ObjectiveTo evaluate the feasibility of using patient-specific 3D-printed navigation templates combined with multimodal imaging for low-grade glioma (LGG) surgery.MethodsA retrospective cohort study was conducted involving 55 patients with supratentorial LGGs. Twenty-seven patients underwent surgery guided by a 3D-printed navigation template with multimodal 3D imaging (3D-printed group), and 28 received conventional neuronavigation-assisted surgery (control group). Perioperative outcomes—including operative time, tumor resection extent, intraoperative navigation usage, complications, and functional recovery—were compared.ResultsThe 3D-printed group had significantly shorter operative times (256.2 ± 9.8 vs. 271.6 ± 8.9 min, P < 0.05) and required fewer intraoperative navigation checks (0.2 ± 0.4 vs. 2.3 ± 1.6, P < 0.05). Tumor resection extent was comparable; residual volumes were categorized objectively (<10, 10–20, >20 cm³), with a trend toward lower residuals in the 3D group. Postoperative KPS scores and complication rates were similar between groups.ConclusionCombining 3D-printed navigation templates with multimodal imaging enhances surgical precision and efficiency in LGG resection while reducing dependence on costly intraoperative systems. This approach is safe, cost-effective, and especially useful in resource-limited environments.

BACKGROUND:Medical image segmentation is a fundamental task in medical image analysis and has been widely applied in multiple medical fields. The latest transformer-based deep learning segmentation model, Segment Anything Model (SAM), has demonstrated outstanding performance in natural image segmentation tasks through large-scale pre-training, achieving zero-shot image semantic understanding and pixel-level segmentation. However, medical images present challenges such as style variability, ill-defined object boundaries, and feature ambiguities, limiting the direct applicability of the SAM to medical image segmentation tasks. PURPOSE:To enhance the robustness of the SAM in the domain of medical segmentation, we propose the SAM-RCCF framework. This approach aims to enhance the generalizability and precision of segmentation performance across diverse intracranial tumor types, including gliomas, metastatic tumors, and meningiomas. MATERIALS AND METHODS:The study collected 484 axial T1-weighted contrast-enhanced (T1CE) magnetic resonance imaging (MRI) data of brain tumor patients, including 164 cases of glioma, 158 cases of metastatic tumors, and 162 cases of meningioma. All imaging data were randomly divided into training and testing sets. We employed the proposed SAM-RCCF model to perform segmentation experiments on these data, and five-fold cross-validation was adopted to evaluate the model's performance. This framework integrates the RefineNet module and the conditional control field with a conditional controller and Mask generator, enabling precise feature recognition and tailored segmentation for medical images, optimizing segmentation accuracy RESULTS: In the glioma segmentation experiment, the SAM-RCCF model achieved outstanding performance with an IOU of 0.90, DSC of 0.912, and HD of 13.13. For the meningioma segmentation task, it obtained an IOU of 0.9214, DSC of 0.93, and HD of 11.41, significantly outperforming other classic segmentation models. CONCLUSION:The segmentation experiment results demonstrate that in the segmentation tasks of glioma, metastatic tumors, and meningioma MRI images, the SAM-RCCF algorithm significantly outperformed the original SAM in terms of DSC, HD, and IOU segmentation performance metrics. The experimental results verify the effectiveness of the SAM-RCCF framework in segmenting complex and variable brain tumor images, enhancing segmentation accuracy and robustness.

Cerebral herniation is a life-threatening neurological emergency, where timely and accurate prediction is crucial for improving patient prognosis. Due to its rapid imaging advantages, CT becomes the preferred choice for cerebral herniation screening. With the continuous development of artificial intelligence technology in the field of neurological diseases, CT-based models provide significant support for computer-aided clinical diagnosis. However, current research on cerebral herniation diagnosis remains limited. Existing methods rely on traditional machine learning or focus solely on midline shift detection, which not only exhibits strong subjectivity but also neglects key structures such as the brainstem and the rich information from sagittal CT images. To address these limitations, this study focuses on mid-sagittal CT images including the brainstem and combines clinical data to construct a multimodal deep learning framework for cerebral herniation prediction. The model integrates mature and advanced deep learning architectures to extract and fuse features from CT images and clinical text data, employing multiscale convolution and attention mechanisms for diagnostic classification. The model is evaluated on datasets from two centers. Results show that on the internal test set, the model achieves accuracy, sensitivity, specificity, and AUC of 89%, 92%, 88%, and 0.94, respectively; on the external test set, it attains accuracy, sensitivity, specificity, and AUC of 81%, 82%, 80%, and 0.89, respectively, outperforming baseline methods and existing state-of-the-art approaches. Additionally, when compared with radiologists on the internal test set, the model's performance matches or exceeds the consensus of physicians. We also reveal the model's focus region through visual analysis, which further deepens the understanding of the model's prediction process and enhances its interpretability. Experiments demonstrate that the proposed method holds significant potential in assisting cerebral herniation diagnosis.

