Center for Lasers and Photonics, IIT Kanpur, Kanpur, India.
Department of Physics, IIT Kanpur, Kanpur, India.
J Biophotonics. 2024 Nov;17(11):e202400284. doi: 10.1002/jbio.202400284. Epub 2024 Oct 8.
Cervical cancer can be detected at an early stage through the changes occurring in biochemical and morphological properties of epithelium layer. Fluorescence spectroscopy has the ability to identify these subtle changes non-invasively and in real time with good accuracy in comparison with conventional techniques. In this paper, we report the usage of a custom designed spatially resolved fibre-optic probe (SRFOP), which consists of 77 fibres in two concentric rings, for the detection of cervical cancer using fluorescence spectroscopy technique. The aim of this study is to classify different grades of cervical precancer on the basis of their fluorescence spectra followed by a robust classification algorithm. Fluorescence spectra of 28 cervical tissue samples of different categories have been recorded using six detector fibres of FOP at different spatial locations with the source fibre (SF). A 405 nm laser diode source has been utilised to excite the samples and a USB 4000 Ocean Optics spectrometer to collect the output spectra in the wavelength range 400-700 nm. Principal component analysis (PCA) was applied to the collected spectra to reduce the dimensionality of the data while preserving the most significant features for classification. The first 10 principal components, which captured the majority of the variance in the spectra, were selected as input features for the classification model. Classification was then performed using an artificial neural network (ANN) with a specific architecture, including an input layer, hidden layers, and a softmax activation function in the output layer. Experimental and classification results both demonstrate that proximal fibres (PFs) perform better than distal fibres (DFs) in capturing the discriminatory features present in the epithelium layer of cervical tissue samples as PF collect most of the signal from the epithelium layer. The combined approach of spatially resolved fluorescence spectroscopy and PCA-ANN classification techniques is able to discriminate different grades of cervical precancer and normal with an average sensitivity, specificity and accuracy of 93.33%, 96.67% and 95.57%, respectively.
宫颈癌可以通过上皮层生化和形态特性的变化在早期被检测到。荧光光谱技术具有在传统技术的基础上以良好的准确度非侵入性和实时识别这些细微变化的能力。在本文中，我们报告了使用一种定制设计的空间分辨光纤探头（SRFOP）的情况，该探头由两个同心环中的 77 根光纤组成，用于使用荧光光谱技术检测宫颈癌。本研究的目的是根据它们的荧光光谱对不同等级的宫颈癌进行分类，然后使用稳健的分类算法进行分类。使用 FOP 的六个探测器纤维在不同的空间位置记录了来自不同类别的 28 个宫颈组织样本的荧光光谱，源纤维（SF）。使用 405nm 激光二极管源激发样品，并使用 USB 4000 Ocean Optics 光谱仪在 400-700nm 的波长范围内收集输出光谱。对采集到的光谱应用主成分分析（PCA）以降低数据的维数，同时保留用于分类的最重要特征。捕获光谱中大部分方差的前 10 个主成分被选为分类模型的输入特征。然后使用具有特定架构的人工神经网络（ANN）进行分类，该架构包括输入层、隐藏层和输出层中的 softmax 激活函数。实验和分类结果均表明，近侧纤维（PFs）比远侧纤维（DFs）在捕获宫颈组织样本上皮层中存在的鉴别特征方面表现更好，因为 PF 收集了来自上皮层的大部分信号。空间分辨荧光光谱和 PCA-ANN 分类技术的组合方法能够以 93.33%、96.67%和 95.57%的平均灵敏度、特异性和准确度来区分不同等级的宫颈癌前病变和正常组织。
