Cancer Diagnosis by Discrimination between Normal and Malignant Human Blood Samples Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy

Prognostic Features of Near-Infrared Spectroscopy Following Primary Radical Prostatectomy

BACKGROUND

Only a few biomarkers have been evaluated for their prognostic value with regard to biochemical recurrence (BCR) following primary radical prostatectomy. We explored the possibilities of using near-infrared (NIR) spectroscopy as a prognostic biomarker for BCR-free survival (BCR-FS).

METHODS

Tissue specimens from 82 prostate cancer patients were obtained. Formalin-fixed paraffin-embedded slides (hematoxylin-eosin-stained) were analyzed using NIR spectroscopy. Prognostic features for BCR-FS were determined following normalization of the spectra.

RESULTS

Several differences were found throughout the NIR spectrum for the patients with or without BCR, for both the first derivative and second derivative of the NIR spectrum. Following categorization and Cox regression analysis, spectral regions at 5236 cm^-1 (first derivative; median BCR-FS not reached versus 3.2 years; HR_high = 0.18 [0.08-0.39]; and p < 0.0001) and at 5956 cm^-1 (second derivative; median BCR-FS not reached versus 3.8 years; HR_low = 0.22 [0.10-0.48]; and p = 0.0002) showed prognostic properties for BCR-FS. The combination of both parameters further increased the prognostic value of NIR (p < 0.0001).

CONCLUSIONS

We demonstrated NIR spectral variations between patients with or without BCR, which have been shown to have prognostic value. This easy-to-use technique could possibly further improve post-primary radical prostatectomy monitoring and swift referral to adjuvant local therapies. Further elaboration is highly recommended to fully elucidate these variations and to gain a deeper insight into the changing chemical and physical compositions of the prostate tumor architecture.

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.

In situ research and diagnosis of breast cancer by using HOF-ATR-FTIR spectroscopy

To accurately investigate in situ breast cancer would be very significant for real-time information and in situ diagnosis. In this in situ study, home-made hollow optical fiber attenuated total reflection (HOF-ATR) probe was integrated into Fourier transform infrared (FTIR) spectroscopic system to perform breast cancer research at molecular level. Based on the FTIR spectral analysis on band shifts and absorbance ratios, it's disclosed that the molecular structure, conformation and content of main components change with cancerization of breast tissue. Fisher's discriminant analysis on HOF-ATR-FTIR spectra was applied to identify the healthy and cancerous breast tissues for the first time. The identification accuracy was 96.67% for training group and 93.33% for cross-validation, respectively, as well as 95% for the prediction group. This paper provides much in situ information of tissue cancerization at molecular level, which can be used as fingerprint biomarkers of tissue cancerization for in situ diagnosis. HOF-ATR-FTIR spectroscopy with discriminant analysis has potential to be an effective and promising method in in situ biomedical research and monitoring of breast cancer.

FTIR Spectroscopy of Cerebrospinal Fluid Reveals Variations in the Lipid: Protein Ratio at Different Stages of Alzheimer's Disease

Alzheimer's disease (AD) is a disease of advanced civilization and a common form of dementia in people over 65 years of age. We used Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) to determine changes in the quantity and quality of the cerebrospinal fluid from AD patients at three different stages of the disease (ADI, ADII, and ADIII), as well as from patients with mild cognitive impairment (MCI). Moreover, based on the FTIR spectra, we calculated the ratio of α-helix and β-sheet secondary protein structures as well as the lipid-protein balance as potential AD markers. The FTIR spectra of cerebrospinal fluid obtained from MCI, ADI, ADII, and ADIII patients showed that peaks corresponding to protein and deoxyribonucleic acid (DNA), and phospholipid and lipid vibrations were shifted in comparison with those of control subjects. Furthermore, the levels of these chemical compounds were lower in the patients than in the control subjects. The β-sheet secondary protein structure levels were increased in the MCI and AD patients compared with the control subjects. In addition, significant changes in the lipid-protein balance were observed. Interestingly, as the disease progressed, the lipid-protein balance became further disrupted, that is, the lipid amount decreased with disease progression. PCA analysis of lipid-protein FTIR regions revealed that the spectra could be used to distinguish between controls and patients with MCI, ADI, ADII, and ADIII.

Salivary Fingerprinting of Periodontal Disease by Infrared-ATR Spectroscopy

PURPOSE

Periodontal diseases, the most common chronic inflammatory diseases in humans, do not only affect tooth-supporting tissues but also other body parts by contributing to the development of life-threatening conditions. Since currently available diagnostic methods in periodontics lack the ability to identify patients at high risk for periodontal disease progression, development of innovative, non-invasive, rapid detection methods for diagnosing periodontal diseases is needed. This study aims to assess the potential of infrared attenuated total reflection (IR-ATR) spectroscopy to detect differences in composition of saliva supernatant in non-periodontitis individuals (control) and patients with generalized aggressive periodontitis (G-AgP).

EXPERIMENTAL DESIGN

IR-ATR is performed with a wavelength interval from 1230 to 1180 cm^-1 , analyzed with a simple subtraction in absorbance data.

RESULTS

Ten samples show in the analysis of variance of the two data sets a true difference (99.8%). A principal component analysis (PCA) is able to discriminate between G-AgP and control groups.

CONCLUSION AND CLINICAL RELEVANCE

This study demonstrates for the first time that IR-ATR spectroscopy is a promising tool for the analysis of saliva supernatant for the diagnosis of periodontitis, and potentially other periodontal conditions. IR-ATR spectroscopy holds the potential to be miniaturized and utilized as a non-invasive screening test.

N-Linked Glycosylation and Near-Infrared Spectroscopy in the Diagnosis of Prostate Cancer

Background: Performing a prostate biopsy is the most robust and reliable way to diagnose prostate cancer (PCa), and to determine the disease grading. As little to no biochemical markers for prostate tissue exist, we explored the possibilities of tissue N-glycosylation and near-infrared spectroscopy (NIR) in PCa diagnosis. Methods: Tissue specimens from 100 patients (benign prostate hyperplasia (BPH), n = 50; and PCa, n = 50) were obtained. The fresh-frozen tissue was dispersed and a tissue N-glycosylation profile was determined. Consequently, the formalin-fixed paraffin-embedded slides were analyzed using NIR spectroscopy. A comparison was made between the benign and malignant tissue, and between the various Gleason scores. Results: A difference was observed for the tissue of N-glycosylation between the benign and malignant tissue. These differences were located in the fycosylation ratios and the total amount of bi- and tetra-antennary structures (all p < 0.0001). These differences were also present between various Gleason scores. In addition, the NIR spectra revealed changes between the benign and malignant tissue in several regions. Moreover, spectral ranges of 1055–1065 nm and 1450–1460 nm were significantly different between the Gleason scores (p = 0.0042 and p = 0.0195). Conclusions: We have demonstrated biochemical changes in the N-glycan profile of prostate tissue, which allows for the distinction between malignant and benign tissue, as well as between various Gleason scores. These changes can be correlated to the changes observed in the NIR spectra. This could possibly further improve the histological assessment of PCa diagnosis, although further method validation is needed.

MIR-biospectroscopy coupled with chemometrics in cancer studies

This review focuses on chemometric techniques applied in MIR-biospectroscopy for cancer diagnosis and analysis over the last ten years of research. Experimental applications of chemometrics coupled with biospectroscopy are discussed throughout this work. The advantages and drawbacks of this association are also highlighted. Chemometric algorithms are evidenced as a powerful tool for cancer diagnosis, classification, and in different matrices. In fact, it is shown how chemometrics can be implemented along all different types of cancer analyses.

Synergy Effect of Combining Fluorescence and Mid Infrared Fiber Spectroscopy for Kidney Tumor Diagnostics

Matching pairs of tumor and non-tumor kidney tissue samples of four patients were investigated ex vivo using a combination of two methods, attenuated total reflection mid infrared spectroscopy and fluorescence spectroscopy, through respectively prepared and adjusted fiber probes. In order to increase the data information content, the measurements on tissue samples in both methods were performed in the same 31 preselected positions. Multivariate data analysis revealed a synergic effect of combining the two methods for the diagnostics of kidney tumor compared to individual techniques.

Identification of species' blood by attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy

Blood is one of the most common and informative forms of biological evidence found at a crime scene. A very crucial step in forensic investigations is identifying a blood stain's origin. The standard methods currently employed for analyzing blood are destructive to the sample and time-consuming. In this study, attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy is used as a confirmatory, nondestructive, and rapid method for distinction between human and animal (nonhuman) blood. Partial least squares-discriminant analysis (PLS-DA) models were built and demonstrated complete separation between human and animal donors, as well as distinction between three separate species: human, cat, and dog. Classification predictions of unknown blood donors were performed by the model, resulting in 100 % accuracy. This study demonstrates ATR FT-IR spectroscopy's great potential for blood stain analysis and species discrimination, both in the lab and at a crime scene since portable ATR FT-IR instrumentation is commercially available.

Continuous wavelet transform-based feature selection applied to near-infrared spectral diagnosis of cancer

Spectrum is inherently local in nature since it can be thought of as a signal being composed of various frequency components. Wavelet transform (WT) is a powerful tool that partitions a signal into components with different frequency. The property of multi-resolution enables WT a very effective and natural tool for analyzing spectrum-like signal. In this study, a continuous wavelet transform (CWT)-based variable selection procedure was proposed to search for a set of informative wavelet coefficients for constructing a near-infrared (NIR) spectral diagnosis model of cancer. The CWT provided a fine multi-resolution feature space for selecting best predictors. A measure of discriminating power (DP) was defined to evaluate the coefficients. Partial least squares-discriminant analysis (PLS-DA) was used as the classification algorithm. A NIR spectral dataset associated to cancer diagnosis was used for experiment. The optimal results obtained correspond to the wavelet of db2. It revealed that on condition of having better performance on the training set, the optimal PLS-DA model using only 40 wavelet coefficients in 10 scales achieved the same performance as the one using all the variables in the original space on the test set: an overall accuracy of 93.8%, sensitivity of 92.5% and specificity of 96.3%. It confirms that the CWT-based feature selection coupled with PLS-DA is feasible and effective for constructing models of diagnostic cancer by NIR spectroscopy.

Diagnostic prediction of renal failure from blood serum analysis by FTIR spectrometry and chemometrics

A new diagnostic approach based on Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectrometry and classification algorithm has been introduced which provides a rapid, reliable, and easy way to perform blood test for the diagnosis of renal failure. Blood serum samples from 35 renal failure patients and 40 healthy persons were analyzed by ATR-FTIR spectrometry. The resulting data was processed by Quadratic Discriminant Analysis (QDA) and QDA combined with simple filtered method. Spectroscopic studies were performed in 900-2000cm(-)(1) spectral region with 3.85cm(-1) data space. Results showed 93.33% and 100% of accuracy for QDA and filter-QDA models, respectively. In the first step, 30 samples were applied to construct the model. In order to modify the capability of QDA in prediction of test samples, filter-based feature selection methods were applied. It was found that the filtered spectra coupled with QDA could correctly predict the test samples in most of the cases.

Diagnosis of colorectal cancer by near-infrared optical fiber spectroscopy and random forest

Near-infrared (NIR) spectroscopy has such advantages as being noninvasive, fast, relatively inexpensive, and no risk of ionizing radiation. Differences in the NIR signals can reflect many physiological changes, which are in turn associated with such factors as vascularization, cellularity, oxygen consumption, or remodeling. NIR spectral differences between colorectal cancer and healthy tissues were investigated. A Fourier transform NIR spectroscopy instrument equipped with a fiber-optic probe was used to mimic in situ clinical measurements. A total of 186 spectra were collected and then underwent the preprocessing of standard normalize variate (SNV) for removing unwanted background variances. All the specimen and spots used for spectral collection were confirmed staining and examination by an experienced pathologist so as to ensure the representative of the pathology. Principal component analysis (PCA) was used to uncover the possible clustering. Several methods including random forest (RF), partial least squares-discriminant analysis (PLSDA), K-nearest neighbor and classification and regression tree (CART) were used to extract spectral features and to construct the diagnostic models. By comparison, it reveals that, even if no obvious difference of misclassified ratio (MCR) was observed between these models, RF is preferable since it is quicker, more convenient and insensitive to over-fitting. The results indicate that NIR spectroscopy coupled with RF model can serve as a potential tool for discriminating the colorectal cancer tissues from normal ones.

Near-infrared spectroscopy as a diagnostic tool for distinguishing between normal and malignant colorectal tissues

Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spectroscopy with chemometrics. The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA. For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference. Principal component analysis (PCA) was used for a preliminary analysis. A total of 186 spectra from 20 patients with partial colorectal resection were collected and divided into three subsets for training, optimizing, and testing the model. The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm⁻¹) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset. It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

Evaluation of different tissue de-paraffinization procedures for infrared spectral imaging

Differential distribution of paraffin in a normal colon tissue section after various de-Waxing procedures in comparison to a paraffinized tissue.

Evaluation of metformin hydrochloride in Wistar rats by FTIR-ATR spectroscopy: A convenient tool in the clinical study of diabetes

Introduction:

The IR absorption patterns (in cm⁻¹) provide the basis to distinguish among the constituents and to separately quantify as well as qualify them and they possess many advantages such as very small sample volume requirement, good precision over entire physiological range, avoid of costly disposables, wealth of information from a single spectral measurement. The efficacy of anti-diabetic drug metformin hydrochloride as used to treat diabetic-induced Wistar rats and their sera were analyzed by FT-IR (ATR) in absorption mode.

Materials and Methods:

The present work was attempted in the study of normal and antidiabetic regimen-treated rat blood samples using FTIR spectroscopy by the attenuated total reflectance (ATR) sampling technique. The biomolecule characteristics were measured as intensity ratio parameter (IRP) values and interpreted.

Results:

To quantify the results three IRPs such as R1, R2 and R3 were calculated, respectively, for lipid, protein, and glucose. The glucose IRP value R3 showed, 0.3802, 0.3304, and 0.2847, respectively, for diseased, metformin-treated, and normal rats.

Conclusion:

The IRP values for glucose are compared to the glucose level obtained by using a glucometer. This study can be conveniently used in diagnostic procedures, patient compliance assessment, and efficacy evaluation of metformin hydrochlorides.

Genetic algorithm spectral feature selection coupled with quadratic discriminant analysis for ATR-FTIR spectrometric diagnosis of basal cell carcinoma via blood sample analysis

A diagnostic approach for basal cell carcinoma (BCC) has been developed based on investigation of the infrared spectra of blood samples.

Infrared spectral histopathology for cancer diagnosis: a novel approach for automated pattern recognition of colon adenocarcinoma

Automated and label-free colon cancer diagnosis and identification of tumor-associated features using FTIR spectral histopathology directly on paraffinized tissue arrays.

Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer

Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva), with their potential application in clinical practice.

Ensemble modeling coupled with six element concentrations in human blood for cancer diagnosis

Six important metal contents (i.e., zinc, barium, magnesium, calcium, copper, and selenium) in blood samples coupled with an ensemble classification algorithm have been used for the classification of normal people and cancer patients. A dataset containing 42 healthy samples and 32 cancer samples was used for experiment. The prediction results from this method outperformed those from the newly developed support vector machine, i.e., a sensitivity of 100%, a specificity of 95.2%, and an overall accuracy of 98.6%. It seems that ELDA coupled with blood element analysis can serve as a valuable tool for diagnosing cancer in clinical practice.

Classification models for detection of lung cancer based on nine element distribution of urine samples

The detection of lung cancer has a special value in the diagnosis of cancer diseases. Based on nine elemental concentrations (i.e., chromium, iron, manganese, aluminum, cadmium, copper, zinc, nickel, and selenium) in urine samples and an ensemble linear discriminant analysis (ELDA), a detection method for lung cancer has been developed. A dataset containing 30 healthy samples and 27 lung cancer samples is used for experiment. The whole dataset was first split into a training set with 29 samples and a test set with 28 samples. The prediction results from the ELDA classifier were compared with those from single Fisher's discriminate analysis (FDA). On the test set, the ELDA classifier achieved better performance, that is, a sensitivity of 100%, a specificity of 86.7%, and an overall accuracy of 92.9%, while the FDA classifier had a sensitivity of 92.3%, a specificity of 93.3%, and an overall accuracy of 92.9%. The superiority of ELDA to FDA is ascribed to the fact that ELDA can model more nonlinear relationships through the cooperation of several single models, suggesting that ensemble modeling is more advisable in such a task.

Diagnosis of basal cell carcinoma by infrared spectroscopy of whole blood samples applying soft independent modeling class analogy

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was applied to discriminate the blood samples obtained from healthy people and those with basal cell carcinoma, demonstrating high accuracy while soft independent modeling class analogy (SIMCA) chemometric technique is benefited. It was aimed to classify the normal case and cancer case blood samples through the use of ATR-FTIR spectroscopy as a rapid method while the sample preparation is so easy in comparison with the common pathologic methods. A total of 72 blood samples, including 32 cancer and 40 normal cases, were analyzed in 1,800-900 cm(-1) spectral region. Results showed 97.6% of accuracy being compared with the current clinical methods. Research results were exemplified with comparable data of other classification methods such as principal component analysis (PCA) and Cluster analysis. The residual errors in prediction (REP) of calibration model for normal and cancerous groups in SIMCA method were 0.00362 and 0.00343, respectively.