Intracranial solitary fibrous tumor (SFT) and hemangiopericytoma (HPC) are rare mesenchymal tumors with significant vascularization, often misdiagnosed as meningiomas. Surgical resection is the primary treatment, with postoperative radiotherapy increasingly recognized for its role in improving recurrence-free survival. However, standard radiotherapy regimens remain undefined. We retrospectively analyzed clinical data from 12 patients diagnosed with SFT/HPC who underwent surgical resection and postoperative intensity-modulated radiotherapy. Clinical information, imaging findings, treatment methods, and outcomes were reviewed. Surgical resection achieved complete or subtotal tumor removal in all cases. Postoperative radiotherapy was administered to 8 patients. During follow-up, 3 patients experienced tumor recurrence, necessitating reoperation, while 1 patient died due to complications. Those who received radiotherapy showed a trend towards reduced recurrence. Surgical resection remains the cornerstone of SFT/HPC treatment, with adjuvant radiotherapy potentially improving outcomes. However, individualized treatment strategies and long-term follow-up are crucial due to the tumor's propensity for recurrence. Further research is needed to optimize treatment approaches and enhance patient survival and quality of life.

Background and objectiveAccurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time consumption, fatigue, and inconsistent diagnoses. Recently, deep learning technologies, particularly Convolutional Neural Networks (CNN), have achieved breakthrough advances in medical image analysis, offering a new paradigm for automated precise diagnosis. However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a multi-task deep learning model for precise brain tumor segmentation and type classification.MethodsThe study included 485 pathologically confirmed cases, comprising T1-enhanced MRI sequence images of high-grade gliomas, metastatic tumors, and meningiomas. The dataset was proportionally divided into training (378 cases), testing (109 cases), and external validation (51 cases) sets. We designed and implemented BrainTumNet, a deep learning-based multi-task framework featuring an improved encoder-decoder architecture, adaptive masked Transformer, and multi-scale feature fusion strategy to simultaneously perform tumor region segmentation and pathological type classification. Five-fold cross-validation was employed for result verification.ResultsIn the test set evaluation, BrainTumNet achieved an Intersection over Union (IoU) of 0.921, Hausdorff Distance (HD) of 12.13, and Dice Similarity Coefficient (DSC) of 0.91 for tumor segmentation. For tumor classification, it attained a classification accuracy of 93.4% with an Area Under the ROC Curve (AUC) of 0.96. Performance remained stable on the external validation set, confirming the model’s generalization capability.ConclusionThe proposed BrainTumNet model achieves high-precision diagnosis of brain tumor segmentation and classification through a multi-task learning strategy. Experimental results demonstrate the model’s strong potential for clinical application, providing objective and reliable auxiliary information for preoperative assessment and treatment decision-making in brain tumor cases.

Background:Meningiomas and gliomas represent the most common benign and malignant brain tumors, where accurate segmentation is essential for clinical assessment and surgical planning. Although magnetic resonance imaging (MRI) serves as a crucial diagnostic tool, precise segmentation remains challenging due to significant morphological and structural variations between tumor types and surrounding complex soft tissues. While Mamba models demonstrate excellence in sequence processing and attention mechanisms show promising performance, both face limitations in feature extraction and computational efficiency, respectively. To address these challenges, we propose the MamTrans algorithm, which integrates state-space models (SSMs) with attention mechanisms to significantly improve computational efficiency while maintaining segmentation accuracy. Methods:This study utilized 418 cases of axial T1-weighted contrast-enhanced MRI data of brain tumors, comprising 177 cases of high-grade gliomas and 241 cases of meningiomas. To validate the findings, five-fold cross-validation was employed. Results:The newly algorithm MamTrans achieved promising segmentation results in the high-grade glioma segmentation experiment, with an intersection over union (IoU) of 88.12, a Dice similarity coefficient (DSC) of 89.23, and a Hausdorff distance (HD) of 12.67. In the meningioma segmentation experiment, its segmentation metrics were IoU of 90.26, DSC of 91.27, and HD of 15.14, on the external dataset, the model obtained IoU of 90.34, DSC of 91.25, and HD of 14.17, outperforming other segmentation models such as U-Net, DeepLab, and Attention U-Net. Conclusions:The research results demonstrate that the proposed MamTrans algorithm outperforms various segmentation models in the segmentation tasks of gliomas and meningiomas. Innovatively, this single algorithm achieves high-precision segmentation for two tumor types with remarkably different morphologies, while significantly reducing model complexity and computational overhead, exhibiting substantial clinical application value.

