEKHA4 expression and the degree of oncogenic dedifferentiation was analysed using stemness scores (ss) calculated from epigenetic and transcriptomic features. We also explored the relationship between PLEKHA4 expression and immune cell infiltration in gliomas using the CIBERSORT databases. Furthermore, drug sensitivity analysis was performed using datasets from the GDSC and GTRP databases. In addition, we performed relevant in vitro experimental studies. Results: PLEKHA4 DNA hypomethylation status was associated with its high expression in glioma tissues as well as poor prognoses. Univariate and multivariate Cox analyses indicated that PLEKHA4 expression may be considered as an independent prognostic factor in patients with glioma. GSEA indicated that high PLEKHA4 expression was associated with Janus kinase (JAK)/signal transducer and activator of transcription (STAT), Wingless-Type MMTV Integration Site Family (Wnt), JUN N-terminal kinase (JNK) signalling pathways and involved in apoptotic, cytoskeletal, and cell adhesion biological processes (BPs). In addition, increased PLEKHA4 expression was associated with higher glioma stemness scores than lower PLEKHA4 expression levels. Furthermore, the expression of PLEKHA4 was shown to be associated with glioma infiltration by CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells. Drug sensitivity analysis also showed that PLEKHA4 expression was negatively correlated with the sensitivity of several small molecule kinase inhibitors. Furthermore, in vitro experiments confirmed that PLEKHA4 knockdown inhibited the proliferation of glioma cells. Conclusions: PLEKHA4 is highly expressed in glioma tissues and correlated with tumour stemness, immune cell infiltration and proliferation, suggesting its potential as a novel prognostic biomarker and therapeutic target in glioma.

BACKGROUND:Intracranial hemorrhage is extremely rare during the initial stages of glioma. Here, we report a case of glioma with unclassified pathology and intracranial bleeding.CASE SUMMARY:After the second surgery for intracerebral hemorrhage, the patient experienced weakness in the left arm and leg, but could walk unassisted. One month after discharge, the weakness in the left limbs had exacerbated and the patient also suffered from headaches and dizziness. A third surgery was ineffective against the rapidly growing tumor. Intracerebral hemorrhage may be the initial symptom of glioma in some rare cases, and atypical perihematomal edema can be used for diagnosis during an emergency. Certain histological and molecular features seen in our case were similar to that of glioblastoma with a primitive neuronal component, which is termed diffuse glioneuronal tumor with features similar to oligodendroglioma and nuclear clusters (DGONC). The patient underwent three surgeries to remove the tumor. The first tumor resection had been performed when the patient was 14-years-old. Resection of the hemorrhage and bone disc decompression were performed when the patient was 39-years-old. One month after the last discharge, the patient underwent neuronavigation-assisted resection of the right frontotemporal parietal lesion plus extended flap decompression. On the 50th d after the third operation, computed tomography imaging showed rapid tumor growth accompanied by brain hernia. The patient was discharged and died 3 d later.CONCLUSION:Glioma can present as bleeding in the initial stage and should be considered in such a setting. We have reported a case of DGONC, which is a rare molecular subtype of glioma with a unique methylation profile.

The procollagen C-protease enhancer ( PCOLCE ) has been identified to influence tumor growth and metastasis in multiple cancers. However, the relationship between PCOLCE activity and the progression of gliomas remains largely unknown. Glioma RNA-seq data were derived from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas databases for analysis. Kaplan–Meier survival curve, clinical characterization correlation, univariate and multivariate Cox, and receiver operating characteristic curve analyses were performed to assess the prognostic role of PCOLCE . Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to determine the functions or pathways associated with PCOLCE . The ESTIMATE and CIBERSORT algorithms, Spearman’s rank correlation analysis, and Tumor Immune Estimation Resource (TIMER) databases were used to explore the relationship between PCOLCE and immune infiltration. Correlation analysis between PCOLCE , related genes, and immune cell markers was conducted using the TIMER database. Immunophenoscore assays were performed to determine differential PCOLCE expression levels in glioma. The sensitivity of multi-drugs were determined to explore potential chemotherapeutic agents in between PCOLCE. Compared to normal brain tissue, PCOLCE expression was increased in glioma and correlated with shorter overall survival (OS). Furthermore, significant differences were observed in the immune scores and immune cell infiltration levels. PCOLCE is positively associated with immune checkpoints and many immune markers. Additionally, PCOLCE expression was higher in gliomas with higher IPS Z-scores in CGGA. High expression of PCOLCE increased sensitivity to multiple chemotherapy agents in CGGA (P < 0.001), and TCGA. These results suggest that PCOLCE significantly influences the prognosis of patients with glioma, can serve as an independent prognostic factor, and is related to tumor immunity. PCOLCE may be a novel immune-related target for treating gliomas. Additionally, analysis of chemosensitivity in gliomas with high PCOLCE expression may provide a promising direction for drug development.

Background Multimodal techniques-assisted resection of glioma under general anesthesia (GA) has been shown to achieve similar clinical outcomes as awake craniotomy (AC) in some studies. In this study, we aim to validate the use of multimodal techniques can achieve the maximal safe resection of high-grade glioma involving language areas (HGILAs) under GA. Methods HGILAs cases were reviewed and collected between January 2009 and December 2020 in our center. Patients were separated into multimodal group (using neuronavigation, intraoperative MRI combined with direct electrical stimulation [DES] and neuromonitoring [IONM]) and conventional group (neuronavigation alone) and clinical outcomes were compared between groups. Studies of HGILAs were reviewed systematically and the meta-analysis results of previous (GA or AC) studies were compared with our results. Results Finally, there were 263 patients in multimodal group and 137 patients in conventional group. Compared to the conventional group, the multimodal group achieved the higher median EOR (100% versus 94.32%, P < 0.001) and rate of gross total resection (GTR) (73.8% versus 36.5%, P < 0.001) and the lower incidence of permanent language deficit (PLD) (9.5% versus 19.7%, P = 0.004). The multimodal group achieved the longer median PFS (16.8 versus 10.3 months, P < 0.001) and OS (23.7 versus 15.7 months, P < 0.001) than the conventional group. The multimodal group achieved a higher rate of GTR than the cohorts in previous multimodal studies under GA and AC (73.8% versus 55.7% [95%CI 32.0–79.3%] versus 53.4% [35.5–71.2%]). The multimodal group had a lower incidence of PLD than the cohorts in previous multimodal studies under GA (9.5% versus 14.0% [5.8–22.1%]) and our incidence of PLD was a little higher than that of previous multimodal studies under AC (9.5% versus 7.5% [3.7–11.2%]). Our multimodal group also achieved a relative longer survival than previous studies. Conclusions Surgery assisted by multimodal techniques can achieve maximal safe resection for HGILAs under GA. Further prospective studies are needed to compare GA with AC for HGILAs.

PurposeTo establish a predictive model to predict the occurrence of language deficit for patients after surgery of glioma involving language areas (GILAs) under general anesthesia (GA).MethodsPatients with GILAs were retrospectively collected in our center between January 2009 and December 2020. Clinical variables (age, sex, aphasia quotient [AQ], seizures and KPS), tumor-related variables (recurrent tumor or not, volume, language cortices invaded or not, shortest distance to language areas [SDLA], supplementary motor area or premotor area [SMA/PMA] involved or not and WHO grade) and intraoperative multimodal techniques (used or not) were analyzed by univariate and multivariate analysis to identify their association with temporary or permanent language deficits (TLD/PLD). The predictive model was established according to the identified significant variables. Receiver operating characteristic (ROC) curve was used to assess the accuracy of the predictive model.ResultsAmong 530 patients with GILAs, 498 patients and 441 patients were eligible to assess TLD and PLD respectively. The multimodal group had the higher EOR and rate of GTR than conventional group. The incidence of PLD was 13.4% in multimodal group, which was much lower than that (27.6%, P<0.001) in conventional group. Three factors were associated with TLD, including SDLA (OR=0.85, P<0.001), preoperative AQ (OR=1.04, P<0.001) and multimodal techniques used (OR=0.41, P<0.001). Four factors were associated with PLD, including SDLA (OR=0.83, P=0.001), SMA/PMA involved (OR=3.04, P=0.007), preoperative AQ (OR=1.03, P=0.002) and multimodal techniques used (OR=0.35, P<0.001). The optimal shortest distance thresholds in detecting the occurrence of TLD/PLD were 1.5 and 4mm respectively. The optimal AQ thresholds in detecting the occurrence of TLD/PLD were 52 and 61 respectively. The cutoff values of the predictive probability for TLD/PLD were 23.7% and 16.1%. The area under ROC curve of predictive models for TLD and PLD were 0.70 (95%CI: 0.65-0.75) and 0.72 (95%CI: 0.66-0.79) respectively.ConclusionThe use of multimodal techniques can reduce the risk of postoperative TLD/PLD after removing GILAs under general anesthesia. The established predictive model based on clinical variables can predict the probability of occurrence of TLD and PLD, and it had a moderate predictive accuracy.

BACKGROUND: The current transsylvian or transopercular approaches make access difficult because of the limited exposure of insular tumors. Hence, maximal and safe removal of insular gliomas is challenging. In this article, a new approach to resect insular gliomas is presented. OBJECTIVE: To determine whether the new transfrontal limiting sulcus approach is helpful for maximal and safe removal of insular gliomas. METHODS: The authors reported surgical techniques for insular gliomas resected through the transfrontal limiting sulcus approach. The authors evaluated the surgical resections of 69 insular gliomas performed through the new approach in their department. The extents of resection and postoperative neurological outcomes were analyzed to determine the value of this new approach. RESULTS: Based on the Berger–Sanai classification, most insular gliomas were giant tumors (59.42%), followed by zone I + IV tumors (24.64%). The median (interquartile range) extent of resection of all patients was 100% (91%, 100%). The total resection rate for all gliomas was (55 of 69, 79.7%), and the total resection rate for low-grade gliomas was (28 of 40, 70%), which was significantly lower than that for high-grade gliomas (27 of 29, 93.1%) ( P = .019). All patients had muscle strength greater than grade 4 3 months after surgery. Only 1 patient had a speech disorder 3 months after surgery. The median Karnofsky Performance Status score at the time of the 3-month follow-up was 90. CONCLUSION: The transfrontal limiting sulcus approach can help to achieve maximal and safe removal of insular gliomas.