Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada.
Department of Computer Science, Western University, London, Canada.
Sci Rep. 2023 Jan 27;13(1):1530. doi: 10.1038/s41598-023-28479-2.
Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.
肿瘤细胞表现出改变的代谢途径，导致辐射抗性和疾病进展。拉曼光谱（RS）是一种无标记的光学模态，可以监测肿瘤细胞和组织中照射后的生物分子特征。卷积神经网络（CNN）直接从数据中自动提取特征，在数据丰富且特征提取具有挑战性的情况下，其分类准确性超过传统机器学习。我们有兴趣开发一种基于 CNN 的预测模型，根据肿瘤对放射治疗的敏感性或抗性程度来描述临床肿瘤对放射治疗的反应。在这项工作中，我们构建了一个用于识别肿瘤组织拉曼光谱中照射后光谱变化的 CNN 架构。该模型使用乳腺癌异种移植的拉曼光谱来训练，以区分照射和未照射的组织。CNN 有效地对组织光谱进行分类，在照射后 3 天采集的数据中，准确率超过 92.1%，在照射后 1 天采集的数据中，准确率超过 85.0%。此外，我们使用留一法（小鼠、切片或拉曼图）验证方法评估 CNN，以研究其对新测试对象的泛化能力。当在训练过程中几乎没有特定对象的信息时，CNN 保留了良好的预测准确性（平均准确率分别为 83.7%、91.4%和 92.7%）。最后，我们将 CNN 的分类性能与基于组和基受限非负矩阵分解和随机森林（GBR-NMF-RF）分类的先前开发的模型进行了比较。我们发现，与 GBR-NMF-RF 方法相比，在照射后 3 天评估的小鼠中，CNN 产生了更高的分类准确性、灵敏度和特异性。总的来说，CNN 可以在照射后早期检测肿瘤组织中的生化光谱变化，而无需进行先前的手动特征提取。这项研究为开发用于监测患者放射反应的预测框架奠定了基础。
