Vries Eva de, Alic Lejla, Schols Rutger M, Emanuel Kaj S, Wieringa Fokko P, Bouvy Nicole D, Tuijthof Gabriëlle J M
Research Engineering, Faculty of Health, Medicine, Life Sciences, Maastricht University, 6229 ER Maastricht, The Netherlands.
Magnetic Detection and Imaging Group, Technical Medical Centre, Faculty of Science and Technology, University of Twente, 7522 NB Enschede, The Netherlands.
Life (Basel). 2023 Jan 28;13(2):357. doi: 10.3390/life13020357.
In vivo diffuse reflectance spectroscopy provides additional contrast in discriminating nerves embedded in adipose tissue during surgery. However, large datasets are required to achieve clinically acceptable classification levels. This study assesses the spectral similarity between ex vivo porcine and in vivo human spectral data of nerve and adipose tissue, as porcine tissue could contribute to generate large datasets.
Porcine diffuse reflectance spectra were measured at 124 nerve and 151 adipose locations. A previously recorded dataset of 32 in vivo human nerve and 23 adipose tissue locations was used for comparison. In total, 36 features were extracted from the raw porcine to generate binary logistic regression models for all combinations of two, three, four and five features. Feature selection was performed by assessing similar means between normalized features of nerve and of adipose tissue (Kruskal-Wallis test, < 0.05) and for models performing best on the porcine cross validation set. The human test set was used to assess classification performance.
The binary logistic regression models with selected features showed an accuracy of 60% on the test set.
Spectral similarity between ex vivo porcine and in vivo human adipose and nerve tissue was present, but further research is required.
体内漫反射光谱技术在手术过程中鉴别嵌入脂肪组织中的神经时可提供额外的对比度。然而,需要大量数据集才能达到临床可接受的分类水平。本研究评估了猪离体组织与人体神经和脂肪组织体内光谱数据之间的光谱相似性,因为猪组织有助于生成大量数据集。
在124个神经部位和151个脂肪部位测量了猪的漫反射光谱。使用先前记录的包含32个人体神经体内部位和23个脂肪组织部位的数据集进行比较。从原始猪数据中总共提取了36个特征,以生成针对两个、三个、四个和五个特征的所有组合的二元逻辑回归模型。通过评估神经和脂肪组织归一化特征之间的相似均值(Kruskal-Wallis检验,P<0.05)以及在猪交叉验证集上表现最佳的模型来进行特征选择。使用人体测试集评估分类性能。
具有选定特征的二元逻辑回归模型在测试集上的准确率为60%。
猪离体组织与人体脂肪和神经组织之间存在光谱相似性,但仍需进一步研究。