用于脑肿瘤可解释机器学习流水线的可扩展人工智能驱动系统
A Scalable AI-Powered System for Explainable Machine Learning Pipelines in Brain Tumor
AI总结：
该研究开发了一款用于神经肿瘤学的可扩展网络视觉分析系统，整合队列管理、特征提取与受保护推理功能，可提升AI可解释性，为临床脑肿瘤AI应用提供实用基础。
AI中文摘要：
人工智能和放射组学正越来越多地应用于脑肿瘤研究，然而其向临床实践的转化仍受限于碎片化工作流程、透明度不足以及与终端用户需求的整合薄弱。我们推出首个可扩展的基于网络的视觉分析系统版本，旨在支持神经肿瘤学中放射组学驱动的机器学习推理。该平台在单一界面内整合了三项核心功能：结构化临床表格的队列管理、医学图像与分割掩码的放射组学特征提取，以及使用预训练机器学习模型的受保护推理。系统通过以用户为中心的迭代设计流程开发，并在公开的胶质母细胞瘤数据集和专有临床队列上进行了评估。一项关键贡献是明确展示中间工作流程产物，这提升了可追溯性、可解释性和人工智能的负责任使用。通过结合可移植性、可检查性和部署简便性，所提出的框架为脑肿瘤分析中面向临床的人工智能应用提供了实用基础。
英文摘要：
Artificial intelligence and radiomics are increasingly used in brain tumor research, yet their translation into clinical practice remains limited by fragmented workflows, poor transparency, and weak integration with end users' needs. We present the first version of a scalable web-based visual analytics system designed to support radiomics-driven machine learning inference in neuro-oncology. The platform integrates three core functions within a single interface: cohort management from structured clinical tables, radiomic feature extraction from medical images and segmentation masks, and guarded inference with pre-trained machine learning models. The system was developed through an iterative user-centred design process and evaluated on both a public glioblastoma dataset and a proprietary clinical cohort. A key contribution is the explicit exposure of intermediate workflow artifacts, which improves traceability, interpretability, and responsible use of AI. By combining portability, inspectability, and deployment simplicity, the proposed framework offers a practical foundation for clinically oriented AI applications in brain tumor analysis.
