DIAG Department, Sapienza University of Rome, Via Ariosto 25, 00185 Roma, Italy.
CNR-IBF, Istituto di Biofisica, Via Moruzzi 1, 56124 Pisa, Italy.
Sensors (Basel). 2022 Oct 3;22(19):7492. doi: 10.3390/s22197492.
The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.
癌症组织的分级仍然是病理学家面临的主要挑战之一。因此，开发增强的分析策略对于准确识别和进一步处理每个病例变得至关重要。拉曼光谱（RS）是一种有前途的肿瘤组织分类工具，因为它允许我们获得分析组织的生化图谱，并观察它们在生物分子、蛋白质、脂质结构、DNA、维生素等方面的演变。然而，通过提供一个分类系统，它能够将原始拉曼光谱信号作为输入来识别样本肿瘤类别，从而进一步提高其潜力；这可以在更短的时间内提供更可靠的响应，并减少或消除假阳性或假阴性诊断。深度学习技术近年来已经无处不在，模型能够在大多数不同的研究领域（例如自然语言处理、计算机视觉、医学成像）中以高精度进行分类。然而，深度模型通常依赖于大量标记数据集来产生合理的准确性，否则在训练数据不足时会出现过拟合问题。在本文中，我们提出了一种通过拉曼光谱的小波变换进行软骨瘤分类的方法（CLARA），该方法能够以高精度对骨组织中的拉曼光谱进行分类。CLARA 通过利用由两个二进制分类步骤组成的分类管道来识别和分级评估数据集中的肿瘤，其中第一个步骤在原始 RS 信号上执行，而第二个步骤则通过使用混合时频 2D 变换来完成。