Background and objectivesGlioblastoma (GBM) and brain metastasis (MET) are the two most common intracranial tumors. However, the different pathogenesis of the two tumors leads to completely different treatment options. In terms of magnetic resonance imaging (MRI), GBM and MET are extremely similar, which makes differentiation by imaging extremely challenging. Therefore, this study explores an improved deep learning algorithm to assist in the differentiation of GBM and MET.Materials and methodsFor this study, axial contrast-enhanced T1 weight (ceT1W) MRI images from 321 cases of high-grade gliomas and solitary brain metastasis were collected. Among these, 251 out of 270 cases were selected for the experimental dataset (127 glioblastomas and 124 metastases), 207 cases were chosen as the training dataset, and 44 cases as the testing dataset. We designed a new deep learning algorithm called SCAT-inception (Spatial Convolutional Attention inception) and used five-fold cross-validation to verify the results.ResultsBy employing the newly designed SCAT-inception model to predict glioblastomas and brain metastasis, the prediction accuracy reached 92.3%, and the sensitivity and specificity reached 93.5 and 91.1%, respectively. On the external testing dataset, our model achieved an accuracy of 91.5%, which surpasses other model performances such as VGG, UNet, and GoogLeNet.ConclusionThis study demonstrated that the SCAT-inception architecture could extract more subtle features from ceT1W images, provide state-of-the-art performance in the differentiation of GBM and MET, and surpass most existing approaches.

Background:High-grade gliomas (HGG) and solitary brain metastases (SBM) are two common types of brain tumors in middle-aged and elderly patients. HGG and SBM display a high degree of similarity on magnetic resonance imaging (MRI) images. Consequently, differential diagnosis using preoperative MRI remains challenging. This study developed deep learning models that used pre-operative T1-weighted contrast-enhanced (T1CE) MRI images to differentiate between HGG and SBM before surgery. Methods:By comparing various convolutional neural network models using T1CE image data from The First Medical Center of the Chinese PLA General Hospital and The Second People's Hospital of Yibin (Data collection for this study spanned from January 2016 to December 2023), it was confirmed that the GoogLeNet model exhibited the highest discriminative performance. Additionally, we evaluated the individual impact of the tumoral core and peritumoral edema regions on the network's predictive performance. Finally, we adopted a slice-based voting method to assess the accuracy of the validation dataset and evaluated patient prediction performance on an additional test dataset. Results:The GoogLeNet model, in a five-fold cross-validation using multi-plane T1CE slices (axial, coronal, and sagittal) from 180 patients, achieved an average patient accuracy of 92.78%, a sensitivity of 95.56%, and a specificity of 90.00%. Moreover, on an external test set of 29 patients, the model achieved an accuracy of 89.66%, a sensitivity of 90.91%, and a specificity of 83.33%, with an area under the curve of 0.939 [95% confidence interval (CI): 0.842-1.000]. Conclusions:GoogLeNet performed better than previous methods at differentiating HGG from SBM, even for core and peritumoral edema in both. HGG and SBM could be fast screened using this end-to-end approach, improving workflow for both tumor treatments.

Objective To evaluate the accuracy of 3D printed individualized localization guide plate in the localization of superficial supratentorial lesions. Methods A prospective randomized controlled study was performed on 46 patients with intracranial supratentorial superficial lesions who received surgical treatment in our hospital from November 2020 to March 2022. The patients were randomly divided into 3D printing group（23 cases） and neural navigation group（23 cases） by envelope method. The 3D printing group used 3D printed individual positioning guide plates designed by the 3D Slicer software to locate and design surgical incision before surgery. The neural navigation group used neuronavigation system to locate and design surgical incision before surgery. Preoperative localization time, intraoperative lesion exposure, postoperative lesion resection and surgical cost were compared between the two groups. Results The positioning time of the 3D printing group（1.0±0.6） min was significantly shorter than that of the navigation group [（9.2±2.1） min, t=17.784,P=0.000], and the surgical cost of the 3D printing group（4763.5±388.1） yuan was less than that of the navigation group [（6476.1±424.7） yuan, t=14.277, P=0.000]. There was no significant difference between the two groups in the degree of intraoperative lesion exposure and lesion resection（both P=1.000）. ConclusionsIndividualized localization guide plate can locate superficial supratentorial lesions rapidly. Its accuracy is comparable to navigation, and its cost is low, which is worthy of promotion and application in clinical practice.

Objective:This study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS).Methods:396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model. The model performance was evaluated and tested, and its interpretability was explored.Results:A total of 102 PA cases were enrolled in this study, 51 from the ioCSF leakage group, and 51 from the non-ioCSF leakage group. 306 sagittal and 306 coronal T1CE slices were collected as the original dataset, and data augmentation was applied before model training and testing. In the test dataset, the DNN model provided a single-slice prediction accuracy of 97.29%, a sensitivity of 98.25%, and a specificity of 96.35%. In clinical test, the accuracy of the DNN model in predicting ioCSF leaks in patients reached 84.6%. The feature maps of the model were visualized and the regions of interest for prediction were the tumor roof and suprasellar region.Conclusion:In this study, the DNN model could predict ioCSF leaks based on preoperative T1CE images, especially in PAs in Hardy Stages A, B, and C. The region of interest in the model prediction-making process is similar to that of humans. DNN models trained with preoperative MRI images may provide a novel tool for predicting ioCSF leak risk for PA patients.

目的 评估面部血管重建结合手机AR显示技术在提高面部注射填充美容安全性的应用效果.方法 自2020年11月至2022年8月,宜宾市第二人民医院医疗美容科行面部注射填充的47例患者,随机分为观察组(24例)和对照组(23例).观察组术前行面部血管CTA检查,将CTA原始影像以DICOM格式保存并导入软件3D Slicer,重建出个体化面部血管三维模型,导入手机增强现实技术(augmented reality,AR)软件,与患者面部配准后,根据叠加影像描记面部血管走行,避开标记进行注射治疗;对照组术者根据手术经验进行注射.统计两组手术时间,术后即刻疼痛评定,以及术后面部血肿、瘀斑、组织坏死和失明发生情况.结果 观察组与对照组手术时间比较,差异无统计学意义(P>0.05),观察组术后即刻疼痛程度低于对照组(P<0.05),术后面部血肿、瘀斑发生次数均低于对照组(P<0.05),对照组发生1例组织坏死,两组患者均无严重并发症发生.结论 在面部注射填充美容手术中,面部血管重建结合手机AR显示技术可有效避开注射区域血管,降低手术并发症,具有一定的临床推广价值.

Objective:To investigate the application value of multimodal image three-dimensional reconstruction combined with facial nerve tracking in acoustic neuroma surgery.Methods:Forty-five patients with single acoustic neuroma accepted primary surgical treatment at Department of Neurosurgery, Yibin Hospital, West China Hospital, Sichuan University from November 2018 to June 2022 were chosen. The head, skull, brain tissues, arteriovenous system, tumor and facial nerves were reconstructed preoperatively by multimodal three-dimensional image reconstruction combined with facial nerve tracking. Preoperative planning was carried out according to the relationship between tumor and surrounding structures. The accuracy of facial nerve tracking was evaluated by intraoperative microscopic observation and nerve electrophysiological monitoring. House-Brackmann (H-B) criteria was used to evaluate facial nerve functions 2 weeks after surgery. Three months after surgery, all patients underwent enhanced MRI scanning, and combined with intraoperative findings, the degrees of acoustic neuroma resection were determined.Results:The facial nerves and their relations with acoustic neuroma were successfully tracked in all 45 patients; as confirmed by intraoperative microscopy and nerve electrophysiological monitoring, the results of facial nerves and their relations with acoustic neuroma were consistent in 42 patients and inconsistent in 3 patients. Using intraoperative nerve electrophysiological monitoring as gold standard, the accuracy of multimodal image three-dimensional reconstruction of the facial nerves was 93.3%. Preoperative facial nerve tracking results were highly consistent with intraoperative nerve electrophysiological monitoring results (Kappa=0.903, P<0.001). The facial nerves were intraoperatively preserved in 91.1% patients (41/45), and the facial nerve function was good in 86.7% patients (39/45) at 2 weeks after surgery. The total/subtotal resection rate of acoustic neuroma was 88.9% (40/45). No death, cerebrospinal fluid leakage, hematoma, or cerebral infarction were noted during the perioperative period. Conclusion:Multimodal image three-dimensional reconstruction combined with facial nerve tracking can help to protect blood vessels and nerves, improve total resection rate and facial nerve function retention rate in surgery of acoustic neuroma.

