Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women's Health Issue-A Review.

This review focuses on recent advances and future perspectives in the use of Raman spectroscopy for cervical cancer, a global women's health issue. Cervical cancer is the fourth most common women's cancer in the world, and unfortunately mainly affects younger women. However, when detected at the early precancer stage, it is highly treatable. High-quality cervical screening programmes and the introduction of the human papillomavirus (HPV) vaccine are reducing the incidence of cervical cancer in many countries, but screening is still essential for all women. Current gold standard methods include HPV testing and cytology for screening, followed by colposcopy and histopathology for diagnosis. However, these methods are limited in terms of sensitivity/specificity, cost, and time. New methods are required to aid clinicians in the early detection of cervical precancer. Over the past 20 years, the potential of Raman spectroscopy together with multivariate statistical analysis has been shown for the detection of cervical cancer. This review discusses the research to date on Raman spectroscopic approaches for cervical cancer using exfoliated cells, biofluid samples, and tissue ex vivo and in vivo.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm-1, followed by peak normalization at 850 cm-1 and preprocessing by MSC.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity.

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data.

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.

Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties.

OBJECTIVE: To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN: Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. RESULTS: The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). CONCLUSION: RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.

Impact of Illegible Prescriptions on Dispensing Practice: A Pilot Study of South African Pharmacy Personnel.

Illegible prescriptions are an illegal, frequent, and longstanding problem for pharmacy personnel engaged in dispensing. These contribute to patient safety issues and negatively impact safe dispensing in pharmaceutical delivery. To date, little is documented on measures taken to assess the negative impact posed by illegible prescriptions on South African pharmacy dispensing personnel. Therefore, this pilot study was performed to evaluate the ability of pharmacy personnel to read and interpret illegible prescriptions correctly; and to report on their perceived challenges, views and concerns when presented with an illegible prescription to dispense. A cross-sectional, three-tiered self-administered survey was conducted among pharmacy personnel. A total of 885 measurements were recorded. The ability to read an illegible prescription is not an indicator of competency, as all (100%) participants (novice and experienced) made errors and experienced difficulty evaluating and deciphering the illegible prescription. The medication names and dosages were correctly identified by only 20% and 18% of all participants. The use of digital prescriptions was indicated by 70% of the participants as a probable solution to the problem. Overall, improving the quality of written prescriptions and instructions can potentially assist dispensing pharmacy personnel in reducing illegible prescription-related patient safety issues and dispensing errors.

Near-Infrared Spectroscopy Enables Arthroscopic Histologic Grading of Human Knee Articular Cartilage.

Purpose: To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS). Methods: In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (ρ) and root-mean-square error (RMSE). Results: The PCA-LME models performed well (independent test: average ρ = 0.668, RMSE = 0.892, P < .001) in the prediction of the reference measurements based on in vitro data. The performance on ex vivo arthroscopic data was poorer but improved substantially after outlier exclusion (independent test: average ρ = 0.462 to 0.614, RMSE = 1.078 to 0.950, P = .019 to .008). Conclusions: NIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy. Clinical Relevance: There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.

Assessment of Ligament Viscoelastic Properties Using Raman Spectroscopy.

