Abstract
The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm−1. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.
Highlights
Oral cancer refers to a subgroup of head and neck malignancies that affect the lips, tongue, salivary glands, gingiva, floor of the mouth, buccal surfaces, and other intraoral locations
It is adapted from the partial least square regression (PLSR) technique, which aims to build a linear regression model using a latent variable (LV) approach to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space
The class average spectra were calculated from 24 representative spectra of HK samples, 22 representative spectra of oral epithelial dysplasia (OED) samples, and 22 representative spectra of oral squamous cell carcinoma (OSCC) samples, respectively
Summary
Oral cancer refers to a subgroup of head and neck malignancies that affect the lips, tongue, salivary glands, gingiva, floor of the mouth, buccal surfaces, and other intraoral locations. In the field of oral disease research, FTIR spectroscopy and imaging techniques have been used to investigate oral cancer and precancer using a variety of biological samples, including oral tissues, exfoliated oral cells, biofluids (e.g., serum, plasma, saliva, sputum), and extracellular vesicles. Those studies provide early evidence for the usefulness of FTIR in oral cancer characterization and the differentiation of cancerous samples from noncancerous ones [26]. IInn tthhee ccuurrrreenntt ssttuuddyy,, wwee rreeppoorrtt aann aaccccuurraattee ddiissccrriimmiinnaattiioonn ooff OOSSCCCC bbiiooppssyy ssaammpplleess ffrroomm HHKK ssaammpplleess uussiinngg ttrraannssmmiissssiioonn FFTTIIRR iimmaaggiinngg tteecchhnniiqquuee ttooggeetthheerr wwiitthh mmaacchhiinnee lleeaarrnniinngg aallggoorriitthhmmss. LOo,uUisS, AM)Ofo, rU5SAm)info×r 53mtimine×s 3attrimooems attemropoemratteumre-. pTehreatduerpea. rTahffiendiezpedarsaaffminpizleesdwsaemrepaliers-dwrieerde aanird-dsrtoierdedanindastvoarceuduimn adveasiccucuatmord. esiccator
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call