目的 探讨术前肿瘤三维重建用于幕上凸面脑肿瘤开颅术定位的准确性,为开颅手术提供一种精准、安全的辅助方法.方法 前瞻性纳入2018年4月-2020年11月四川大学华西医院宜宾医院神经外科收治的幕上凸面脑肿瘤患者,随机分入重建组和对照组.重建组采用术前肿瘤三维重建进行开颅术定位,对照组采用传统二维断层影像进行开颅术定位.比较两组患者的基础情况、术中定位及肿瘤暴露情况满意率、骨窗最大直径、手术时间、脑引流静脉损伤情况和术后皮下积液或颅内感染发生情况.结果 共纳入43例患者,重建组22例,对照组21例.两组患者的年龄、性别构成、中线移位发生率、肿瘤生长部位和肿瘤大小差异均无统计学意义(P＞0.05).两组患者的脑引流静脉损伤发生率和术后皮下积液或颅内感染发生率差异无统计学意义(P＞0.05).重建组的术中定位及肿瘤暴露情况满意率(95.5％vs.66.7％)高于对照组,骨窗最大直径[(6.26±1.32)vs.(7.31±1.13)cm]和手术时间[(194.00±22.76)vs.(214.57±26.53)min]低于对照组,差异均有统计学意义(P＜0.05).结论 采用术前三维重建有助于更精确地定位肿瘤,提高肿瘤暴露满意率,缩小开颅骨窗直径,缩短手术时间,与传统的二维断层影像定位相比更具有优势.

目的 探究手机普视3D SLAM AR软件在颅内幕上浅表肿瘤术前定位的价值.方法 分析10例颅内幕上浅表肿瘤患者的临床资料,病变直径2.3～6.8cm.术前利用MRI或CT扫描数据对肿瘤、头部轮廓、鼻部及双侧耳廓进行三维重建,载入普视3D SLAM软件,进行肿瘤术前定位(实验组),同时利用传统定位方法进行徒手定位(对照组),最后利用神经导航定位病变中心点作为评价标准,普视3D SLAM软件与徒手定位的中心点与导航定位的中心点之间的距离为定位误差,对结果进行统计学分析.结果 普视3DSLAM软件的定位误差[(0.48±0.04)cm]小于徒手定位法[(2.08±0.23)cm](P＜0.05).结论 普视3D手机AR软件Slam模块对颅内幕上浅表肿瘤定位较传统徒手定位法更准确,此外还能直观显示病变周围的皮层静脉、纤维束等结构,有助于合理地制定术前计划,最大程度地减小手术创伤.

目的 评估3D打印个体化穿刺导板辅助经皮微球囊压迫半月节治疗原发性三叉神经痛术中的准确性和疗效.方法 前瞻性研究2019年2月至2021年8月于我院治疗的35例原发性三叉神经痛患者,随机分为试验组和对照组,试验组18例,对照组17例.所有患者术前均行头颅CT薄层扫描,试验组采用3D打印个体化穿刺导板辅助经皮穿刺,对照组在移动C臂引导下徒手穿刺;记录所有患者穿刺针到位所用时间,术中穿刺针调整次数,术中球囊一次性成梨例数,术中移动C臂照射次数,记录患者术后并发症情况,术后BNI分级评分评估疗效.结果 与对照组相比,试验组穿刺到位所用时间、术中穿刺针调整次数及术中移动C臂照射次数均低于对照组(P<0.05),且术中球囊一次性成梨率明显高于对照组(P<0.05).根据BNI分级标准,两组患者术后治疗效果和并发症发生情况的差异无统计学意义(P>0.05),所有患者均未发生严重并发症.结论 3D打印个体化穿刺导板辅助经皮微球囊压迫半月节治疗原发性三叉神经痛手术,其精确度高,能有效提高穿刺成功率,缩短手术时间,降低手术风险,临床操作更简便、安全,值得向临床应用推广.

