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Analytical technologies that can improve disease diagnosis are highly sought after. Current screening/diagnostic tests for several diseases are limited by their moderate diagnostic performance, invasiveness, costly and laborious methodologies or the need for multiple tests before a definitive diagnosis. Spectroscopic techniques, including infrared (IR) and Raman, have attracted great interest in the medical field, with applications expanding from early disease detection to monitoring and real-time diagnosis.\ud This review highlights applications of IR and Raman spectroscopy, with a focus on cancer and infectious diseases since 2015, and underscores the diverse sample types that can be analyzed, such as biofluids, cells and tissues. Studies involving more than 25 participants per group (disease and control group; if no control group >25 in disease group) were considered eligible, to retain the clinical focus of the paper. Following literature searches, we identified 94 spectroscopic studies on different cancers and 30 studies on infectious diseases. The review suggests that such technologies have the potential to develop into an objective, inexpensive, point-of-care test or facilitate disease diagnosis and monitoring. Up-to-date considerations for the implementation of spectroscopic techniques into a clinical setting, health economics and successful applications of vibrational spectroscopic tests in the clinical arena are also discussed.

Simple Summary Due to the non-specific symptoms of brain cancer (e.g., headaches or memory changes), gliomas will often remain undetected until they are larger or at a higher grade, reducing the patient’s likelihood of a good clinical outcome. Earlier detection and diagnosis of brain tumours is vital to improve patient outcomes, leading to safer surgeries and earlier treatments. A liquid biopsy for brain tumour would prove revolutionary however in order to detect disease earlier the liquid biopsy needs to be able to detect smaller tumours; and current liquid biopsies perform worse when detecting smaller or earlier stage tumours. Here, for the first time, we confirm the applicability of a validated spectroscopic liquid biopsy approach to detect both small and low-grade gliomas proving that the spectroscopic liquid biopsy approach is insensitive to tumour volume unlike other liquid biopsies. Abstract Background: To support the early detection and diagnosis of brain tumours we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumour diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumour genetic material) report a lower classification accuracy for early-stage tumours. In this manuscript we present an investigation into the link between brain tumour volume and liquid biopsy test performance. Methods: In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligoastrocytoma and oligodendroglioma)) tumour volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumour patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Results: Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumour volumes as small as 0.2 cm3 were correctly identified. Conclusions: Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis.

Biofluid spectroscopy is an emerging technology in the field of clinical investigation, providing a simple way to extract diagnostic and observational information from easy to acquire samples. Infrared spectroscopy is well suited to analyse a large range of biofluid samples, including blood and its derivatives, due to flexible sampling modes and high sensitivity to subtle biological changes. As the technology advances towards the clinic, factors influencing successful clinical translation are becoming apparent. Here, we provide a tutorial for effective biofluid spectroscopy study design, discussing sample and instrument parameters, as well as clinical considerations. The aim is to present the current understanding of clinical translation in the field of biofluid spectroscopy, and to facilitate other clinical applications to advance to the clinic.

International audience; Changes in protein concentrations within human blood are used as an indicator for nutritional state, hydration and underlying illnesses. They are often measured at regular clinical appointments and the current analytical process can result in long waiting times for results and the need for return patient visits. Attenuated total reflectance – Fourier transform infrared (ATR-FTIR) spectroscopy has the ability to detect minor molecular differences, qualitatively and quantitatively, in biofluid samples, without extensive sample preparation. ATR-FTIR can return an analytical measurement almost instantaneously and therefore could be deemed as an ideal technique for monitoring molecular alterations in blood within the clinic.To determine the suitability of using ATR-FTIR spectroscopy to enable protein quantification in a clinical setting, pooled human serum samples spiked with varying concentrations of human serum albumin (HSA) and immunoglobulin G (IgG) were analysed, before analysing patient clinical samples. Using a validated partial least squares method, the spiked samples (IgG) produced a linearity as high as 0.998 and a RMSEV of 0.49 ± 0.05 mg mL−1, with the patient samples producing R2 values of 0.992 and a corresponding RMSEV of 0.66 ± 0.05 mg mL−1. This claim was validated using two blind testing models, leave one patient out cross validation and k-fold cross validation, achieving optimum linearity and RMSEV values of 0.934 and 1.99 ± 0.79 mg mL−1, respectively.This demonstrates that ATR-FTIR is able to quantify protein within clinically relevant complex matrices and concentrations, such as serum samples, rapidly and with simple sample preparation. The ability to provide a quantification step, along with rapid disease classification, from a spectroscopic signature will aid clinical translation of vibrational spectroscopy to assist with problems currently faced with patient diagnostic pathways.