Injuries to the ligaments of the knee commonly impact vulnerable and physically active individuals. These injuries can lead to the development of degenerative diseases such as post-traumatic osteoarthritis (PTOA). Non-invasive optical modalities, such as infrared and Raman spectroscopy, provide means for quantitative evaluation of knee joint tissues and have been proposed as potential quantitative diagnostic tools for arthroscopy. In this study, we evaluate Raman spectroscopy as a viable tool for estimating functional properties of collateral ligaments. Artificial trauma was induced by anterior cruciate ligament transection (ACLT) in the left or right knee joint of skeletally mature New Zealand rabbits. The corresponding contralateral (CL) samples were extracted from healthy unoperated joints along with a separate group of control (CNTRL) animals. The rabbits were sacrificed at 8 weeks after ACLT. The ligaments were then harvested and measured using Raman spectroscopy. A uniaxial tensile stress-relaxation testing protocol was adopted for determining several biomechanical properties of the samples. Partial least squares (PLS) regression models were then employed to correlate the spectral data with the biomechanical properties. Results show that the capacity of Raman spectroscopy for estimating the biomechanical properties of the ligament samples varies depending on the target property, with prediction error ranging from 15.78% for tissue cross-sectional area to 30.39% for stiffness. The hysteresis under cyclic loading at 2 Hz (RMSE = 6.22%, Normalized RMSE = 22.24%) can be accurately estimated from the Raman data which describes the viscous damping properties of the tissue. We conclude that Raman spectroscopy has the potential for non-destructively estimating ligament biomechanical properties in health and disease, thus enhancing the diagnostic value of optical arthroscopic evaluations of ligament integrity.

Articular cartilage optical properties in the near-infrared (NIR) spectral range vary with depth and tissue integrity.

Optical properties of biological tissues in the NIR spectral range have demonstrated significant potential for in vivo diagnostic applications and are critical parameters for modelling light interaction in biological tissues. This study aims to investigate the optical properties of articular cartilage as a function of tissue depth and integrity. The results suggest consistent wavelength-dependent variation in optical properties between cartilage depth-wise zones, as well as between healthy and degenerated tissue. Also, statistically significant differences (p<0.05) in both optical properties were observed between the different cartilage depth-wise zones and as a result of tissue degeneration. When taken into account, the outcome of this study could enable accurate modelling of light interaction in cartilage matrix and could provide useful diagnostic information on cartilage integrity.

Characterization of connective tissues using near-infrared spectroscopy and imaging.

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

Quantitative dual contrast photon-counting computed tomography for assessment of articular cartilage health.

Photon-counting detector computed tomography (PCD-CT) is a modern spectral imaging technique utilizing photon-counting detectors (PCDs). PCDs detect individual photons and classify them into fixed energy bins, thus enabling energy selective imaging, contrary to energy integrating detectors that detects and sums the total energy from all photons during acquisition. The structure and composition of the articular cartilage cannot be detected with native CT imaging but can be assessed using contrast-enhancement. Spectral imaging allows simultaneous decomposition of multiple contrast agents, which can be used to target and highlight discrete cartilage properties. Here we report, for the first time, the use of PCD-CT to quantify a cationic iodinated CA4+ (targeting proteoglycans) and a non-ionic gadolinium-based gadoteridol (reflecting water content) contrast agents inside human osteochondral tissue (n = 53). We performed PCD-CT scanning at diffusion equilibrium and compared the results against reference data of biomechanical and optical density measurements, and Mankin scoring. PCD-CT enables simultaneous quantification of the two contrast agent concentrations inside cartilage and the results correlate with the structural and functional reference parameters. With improved soft tissue contrast and assessment of proteoglycan and water contents, PCD-CT with the dual contrast agent method is of potential use for the detection and monitoring of osteoarthritis.

Infrared Fiber-Optic Spectroscopy Detects Bovine Articular Cartilage Degeneration.

OBJECTIVE: Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis. DESIGN: Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers. RESULTS: All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%. CONCLUSIONS: The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.

A review of multi-responsive membranous systems for rate-modulated drug delivery.

Membrane technology is broadly applied in the medical field. The ability of membranous systems to effectively control the movement of chemical entities is pivotal to their significant potential for use in both drug delivery and surgical/medical applications. An alteration in the physical properties of a polymer in response to a change in environmental conditions is a behavior that can be utilized to prepare 'smart' drug delivery systems. Stimuli-responsive or 'smart' polymers are polymers that upon exposure to small changes in the environment undergo rapid changes in their microstructure. A stimulus, such as a change in pH or temperature, thus serves as a trigger for the release of drug from membranous drug delivery systems that are formulated from stimuli-responsive polymers. This article has sought to review the use of stimuli-responsive polymers that have found application in membranous drug delivery systems. Polymers responsive to pH and temperature have been extensively addressed in this review since they are considered the most important stimuli that may be exploited for use in drug delivery, and biomedical applications such as in tissue engineering. In addition, dual-responsive and glucose-responsive membranes have been also addressed as membranes responsive to diverse stimuli.

