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 k-nearest neighbors. Their performances on training data were compared using subject-independent k-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.

中文翻译：

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使用新型光学针探针进行活检的实时组织分类

空心针活检是组织病理学过程的一部分，是癌组织检查所必需的。在体内引导针的最常见方法是超声检查，这在很大程度上也是唯一的引导方法。超声筛查需要用户经验。此外，患者的不自主运动（例如呼吸）可能会引入伪影并使屏幕模糊。光学增强型空心针活检探头可能会在此过程中为介入放射科医生提供帮助，在针尖在体内前进时提供有关针尖附近组织特性的实时信息。在这项研究中，我们在定制的芯针探针中使用漫反射光谱进行实时组织分类。我们的目的是提供智能针探针在组织分化方面的初始特征，并验证探针通知侵入所需器官的基本目的。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。对收集的数据进行分析，以进行特征提取和评估两个基于机器学习的分类器：支持向量机和 k 最近邻。使用独立于受试者的 k 倍交叉验证来比较他们在训练数据上的表现。选择最佳分类器模型，然后评估其实时自动组织识别和分类的可行性。最终模型在实时分类时使用针探针时，对大鼠器官实时分类的正确率达到近80%。