目的 研究二甲双胍处理对淀粉样蛋白前体蛋白/早老素基因1(APP/PS1)转基因小鼠神经保护作用及机制.方法 将APP/PS1转基因小鼠分成模型组和实验组,将同窝野生型小鼠作为正常组,每组16只.实验组给予二甲双胍350 mg·kg-1·d-1,正常组和模型组给予标准饮用水,连续4个月.用水迷宫实验检测3组小鼠逃生时间,以蛋白质印迹法检测沉默信息调节因子2同源蛋白1(SIRT1)、β淀粉蛋白前体蛋白(APP)、APPβ位点剪切酶1(BACE1)、β羧基末端片段(β-CTF)和核转录因子kappa B(NF-κB),酶联免疫吸附实验检测β-淀粉样蛋白(Aβ40)和β-淀粉样蛋白42(Aβ42),脑切片荧光染色检测老年斑数量,高尔基染色检测树突棘密度.结果 正常组、模型组和实验组于水迷宫实验第6天的逃生时间分别为(30.08±4.26),(54.71±6.58)和(34.56±3.27)s;这3组间树突棘密度分别为(1.68±0.17),(1.05±0.17)和(1.60±0.21)cell·μm-1.模型组和实验组的BACE1蛋白相对表达量分别为1.01±0.02和0.67±0.06;这2组的NF-κB蛋白相对表达量分别为1.05±0.03和0.71±0.04;这2组的老年斑数量分别为24.86±0.55和13.21±0.38;这2组的Aβ42水平分别为(2.54±0.17)和(1.13±0.09)ng·mg-1.模型组与正常组比较,上述指标的差异均有统计学意义(P<0.01,P<0.001);实验组与模型组比较,上述指标的差异均有统计学意义(P<0.01,P<0.001).结论 二甲双胍长期处理可缓解APP/PS1转基因小鼠认知障碍,这可能与增加SIRT1蛋白表达与减少BACE1产生有关.

目的 探讨基于多模态影像三维重建技术辅助功能区及其毗邻部位脑肿瘤手术中的作用.方法 回顾性分析2019-04—2021-06宜宾市第二人民医院神经外科收治的21例功能区及其毗邻部位脑肿瘤患者的临床资料.术前使用多模态影像三维重建技术,根据重建影像进行综合评估并实施手术,通过与术中所见与术后疗效综合评估有效性.结果 所有患者术中所见与术前重建影像均高度一致.其中19例患者术前均成功实现颅骨、脑组织、肿瘤、血管及白质纤维束走行的三维重建和融合,2例患者无法辨认功能区脑回和肿瘤的关系.21例患者中,肿瘤全切17例,次全切1例,3例部分切除.术后出现1例一过性肢体活动障碍和1例一过性语言障碍,均恢复良好.结论 利用多模态影像三维重建技术有助于在术前对于肿瘤与功能区脑回、锥体束及脑血管之间的关系进行精确评估,制定完备的手术方案,最大程度保护神经功能,并能提高肿瘤的全切除率.

目的 分析优化手术流程在深低温保存钴-60灭菌自体颅骨修补颅骨缺损手术降低近期(1个月)并发症发生率的方法、优点及意义.方法 回顾性分析我院2018年10月～2020年10月收治的46例颅骨缺损病人的临床资料,全部采用深低温保存自体颅骨修补颅骨缺损.根据术前颅骨处理方式不同分为两组,实验组24例,采用80℃无菌生理盐水1000 ml+地塞米松20 mg复温30分钟;对照组22例,采用传统处理方式.结果 实验组皮下积液1例;对照组硬膜外血肿2例,皮下积液11例,其中严重皮下积液1例,颅骨感染1例(再手术).通过80℃无菌生理盐水+地塞米松20 mg复温30分钟,大大减少手术近期并发症,提升手术效果及病人舒适度.随访证实无骨瓣塌陷、无骨髓炎、无皮下积液.结论 改进颅骨处理方式能大大降低深低温保存自体颅骨修补颅骨缺损近期并发症.对于有条件开展自体颅骨修补的单位值得推荐,是一种简便、经济、安全、可行的方法.