Early diagnosis of brain tumours is challenging and a major unmet need. Patients with brain tumours most often present with non-specific symptoms more commonly associated with less serious diagnoses, making it difficult to determine which patients to prioritize for brain imaging. Delays in diagnosis affect timely access to treatment, with potential impacts on quality of life and survival. A test to help identify which patients with non-specific symptoms are most likely to have a brain tumour at an earlier stage would dramatically impact on patients by prioritizing demand on diagnostic imaging facilities. This clinical feasibility study of brain tumour early diagnosis was aimed at determining the accuracy of our novel spectroscopic liquid biopsy test for the triage of patients with non-specific symptoms that might be indicative of a brain tumour, for brain imaging. Patients with a suspected brain tumour based on assessment of their symptoms in primary care can be referred for open access CT scanning. Blood samples were prospectively obtained from 385 of such patients, or patients with a new brain tumour diagnosis. Samples were analysed using our spectroscopic liquid biopsy test to predict presence of disease, blinded to the brain imaging findings. The results were compared to the patient’s index brain imaging delivered as per standard care. Our test predicted the presence of glioblastoma, the most common and aggressive brain tumour, with 91% sensitivity, and all brain tumours with 81% sensitivity, and 80% specificity. Negative predictive value was 95% and positive predictive value 45%. The reported levels of diagnostic accuracy presented here have the potential to improve current symptom-based referral guidelines, and streamline assessment and diagnosis of symptomatic patients with a suspected brain tumour., Brennan et al. show a rapid, low-cost blood test can identify which patients with suspected brain tumour to prioritize for diagnostic imaging, reducing time to diagnosis. The test is more than 90% sensitive for the most aggressive tumours. Earlier diagnosis can provide health and economic benefits., Graphical Abstract Graphical Abstract

Objectives An early economic evaluation to inform the translation into clinical practice of a spectroscopic liquid biopsy for the detection of brain cancer. Two specific aims are (1) to update an existing economic model with results from a prospective study of diagnostic accuracy and (2) to explore the potential of brain tumor-type predictions to affect patient outcomes and healthcare costs. Methods A cost-effectiveness analysis from a UK NHS perspective of the use of spectroscopic liquid biopsy in primary and secondary care settings, as well as a cost–consequence analysis of the addition of tumor-type predictions was conducted. Decision tree models were constructed to represent simplified diagnostic pathways. Test diagnostic accuracy parameters were based on a prospective validation study. Four price points (GBP 50-200, EUR 57-228) for the test were considered. Results In both settings, the use of liquid biopsy produced QALY gains. In primary care, at test costs below GBP 100 (EUR 114), testing was cost saving. At GBP 100 (EUR 114) per test, the ICER was GBP 13,279 (EUR 15,145), whereas at GBP 200 (EUR 228), the ICER was GBP 78,300 (EUR 89,301). In secondary care, the ICER ranged from GBP 11,360 (EUR 12,956) to GBP 43,870 (EUR 50,034) across the range of test costs. Conclusions The results demonstrate the potential for the technology to be cost-effective in both primary and secondary care settings. Additional studies of test use in routine primary care practice are needed to resolve the remaining issues of uncertainty—prevalence in this patient population and referral behavior.

Data quality and reproducibility are vital for robust, reliable and consistent diagnostic techniques within clinical environments. Most variance within clinical laboratories occur within the pre-analytical phase, prior to obtaining and analysing data. Herein, we investigate pre-analytical considerations for the spectroscopic analysis of blood serum for the purpose of clinical diagnostics. Variables within sample collection, storage and preparation are explored in order to evaluate their effects on the spectral outcome, including; differences between sample collection tubes, centrifugal speed and time, short and long-term storage conditions, individual analyst technique, sample volumes, environment of sample batches, as well as various drying techniques. Exploratory data analysis using principal component analysis was implemented to unearth spectral variance not immediately observable. Minor spectral variations were observed within each experiment; however, these were not considered significant differences as a result of these varying experimental factors. Variation between different operator techniques when preparing samples was observed, yet can be resolved with appropriate standard operating procedures, regardless of other factors such as sample volume and storage conditions. The findings within this report suggest that experimental variations within a laboratory, or between different laboratories, does not significantly affect the spectral outcome, and with good laboratory practice and careful sample handling results should be consistent and reliable.

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies &gt, 80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients.

