Morell Alexis A, Eichberg Daniel G, Shah Ashish H, Luther Evan, Lu Victor M, Kader Michael, Higgins Dominique M O, Merenzon Martin, Patel Nitesh V, Komotar Ricardo J, Ivan Michael E
Department of Neurosurgery, University of Miami Miller School of Medicine, 1095 NW 14th Terrace, Miami, Florida, 33136, USA.
Sylvester Cancer Center, University of Miami Health System, Miami, Florida, USA.
Neurooncol Adv. 2022 Sep 19;4(1):vdac142. doi: 10.1093/noajnl/vdac142. eCollection 2022 Jan-Dec.
Large-scale brain networks and higher cognitive functions are frequently altered in neuro-oncology patients, but comprehensive non-invasive brain mapping is difficult to achieve in the clinical setting. The objective of our study is to evaluate traditional and non-traditional eloquent areas in brain tumor patients using a machine-learning platform.
We retrospectively included patients who underwent surgery for brain tumor resection at our Institution. Preoperative MRI with T1-weighted and DTI sequences were uploaded into the Quicktome platform. We categorized the integrity of nine large-scale brain networks: language, sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention, salience and limbic. Network integrity was correlated with preoperative clinical data.
One-hundred patients were included in the study. The most affected network was the central executive network (49%), followed by the default mode network (43%) and dorsal attention network (32%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (3.42 vs 2.19, < .001), compared to patients without deficits. Furthermore, we found that patients without neurologic deficits had an average 2.19 networks affected and 1.51 networks at-risk, with most of them being related to non-traditional eloquent areas ( < .001).
Our results show that large-scale brain networks are frequently affected in patients with brain tumors, even when presenting without evident neurologic deficits. In our study, the most commonly affected brain networks were related to non-traditional eloquent areas. Integrating non-invasive brain mapping machine-learning techniques into the clinical setting may help elucidate how to preserve higher-order cognitive functions associated with those networks.
神经肿瘤患者常出现大规模脑网络和高级认知功能的改变，但在临床环境中难以实现全面的非侵入性脑图谱绘制。我们研究的目的是使用机器学习平台评估脑肿瘤患者的传统和非传统明确区域。
我们回顾性纳入了在本机构接受脑肿瘤切除手术的患者。将术前带有T1加权和DTI序列的MRI上传到Quicktome平台。我们对九个大规模脑网络的完整性进行了分类：语言、感觉运动、视觉、腹侧注意、中央执行、默认模式、背侧注意、突显和边缘系统。网络完整性与术前临床数据相关。
100名患者纳入研究。受影响最严重的网络是中央执行网络（49%），其次是默认模式网络（43%）和背侧注意网络（32%）。与无术前缺陷的患者相比，术前有缺陷的患者在手术前显示出改变的网络数量显著更多（3.42对2.19，<0.001）。此外，我们发现无神经缺陷的患者平均有2.19个网络受到影响，1.51个网络处于风险中，其中大多数与非传统明确区域相关（<0.001）。
我们的结果表明，脑肿瘤患者的大规模脑网络经常受到影响，即使没有明显的神经缺陷。在我们的研究中，最常受影响的脑网络与非传统明确区域相关。将非侵入性脑图谱机器学习技术整合到临床环境中可能有助于阐明如何保留与这些网络相关的高阶认知功能。