Near Infrared Spectroscopy Enables Differentiation of Mechanically and Enzymatically Induced Cartilage Injuries.

This study evaluates the feasibility of near infrared (NIR) spectroscopy to distinguish between different cartilage injury types associated with post-traumatic osteoarthritis and idiopathic osteoarthritis (OA) induced by mechanical and enzymatic damages. Bovine osteochondral samples (n = 72) were subjected to mechanical (n = 24) and enzymatic (n = 36) damage; NIR spectral measurements were acquired from each sample before and after damage, and from a separate control group (n = 12). Biomechanical measurements were then conducted to determine the functional integrity of the samples. NIR spectral variations resulting from different damage types were investigated and the samples classified using partial least squares discriminant analysis (PLS-DA). Partial least squares regression (PLSR) was then employed to investigate the relationship between the NIR spectra and biomechanical properties of the samples. Results of the study demonstrate that substantial spectral changes occur in the region of 1700-2200 nm due to tissue damages, while differences between enzymatically and mechanically induced damages can be observed mainly in the region of 1780-1810 nm. We conclude that NIR spectroscopy, combined with multivariate analysis, is capable of discriminating between cartilage injuries that mimic idiopathic OA and traumatic injuries based on specific spectral features. This information could be useful in determining the optimal treatment strategy during cartilage repair in arthroscopy.

Synchrotron MicroCT Reveals the Potential of the Dual Contrast Technique for Quantitative Assessment of Human Articular Cartilage Composition.

Dual contrast micro computed tomography (CT) shows potential for detecting articular cartilage degeneration. However, the performance of conventional CT systems is limited by beam hardening, low image resolution (full-body CT), and long acquisition times (conventional microCT). Therefore, to reveal the full potential of the dual contrast technique for imaging cartilage composition we employ the technique using synchrotron microCT. We hypothesize that the above-mentioned limitations are overcome with synchrotron microCT utilizing monochromatic X-ray beam and fast image acquisition. Human osteochondral samples (n = 41, four cadavers) were immersed in a contrast agent solution containing two agents (cationic CA4+ and non-ionic gadoteridol) and imaged with synchrotron microCT at an early diffusion time point (2 h) and at diffusion equilibrium (72 h) using two monochromatic X-ray energies (32 and 34 keV). The dual contrast technique enabled simultaneous determination of CA4+ (i.e., proteoglycan content) and gadoteridol (i.e., water content) partitions within cartilage. Cartilage proteoglycan content and biomechanical properties correlated significantly (0.327 < r < 0.736, p < 0.05) with CA4+ partition in superficial and middle zones at both diffusion time points. Normalization of the CA4+ partition with gadoteridol partition within the cartilage significantly (p < 0.05) improved the detection sensitivity for human osteoarthritic cartilage proteoglycan content, biomechanical properties, and overall condition (Mankin, Osteoarthritis Research Society International, and International Cartilage Repair Society grading systems). The dual energy technique combined with the dual contrast agent enables assessment of human articular cartilage proteoglycan content and biomechanical properties based on CA4+ partition determined using synchrotron microCT. Additionally, the dual contrast technique is not limited by the beam hardening artifact of conventional CT systems. © 2019 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 38:563-573, 2020.

Dual contrast in computed tomography allows earlier characterization of articular cartilage over single contrast.

