Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Nat Biomed Eng. 2022 May;6(5):648-657. doi: 10.1038/s41551-021-00809-7. Epub 2021 Nov 11.
Non-invasive imaging methods for detecting intratumoural viral spread and host responses to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents. Here we show that in mice with glioblastoma multiforme, the early apoptotic responses to oncolytic virotherapy (characterized by decreased cytosolic pH and reduced protein synthesis) can be rapidly detected via chemical-exchange-saturation-transfer magnetic resonance fingerprinting (CEST-MRF) aided by deep learning. By leveraging a deep neural network trained with simulated magnetic resonance fingerprints, CEST-MRF can generate quantitative maps of intratumoural pH and of protein and lipid concentrations by selectively labelling the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring exogenous contrast agents. We also show that in a healthy volunteer, CEST-MRF yielded molecular parameters that are in good agreement with values from the literature. Deep-learning-aided CEST-MRF may also be amenable to the characterization of host responses to other cancer therapies and to the detection of cardiac and neurological pathologies.
用于检测肿瘤内病毒传播和宿主对溶瘤病毒治疗反应的非侵入性成像方法要么速度较慢，特异性不足，要么需要使用放射性或基于金属的对比剂。在这里，我们展示了在患有多形性胶质母细胞瘤的小鼠中，通过深度学习辅助化学交换饱和转移磁共振指纹图谱（CEST-MRF），可以快速检测到溶瘤病毒治疗的早期凋亡反应（其特征为胞质 pH 值降低和蛋白质合成减少）。通过利用经过模拟磁共振指纹图谱训练的深度神经网络，CEST-MRF 可以通过选择性标记内源性蛋白质的可交换酰胺质子和脂质的可交换大分子质子，生成肿瘤内 pH 值以及蛋白质和脂质浓度的定量图谱，而无需使用外源性对比剂。我们还表明，在健康志愿者中，CEST-MRF 产生的分子参数与文献中的值非常吻合。基于深度学习的 CEST-MRF 也可能适用于表征宿主对其他癌症治疗的反应以及检测心脏和神经病理学。
