文章：

使用一维卷积神经网络追踪胶质母细胞瘤的治疗反应

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

原文发布日期：7 August 2023

DOI: 10.3390/cancers15154002

类型: Article

开放获取: 是

英文摘要：

Background: Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation. Methods: This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation. Results: The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method. Conclusions: The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

摘要翻译： 

背景：胶质母细胞瘤（GB）是一种难以治疗的恶性脑肿瘤，即便经过积极治疗后仍常复发。治疗反应评估依赖于遵循神经肿瘤学反应评估标准（RANO）的磁共振成像（MRI）。然而，假性进展和假性反应等现象阻碍了早期评估。磁共振波谱成像（MRS/MRSI）可提供代谢组学信息，但因缺乏普及度和标准化而未得到充分利用。

方法：本研究探索了波谱成像（MRSI）结合多种机器学习方法（包括一维卷积神经网络）在改善治疗反应评估方面的潜力。通过临床前GB模型（携带GL261肿瘤的小鼠）进行方法优化与验证。

结果：所提出的一维卷积神经网络模型成功识别了MRSI采样肿瘤的不同区域，包括正常脑组织（N）、对照组/无应答肿瘤（T）以及对治疗有反应的肿瘤（R）。利用梯度加权类别激活图（Grad-CAM）生成的类别激活图能够研究与模型相关的关键区域，从而提供模型可解释性。生成的显示N、T、R区域的彩色编码图与真实情况相比具有高度准确性（基于Dice评分），且性能优于我们先前的方法。

结论：该研究方法可能为治疗反应评估提供全新且更优的解决方案，有望更早提示肿瘤复发阶段。

原文链接：

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks