Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method

Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds

Rapid identification of soybean seed varieties is crucial for agricultural production and seed quality. Identifying varieties of soybean seed using conventional chemical methods is time-consuming, destructive, and inappropriate for seed quality evaluation. This study utilized hyperspectral imaging technology (HSI) to identify four varieties of soybean seeds. The hyperspectral images of soybean seeds were collected in the spectral range of 400-1000 nm. A multi-level data fusion strategy based on spectral and image information was proposed to improve the accuracy of model. Subsequently, the multi-level data fusion strategy based on partial least squares discriminant analysis (PLS-DA) was used to establish the classification models of soybean seeds. Compared with the models using individual analytical sources, the results demonstrated that the models with multi-level data fusion strategy obtained better prediction performance. The high-level data fusion (HLDF) based on Bayesian consensus provided the optimal results with an accuracy (Acc) and F1-score of 93.13 % and 93.70 % in the prediction phase, respectively. Therefore, the multi-level data fusion strategy can be used as an identification method for soybean seed varieties and an effective approach to enhance the discriminatory capability of models.

Classification of idiopathic recurrent spontaneous miscarriage using FTIR and Raman spectroscopic fusion technology

Recurrent spontaneous miscarriage refers to the repeated loss of two or more clinically detected pregnancies occurring within 24 weeks of gestation. No identifiable cause has been identified for nearly 50% of these cases. This group is referred to as idiopathic recurrent spontaneous miscarriage (IRSM) or miscarriage of unknown origin. Due to lack of robust scientific evidence, guidelines on the diagnosis and management of IRSM are not well defined and often contradictory. This motivates us to explore the vibrational fingerprints of endometrial tissue in these women. Endometrial tissues were collected from women undergoing IRSM (n = 20) and controls (n = 20) corresponding to the window of implantation. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectra were obtained within the range of 400-4000 cm-1 using Agilent Cary 630 FTIR spectrometer. Raman spectra were also generated within the spectral window of 400-4000 cm-1 using Thermo Fisher Scientific, DXR Raman spectrophotometer. Based on the limited molecular information provided by a single spectroscopic tool, fusion strategy combining Raman and ATR-FTIR spectroscopic data of IRSM is proposed. The significant features were extracted applying principal component analysis (PCA) and wavelet threshold denoising (WTD) and fused spectral data used as input into support vector machine (SVM), adaptive boosting (AdaBoost) and decision tree (DT) models. Altered molecular vibrations associated with proteins, glutamate, and lipid metabolism were observed in IRSM using Raman spectroscopy. FTIR analysis indicated changes in the molecular vibrations of lipids and proteins, collagen dysregulation and impaired glucose metabolism. Combination of both spectroscopic data using mid-level fusion (MLF: 92% using AdaBoost and DT models) and high-level fusion (HLF: 92% using SVM models) methods showed improved IRSM classification accuracy as compared to individual spectral models. Our results indicate that spectral fusion technology hold promise in enhancing diagnostic accuracy of IRSM in clinical settings. Validation of these findings in a larger patient population is underway.

The application of vibrational spectroscopy in forensic analysis of biological evidence

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

SLE diagnosis research based on SERS combined with a multi-modal fusion method

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

FTIR spectroscopy for assessment of hair from lung cancer patients and its application in monitoring the chemotherapy treatment effect

Lung cancer is the most common cancer and the leading cause of death in China. The current gold standard for clinical lung cancer diagnosis is based on histopathological examination of tumors, but it has the limitation for easy operation and convenient applications. Therefore, researchers are still striving to develop other tools and methods for non-invasive and rapid assessment of the health conditions of lung cancer patients. Hair, as a reflection of the metabolism of the body, is closely related to human health conditions. In principle, Fourier-transform infrared (FTIR) spectroscopy can probe the major chemical compositions in the hair. However, as indicated by previous studies, there is still the challenge to make good use of FTIR spectroscopy for achieving reliable analysis of hair from cancer patients. In this study, hair samples from 82 lung cancer patients were collected and subjected to FTIR measurements and analysis, which showed the protein content in the hair is closely related to the protein content in the blood serum of patients, and the contents of protein and lipid are statistically lower in the lung cancer patients. Furthermore, we demonstrated that FTIR spectroscopy could be employed to monitor the hair of lung cancer patients undergoing chemotherapy, and confirmed that the FTIR spectra of the hair may reflect the resultant effect of the chemotherapy. As such, this work validates the way of using FTIR spectroscopy in hair analysis for the assistance of medical diagnosis of lung cancer as well as monitoring the conditions of the patients under the medical treatment.

Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association

In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43- ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32- ν1- PO43- ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.

Comparison of serum from lung cancer patients and from patients with benign lung nodule using FTIR spectroscopy

Lungcancer remains the leading cause of cancer related deaths in worldwide. Earlydiagnosis oflungcancer can significantly improve survival rate. However, due to its close resemblance to the malignant nodules, the possible existence of benign nodules often leads to erroneous decisions. The aim of this study was to explore whether fourier transform infrared (FTIR) spectroscopy could improve the accuracy of early diagnosis of lung cancer by distinguishing lung cancer patients' (LCP') serum from patients with benign lung nodules' (PBLN') serum. In this study, A1243+1081/A1652+1539 ratio in LCP group was increased significantly compared with that in PBLN group, indicating that the ratio could be used to distinguish the serum of LCP from that of PBLN. In addition, the ratios of A2926/A2969, A1744/A2926+2859, A2926+2859/A1652+1539 were also increased significantly in LCP group compared with that in PBLN group. These findings suggest that FTIR spectroscopy might be a potentially effective method for the early diagnosis of lung cancer.

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

It is well known that proteins are important bio-macromolecules in human organisms, and numerous proteins are widely used in the clinical practice, whereas their application in forensic science is currently limited. This limitation is mainly attributed to the postmortem degradation of targeted proteins, which can significantly impact final conclusions. In the last decade, numerous methods have been established to detect the protein from a forensic perspective, and some of the postmortem proteins have been applied in forensic practice. To better understand the emerging issues and challenges in postmortem proteins, we have reviewed the current application of protein technologies at postmortem in forensic practice. Meanwhile, we discuss the application of proteins in identifying the cause of death, and postmortem interval (PMI). Finally, we highlight the interpretability and limitations of postmortem protein challenges. We believe that utilizing the multi-omics method can enhance the comprehensiveness of applying proteins in forensic practice.

Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease

Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.

Elucidating binding mechanisms of naringenin by alpha-chymotrypsin: Insights into non-binding interactions and complex formation

As an inevitable parameter in the description of enzyme properties, the investigation of enzyme-ligand interactions has attracted a lot of attention. Alpha-Chymotrypsin (α-Chy) is essential for protein digestion and plays an important role in human health. Naringenin (NAG) as a potent antioxidant has recently been applied in the pharmaceutical industry. Using multispectral methods and computational simulation techniques, the binding strength of NAG to α-Chy was investigated in this research. UV-vis and fluorescence quenching data showed significant spectral changes upon binding of NAG to α-Chy. As demonstrated by fluorescence techniques, NAG could employ a static quenching process to decrease the intrinsic fluorescence of α-Chy. Both circular dichroism (CD) and FTIR spectroscopic analyses revealed that binding of NAG to α-Chy caused more flexible conformation. The slight increases in RMSD (0.06 nm) were observed for the NAG-(α-Chy) compound was supported by the results of thermal stability data. Docking computation confirmed that hydrogen and Van der Waals interactions are the important forces, which is in exact agreement with thermodynamics studies. Kinetic analysis of the enzyme showed an increase in activity, which was consistent, with the MD simulation results. The findings from the in-silico studies were in complete agreement with the experimental results.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

Multi-scale sequential feature selection for disease classification using Raman spectroscopy data

Raman spectroscopy (RS) optical technology promises non-destructive and fast application in medical disease diagnosis in a single step. However, achieving clinically relevant performance levels remains challenging due to the inability to search for significant Raman signals at different scales. Here we propose a multi-scale sequential feature selection method that can capture global sequential features and local peak features for disease classification using RS data. Specifically, we utilize the Long short-term memory network (LSTM) module to extract global sequential features in the Raman spectra, as it can capture long-term dependencies present in the Raman spectral sequences. Meanwhile, the attention mechanism is employed to select local peak features that were ignored before and are the key to distinguishing different diseases. Experimental results on three public and in-house datasets demonstrate the superiority of our model compared with state-of-the-art methods for RS classification. In particular, our model achieves an accuracy of 97.9 ± 0.2% on the COVID-19 dataset, 76.3 ± 0.4% on the H-IV dataset, and 96.8 ± 1.9% on the H-V dataset.

Machine learning applications for early detection of esophageal cancer: a systematic review

INTRODUCTION

Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC.

METHODS

We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review.

RESULTS

The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods.

CONCLUSION

Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization

Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

CNN-LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy

Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN-LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN-LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN-LSTM neural network combined with IR spectroscopy helps to identify different pasta products.

Fourier transform infrared spectroscopic marker of glioblastoma ob-tained from machine learning and changes in the spectra

BACKGROUND

Glioblastoma is the most malignant brain cancer with an average survival rate of 5 years. In neurosurgical practice, it is impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue.

METHODS

Herein, the features of absorbance at specific wavenumbers considered characteristic of glioblastoma tissues could be markers of this cancer. We used Fourier transform infrared spectroscopy to measure the spectra of tissues collected from control and patients suffering from glioblastoma.

RESULTS

The spectrum obtained from glioblastoma tissues demonstrated an additional peak at 1612 cm^-1 and a shift of peaks at 1675 cm^-1 and 1637 cm^-1. Deconvolution of amide I vibrations showed that in the glioblastoma tissue, the percentage amount of β-sheet is around 20% higher than that in the control. Moreover, the principal component analysis showed that using fingerprint and amide I regions it is possible to distinguish cancer and non-cancer samples. Machine learning methods presented that the accuracy of the results is around 100%. Finally, analysis of the differences in the rate of change of Fourier transform infrared spectroscopy spectra showed that absorbance features between 1053 cm^-1 and 1056 cm^-1 as well as between 1564 cm^-1 and 1588 cm^-1 are characteristic of glioblastoma.

CONCLUSION

Calculated features of absorbance at specific wavenumbers could be used as a spectroscopic marker of glioblastoma which may be useful in the future for neuronavigation.

Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy

BACKGROUND

Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression. Raman spectroscopy (RS), a vibrational spectroscopic label-free technique, has already shown promise in cancer diagnostics.

METHODS

RS was combined with machine learning to discriminate glioma grades. Raman spectral signatures of glycosylation patterns were used in serum samples and fixed tissue biopsy samples, as well as in single cells and spheroids.

RESULTS

Glioma grades in fixed tissue patient samples and serum were discriminated with high accuracy. Discrimination between higher malignant glioma grades (III and IV) was achieved with high accuracy in tissue, serum, and cellular models using single cells and spheroids. Biomolecular changes were assigned to alterations in glycosylation corroborated by analysing glycan standards and other changes such as carotenoid antioxidant content.

CONCLUSION

RS combined with machine learning could pave the way for more objective and less invasive grading of glioma patients, serving as a useful tool to facilitate glioma diagnosis and delineate biomolecular glioma progression changes.

Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma

Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF₂) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma.