摘要：

研究背景: 多模态神经影像学检查在功能神经外科的诊断与治疗中发挥重要作用，但目前临床对这些复杂数据的管理有所欠缺。本研究尝试建立一套可行的多模态神经影像学数据信息管理系统并评估其应用效果。方法: 通过规范临床诊疗流程、分析影像学数据产生节点及梳理数据流动线路、建立存储命名规则，以及搭建存储服务器、培训专业人员等措施，设计并应用多模态神经影像学数据信息管理系统，以术前结构序列、其他术前影像、电极术后CT、电极重建、术后CT/MRI共5类数据的归档率作为主要评估指标，以数据归档消耗的总人·时和每例病例消耗的平均人·时作为次要评估指标。结果: 未进行多模态神经影像学数据信息管理（对照组，64例）的情况下，总人力消耗为192人·时，平均为3人·时/例；进行多模态神经影像学数据信息管理（数据管理组，50例）的情况下，总人力消耗84人·时，平均为1.68人·时/例。数据管理组术前结构序列[100%（50/50）对32.81%（21/64）；χ²=11.383，P=0.001]、其他术前影像[96%（48/50）对26.56%（17/64）；χ²=13.839，P=0.000]、电极术后CT[96%（48/50）对32.81%（21/64）；χ²=10.409，P=0.001]、电极重建[96%（48/50）对32.81%（21/64）；χ²=10.409，P=0.001]、术后CT/MRI[96%（48/50）对15.63%（10/64）；χ²=22.169，P=0.000]数据归档率均高于对照组。结论: 设计契合临床的多模态神经影像学数据信息管理系统，合理设置数据收集和归档节点，可以有效提高数据归档率，节约人力资源，保障临床数据的完备存储和临床诊疗的顺畅运行，有利于提高临床诊断与治疗水平。

关键词: 神经外科（学）, 神经成像, 电子数据处理, 卫生人力

Abstract:

Background: Multi-modal neuroimaging examinations play a crucial role in the diagnosis and treatment of functional neurosurgery. However, there is currently a lack of effective management for these complex data in clinical practice. This study attempts to establish a feasible multi- modal neuroimaging data information management system and evaluate its application effects. Methods: By standardizing clinical diagnosis and treatment processes, analyzing the nodes where imaging data were generated, and streamlining data flow routes, establishing storage naming conventions, setting up storage servers, and training specialized personnel, we designed and applied a multi-modal neuroimaging data information management system. The primary evaluation indicators were the archiving rates of 5 types of data: structural sequences, other preoperative images, postoperative electrode CT, electrode reconstruction, and postoperative CT/MRI. The secondary evaluation indicators included the total man-hours consumed for data archiving and the average man-hours consumed per case. Results: Without multi-modal neuroimaging data information management (control group, n = 64), the total manpower consumption was 192 man-hours, with an average of 3 man-hours per case. With multi-modal neuroimaging data information management (data management group, n = 50), the total manpower consumption was 84 man-hours, with an average of 1.68 man-hours per case. The data management group had higher archiving rates compared to the control group: structural sequences [100% (50/50) vs. 32.81% (21/64); χ² = 11.383, P = 0.001], other preoperative images [96% (48/50) vs. 26.56% (17/64); χ² = 13.839, P = 0.000], postoperative electrode CT [96% (48/50) vs. 32.81% (21/64); χ² = 10.409, P = 0.001], electrode reconstruction [96% (48/50) vs. 32.81% (21/64); χ² = 10.409, P = 0.001], postoperative CT/MRI [96% (48/50) vs. 15.63% (10/64); χ² = 22.169, P = 0.000]. Conclusions: Designing a multi-modal neuroimaging data information management system that aligns with clinical practice and reasonably setting data collection and archiving nodes can effectively improve data archiving rates, save manpower resources, ensure the complete storage of clinical data, and ensure the smooth operation of clinical tasks, and enhance clinical diagnosis and treatment levels.

Key words: Neurosurgery, Neuroimaging, Electronic data processing, Health workforce