Cationic computed tomography contrast agents are more sensitive for detecting cartilage degeneration than anionic or non-ionic agents. However, osteoarthritis-related loss of proteoglycans and increase in water content contrarily affect the diffusion of cationic contrast agents, limiting their sensitivity. The quantitative dual-energy computed tomography technique allows the simultaneous determination of the partitions of iodine-based cationic (CA4+) and gadolinium-based non-ionic (gadoteridol) agents in cartilage at diffusion equilibrium. Normalizing the cationic agent partition at diffusion equilibrium with that of the non-ionic agent improves diagnostic sensitivity. We hypothesize that this sensitivity improvement is also prominent during early diffusion time points and that the technique is applicable during contrast agent diffusion. To investigate the validity of this hypothesis, osteochondral plugs (d = 8 mm, N = 33), extracted from human cadaver (n = 4) knee joints, were immersed in a contrast agent bath (a mixture of CA4+ and gadoteridol) and imaged using the technique at multiple time points until diffusion equilibrium. Biomechanical testing and histological analysis were conducted for reference. Quantitative dual-energy computed tomography technique enabled earlier determination of cartilage proteoglycan content over single contrast. The correlation coefficient between human articular cartilage proteoglycan content and CA4+ partition increased with the contrast agent diffusion time. Gadoteridol normalized CA4+ partition correlated significantly (P < .05) with Mankin score at all time points and with proteoglycan content after 4 hours. The technique is applicable during diffusion, and normalization with gadoteridol partition improves the sensitivity of the CA4+ contrast agent.

A comparative evaluation of diffuse reflectance and Raman spectroscopy in the detection of cervical cancer.

Optical spectroscopic techniques show improved diagnostic accuracy for non-invasive detection of cervical cancers. In this study, sensitivity and specificity of two in vivo modalities, i.e diffuse reflectance spectroscopy (DRS) and Raman spectroscopy (RS), were compared by utilizing spectra recorded from the same sites (67 tumor (T), 22 normal cervix (C), and 57 normal vagina (V)). Data was analysed using principal component - linear discriminant analysis (PC-LDA), and validated using leave-one-out-cross-validation (LOOCV). Sensitivity, specificity, positive predictive value and negative predictive value for classification between normal (N) and tumor (T) sites were 91%, 96%, 95% and 93%, respectively for RS and 85%, 95%, 93% and 88%, respectively for DRS. Even though DRS revealed slightly lower diagnostic accuracies, owing to its lower cost and portability, it was found to be more suited for cervical cancer screening in low resource settings. On the other hand, RS based devices could be ideal for screening patients with centralised facilities in developing countries.

Raman spectroscopic study of radioresistant oral cancer sublines established by fractionated ionizing radiation.

Radiotherapy is an important treatment modality for oral cancer. However, development of radioresistance is a major hurdle in the efficacy of radiotherapy in oral cancer patients. Identifying predictors of radioresistance is a challenging task and has met with little success. The aim of the present study was to explore the differential spectral profiles of the established radioresistant sublines and parental oral cancer cell lines by Raman spectroscopy. We have established radioresistant sublines namely, 50Gy-UPCI:SCC029B and 70Gy-UPCI:SCC029B from its parental UPCI:SCC029B cell line, by using clinically admissible 2Gy fractionated ionizing radiation (FIR). The developed radioresistant character was validated by clonogenic cell survival assay and known radioresistance-related protein markers like Mcl-1, Bcl-2, Cox-2 and Survivin. Altered cellular morphology with significant increase (p<0.001) in the number of filopodia in radioresistant cells with respect to parental cells was observed. The Raman spectra of parental UPCI:SCC029B, 50Gy-UPCI:SCC029B and 70Gy-UPCI:SCC029B cells were acquired and spectral features indicate possible differences in biomolecules like proteins, lipids and nucleic acids. Principal component analysis (PCA) provided three clusters corresponding to radioresistant 50Gy, 70Gy-UPCI:SCC029B sublines and parental UPCI:SCC029B cell line with minor overlap, which suggest altered molecular profile acquired by the radioresistant cells due to multiple doses of irradiation. The findings of this study support the potential of Raman spectroscopy in prediction of radioresistance and possibly contribute to better prognosis of oral cancer.