Fourier Transform Infrared Spectroscopy (FTIR) has been largely employed by scientific researchers to improve diagnosis and treatment of cancer, using various biofluids and tissues. The technology has proved to be easy to use, rapid and cost-effective for analysis on human blood serum to discriminate between cancer versus healthy control samples. The high sensitivity and specificity achievable during samples classification aided by machine learning algorithms, offers an opportunity to transform cancer referral pathways, as it has been demonstrated in a unique and recent prospective clinical validation study on brain tumours. We herein highlight the importance of early detection in cancer research using FTIR, discussing the technique, the suitability of serum for analysis and previous studies, with special focus on pre-clinical factors and clinical translation requirements and development.

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

In recent years, the application of vibrational spectroscopy in biomedical research has rapidly expanded; covering aspects of pharmaceutical development, to point-of-care technologies. Vibrational spectroscopy techniques such as Fourier-transform IR (FTIR), and Raman spectroscopy have been at the forefront of this movement, with their complementary information able to shine light onto a range of medical applications. As a relative newcomer to biomedical applications, two-dimensional (2D)-IR is also gaining traction in the field. Here we describe the recent development of these techniques as analytical tools in medical science, and their relative advancements towards the clinic.

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Non-specific symptoms, as well as the lack of a cost-effective test to triage patients in primary care, has resulted in increased time-to-diagnosis and a poor prognosis for brain cancer patients. A rapid, cost-effective, triage test could significantly improve this patient pathway. A blood test using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy for the detection of brain cancer, alongside machine learning technology, is advancing towards clinical translation. However, whilst the methodology is simple and does not require extensive sample preparation, the throughput of such an approach is limited. Here we describe the development of instrumentation for the analysis of serum that is able to differentiate cancer and control patients at a sensitivity and specificity of 93.2% and 92.8%. Furthermore, preliminary data from the first prospective clinical validation study of its kind are presented, demonstrating how this innovative technology can triage patients and allow rapid access to imaging., Diagnosing brain cancer is frequently difficult and requires specialist equipment. Here, the authors develop their previous attenuated total reflectance-Fourier transform infrared spectroscopy method and incoporate the use of disposable silicon wafers for diagnosing brain cancer using serum samples.

Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients (3,897 spectra), the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥ 6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.

Simple Summary Gliomas represent the vast majority of primary brain tumours and are of significant medical importance due to the poor clinical course of affected patients. The isocitrate dehydrogenase 1 (IDH1) mutation is associated with improved prognosis, compared to patients with IDH1-wildtype lesions of the same stage. In this proof-of-concept study, Fourier transform infrared spectroscopy was used to determine the IDH1 molecular status in fixed glioma sections. Classification algorithms successfully distinguished the two IDH1 classes with high accuracies (>80%). Knowledge of the IDH1 status would be beneficial, as maximum resection may be preferred in patients with IDH1-mutant gliomas, whilst a more limited resection can be best for IDH1-wildtype gliomas. Furthermore, we examined blood serum in an attempt to identify the biomolecular alterations caused by the IDH1 mutation. Non-invasive approaches that can detect the molecular status may guide some patients to an alternative treatment prior to surgery. Abstract Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

The complex patterns observed from evaporated liquid drops have been examined extensively over the last 20 years. Complete understanding of drop deposition is vital in many medical processes, and one which is essential to the translation of biofluid spectroscopic disease diagnostics. The promising use of spectroscopy in disease diagnosis has been hindered by the complicated patterns left by dried biological fluids which may inhibit the clinical translation of this technology. Coffee-ring formation, cracking and gelation patterns have all been observed in biofluid drops, and with surface homogeneity being a key element to many spectroscopic techniques, experimental issues have been found to arise. A better understanding of the fundamental processes involved in a drying droplet could allow efficient progression in this research field, and ultimately benefit the population with the development of a reliable cancer diagnostic.

By 2050, it is estimated that the global population will have surpassed 9 billion people, presenting a significant challenge with regards to food security. In order to provide sufficient quantities of nutritious food in the future, it is necessary to improve agricultural productivity by up to 70%. Nutrient deficiencies are one particular threat to food security that can have a negative impact on crop yield and quality. Currently the standard agricultural approach to prevention is to supply an excess of macronutrient fertiliser, such as nitrate or phosphate, during crop production. However, the efficiency of this approach is poor as deficiencies of specific nutrients, such as Ca, are not prevented in this circumstance, and fertiliser use is associated with a host of adverse environmental impacts. Herein, we describe a novel method to detect Ca deficiency using synchrotron radiation-based Fourier-transform infrared (FTIR) microspectroscopy in live and fixed tissue of the model plant Commelina communis, as a precursor to targeted nutrient remediation in the field.

Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons’ visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.