Surazynski Lukasz, Hassinen Ville, Nieminen Miika T, Seppänen Tapio, Myllylä Teemu
Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
Optoelectronics and Measurement Techniques Research Unit, Faculty of Information and Electrical Engineering, University of Oulu, Oulu, Finland.
Appl Spectrosc. 2024 May;78(5):477-485. doi: 10.1177/00037028241230568. Epub 2024 Feb 19.
Core needle biopsy is a part of the histopathological process, which is required for cancerous tissue examination. The most common method to guide the needle inside of the body is ultrasound screening, which in greater part is also the only guidance method. Ultrasound screening requires user experience. Furthermore, patient involuntary movements such as breathing might introduce artifacts and blur the screen. Optically enhanced core needle biopsy probe could potentially aid interventional radiologists during this procedure, providing real-time information on tissue properties close to the needle tip, while it is advancing inside of the body. In this study, we used diffuse optical spectroscopy in a custom-made core needle probe for real-time tissue classification. Our aim was to provide initial characteristics of the smart needle probe in the differentiation of tissues and validate the basic purpose of the probe of informing about breaking into a desired organ. We collected optical spectra from rat blood, fat, heart, kidney, liver, lungs, and muscle tissues. Gathered data were analyzed for feature extraction and evaluation of two machine learning-based classifiers: support vector machine and -nearest neighbors. Their performances on training data were compared using subject-independent -fold cross-validation. The best classifier model was chosen and its feasibility for real-time automated tissue recognition and classification was then evaluated. The final model reached nearly 80% of correct real-time classification of rat organs when using the needle probe during real-time classification.
粗针活检是组织病理学检查过程的一部分，是癌组织检查所必需的。引导针进入体内最常用的方法是超声筛查，在很大程度上它也是唯一的引导方法。超声筛查需要用户经验。此外，患者的非自主运动，如呼吸，可能会产生伪像并使屏幕模糊。光学增强型粗针活检探头在该过程中可能会帮助介入放射科医生，在针在体内推进时提供靠近针尖的组织特性的实时信息。在本研究中，我们在定制的粗针探头中使用漫射光谱进行实时组织分类。我们的目的是提供智能针探头在组织区分方面的初始特性，并验证该探头告知是否刺入所需器官的基本用途。我们收集了大鼠血液、脂肪、心脏、肾脏、肝脏、肺和肌肉组织的光谱。对收集到的数据进行分析以进行特征提取，并评估两种基于机器学习的分类器：支持向量机和k近邻。使用独立于受试者的10折交叉验证比较它们在训练数据上的性能。选择最佳分类器模型，然后评估其用于实时自动组织识别和分类的可行性。在实时分类期间使用针探头时，最终模型对大鼠器官的实时正确分类率接近80%。
