A comprehensive FTIR micro-spectroscopic analysis and classification of precancerous human oral tissue aided by machine learning†

IF 3.5 Q2 CHEMISTRY, ANALYTICAL Sensors & diagnostics Pub Date : 2024-09-27 DOI:10.1039/D4SD00122B
Pranab Jyoti Talukdar, Kartikeya Bharti, Sumita Banerjee, Sautami Basu, Sanjeet Kumar Das, Ranjan Rashmi Paul, Mousumi Pal, Mahendra Prasad Mishra, Saikat Mukherjee, Pooja Lahiri and Basudev Lahiri
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Abstract

We present an analysis of the molecular vibrational assessments of different grades of oral precancerous tissue sections, aiming to an early, alternative method other than histopathology to definitively distinguish their grades and remove the interobserver variability related to histopathological grading. Assessment of the prognosis of oral potentially malignant disorders (OPMDs) is dependent only on clinical features, and no defined criteria are still proposed to analyze the treatment outcome. Chair-side analysis of the lymph node metastasis and staging of oral squamous cell carcinoma (OSCC) is also dependent on palpatory findings followed by magnetic resonance imaging (MRI). Among these, Fourier-transform infrared (FTIR) micro-spectroscopy emerges as a highly promising and versatile approach for analyzing oral cancer and precancer specimens, enabling the identification of chemical and molecular changes in tissue samples. In this work, an adequate number of tissue sections affected by different grades of precancer (mild dysplasia, moderate dysplasia, and severe dysplasia) were investigated for biochemical changes in the epithelium and sub-epithelium layers as characterized by their corresponding molecular vibration spectrum. The current study demonstrated distinct alterations based on the spectrum shift of proteins (particularly amide I and amide III) over the progression of precancer. Additionally, using the amide I and amide III regions, a peak fitting method was employed to estimate the secondary structures of proteins. Further, chemometric techniques of principal components analysis–linear discriminant analysis (PCA–LDA) were used to create discrimination models for the precancerous and control groups. Our investigation revealed that the predictive performance of the amide III region was better than that of the amide I region, achieving a 95% accuracy rate. To the best of our knowledge, this is one of the first studies on the application of FTIR micro-spectroscopy for the classification of oral precancers in humans, aided by machine learning.

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机器学习辅助下的人类口腔癌前组织傅立叶变换红外微光谱综合分析与分类†。
我们对不同等级的口腔癌前组织切片进行了分子振动评估分析,旨在提供一种组织病理学以外的早期替代方法,以明确区分其等级,并消除与组织病理学分级相关的观察者间差异。口腔潜在恶性疾病(OPMD)的预后评估仅依赖于临床特征,目前仍未提出明确的标准来分析治疗结果。口腔鳞状细胞癌(OSCC)的淋巴结转移和分期也依赖于触诊结果和磁共振成像(MRI)。在这些方法中,傅立叶变换红外(FTIR)显微光谱法是一种极具前景的多功能方法,可用于分析口腔癌和癌前病变标本,从而识别组织样本中的化学和分子变化。在这项工作中,研究人员对受不同等级癌前病变(轻度发育不良、中度发育不良和重度发育不良)影响的大量组织切片进行了调查,以了解上皮层和上皮下层的生化变化,并通过相应的分子振动光谱对其进行表征。本研究根据蛋白质(尤其是酰胺 I 和酰胺 III)在癌前病变进展过程中的频谱移动,证明了其明显的变化。此外,利用酰胺 I 和酰胺 III 区域,采用峰拟合方法估算了蛋白质的二级结构。此外,我们还利用主成分分析-线性判别分析(PCA-LDA)的化学计量学技术为癌前病变组和对照组建立了判别模型。我们的调查显示,酰胺 III 区域的预测性能优于酰胺 I 区域,准确率达到 95%。据我们所知,这是首次应用傅立叶变换红外微光谱在机器学习的辅助下对人类口腔癌前病变进行分类的研究。
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Back cover Pursuing theranostics: a multimodal architecture approach. A review on Ti3C2Tx based nanocomposites for the electrochemical sensing of clinically relevant biomarkers Back cover Introduction to Supramolecular Sensors: From Molecules to Materials
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