Cao Zheng, Pan Xiang, Yu Hongyun, Hua Shiyuan, Wang Da, Chen Danny Z, Zhou Min, Wu Jian
RealDoctor AI Research Center, College of Computer Science and Technology, Zhejiang University, China.
Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine, China.
BME Front. 2022 Apr 7;2022:9872028. doi: 10.34133/2022/9872028. eCollection 2022.
Distinguishing tumors from normal tissues is vital in the intraoperative diagnosis and pathological examination. In this work, we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues. Raman spectra can reflect the substance components of the target tissues. However, the feature peak is slight and hard to detect due to environmental noise. Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches. First, we collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with the Raman shift ranging from 385 to 1545 cm. Second, a one-dimensional residual convolutional neural network (1D-ResNet) architecture is designed to classify the tumor tissues of colorectal cancer. Third, we visualize and interpret the fingerprint peaks found by our deep learning model. Experimental results show that our deep learning method achieves 98.5% accuracy in the detection of colorectal cancer and outperforms traditional methods. Overall, Raman spectra are a novel modality for clinical detection of colorectal cancer. Our proposed ensemble 1D-ResNet could effectively classify the Raman spectra obtained from colorectal tumor tissues or normal tissues.
在术中诊断和病理检查中，区分肿瘤组织与正常组织至关重要。在这项工作中，我们提议将拉曼光谱作为一种新型的手术检测手段来检测结直肠癌组织。拉曼光谱能够反映目标组织的物质成分。然而，由于环境噪声的影响，特征峰很微弱且难以检测。收集高质量的拉曼光谱数据集并开发有效的深度学习检测方法可能是可行的途径。首先，我们从26名结直肠癌患者身上收集了一个大型拉曼光谱数据集，拉曼位移范围为385至1545厘米。其次，设计了一种一维残差卷积神经网络（1D-ResNet）架构来对结直肠癌的肿瘤组织进行分类。第三，我们对深度学习模型找到的指纹峰进行可视化和解释。实验结果表明，我们的深度学习方法在结直肠癌检测中准确率达到了98.5%，优于传统方法。总体而言，拉曼光谱是结直肠癌临床检测的一种新型手段。我们提出的集成1D-ResNet能够有效地对从结直肠肿瘤组织或正常组织获得的拉曼光谱进行分类。
