Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning

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.

Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

The potential of Raman and FT-IR spectroscopic methods for the detection of chlorine in human nail samples

Although chlorine (Cl2) has been used as a chemical warfare agent since World War I there is no known specific and reliable biomarker to indicate the presence of chlorine. We distinguished chlorinated human nails from unchlorinated ones using Raman spectroscopy and Fourier Transform Infrared (FT-IR) Spectroscopy. This research was carried out between October 2018 and July 2019 on two nail samples taken from 55 male and 104 female volunteers. One sample from each participant was chlorinated, while the second sample was used as a control. Spectral data were collected from chlorinated and unchlorinated (control) human nails using Raman and FT-IR spectroscopy. Raman measurements were made between 100 and 3200 cm^-1, while FT-IR measurements were recorded over the range of 650 to 4000 cm^-1. Partial least squares regression-discriminant analysis (PLS-DA) was used to develop classification models for each spectral instrument. Results showed that the control and chlorinated nail samples were successfully discriminated with similar results achieved with both instruments. Minor differences were observed in the performance of classification models. The FT-IR spectroscopy model (sensitivity = 95%, specificity = 99%, accuracy = 97%) was found to be more successful with a smaller margin of error (sensitivity = 95%, specificity = 99%, accuracy = 96%) compared to the Raman spectroscopy model. This method can be used successfully for both ante-mortem and post-mortem diagnosis of chlorine exposure.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy's multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Fourier transform infrared spectroscopy based spectral biomarkers of metastasized breast cancer progression

Breast cancer is a global health issue and the second leading cause of cancer death in women. Breast cancer tends to migrate to bone and causes bone metastases which is ultimately the cause of death. Here, we report the use of FTIR to identify spectral biomarkers of cancer progression on 3D in vitro model of breast cancer bone metastasis. Our results indicate that the following spectral biomarkers can monitor cancer progression, for example, lipids (CH2 asymmetric/CH2 symmetric stretch), Amide I/Amide II, and RNA/DNA. Principal component analysis also confirmed the involvement of protein, lipids and nucleic acids in cancer progression on sequential culture. The collective observations from this study suggest successful application of FTIR as a non-invasive and accurate method to identify biochemical changes in cancer cells during the progression of breast cancer bone metastasis.