Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.

中文翻译：

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基于独立成分分析，使用血氧水平依赖性功能 MRI 进行肿瘤组织检测。

准确界定胶质瘤与周围正常大脑区域有助于最大限度地切除肿瘤并改善预后。血氧水平依赖 (BOLD) 功能磁共振成像 (fMRI) 已常规用于周围功能区域的术前测绘。为了充分利用这些成像数据，我们在这里展示了使用术前功能磁共振成像进行肿瘤勾画的可行性。特别是，我们介绍了一种基于静息态功能磁共振成像（rs-fMRI）独立成分分析（ICA）的肿瘤检测新方法，具有自动肿瘤成分识别功能。来自三个中心的 32 名神经胶质瘤患者的多中心 rs-fMRI 数据，加上来自第四个中心的 28 名非脑肌肉骨骼肿瘤患者的额外概念验证数据，被输入到具有不同组件总数的个体 ICA 中（跨国公司）。根据新的模板匹配算法自动确定源自优化 TNC 设置的最适合的肿瘤相关成分。三个中心胶质瘤组织检测成功率分别为100%、100%和93.75%，肌肉骨骼肿瘤检测成功率85.19%。我们认为，高成功率可能来自于以前被忽视的 BOLD rs-fMRI 在表征肿瘤引起的异常血管化、血管舒缩和灌注方面的能力。我们的研究结果建议额外使用 rs-fMRI 进行全面的术前评估。