Photoacoustic Spectroscopy for the Determination of Lung Cancer Biomarkers-A Preliminary Investigation

Considerations regarding the selection, sampling, extraction, analysis, and modelling of biomarkers in exhaled breath for early lung cancer screening

Lung cancer (LC) is the deadliest cancer due to the lack of efficient screening methods that detect the disease early. This review, covering the years 2011 - 2025, summarizes state-of-the-art LC screening through analysis of volatile organic compounds (VOCs) in exhaled breath. All fundamental parts of the methodology are covered, i.e., sampling, analysis, and multivariate data modelling. This review shows that breath is commonly collected in Tedlar® bags and subsequently analysed with solid phase micro-extraction gas chromatography mass spectrometry (SPME-GC-MS) or sensors. Data analysis has been made using multivariate methods like principal component analysis (PCA) or artificial neural networks (ANNs). The VOCs exhaled by LC patients and healthy subjects are in principle the same. However, concentration levels differ between the two groups. Therefore, LC patients are usually separated from healthy controls through multivariate modelling of a set of VOC biomarkers rather than by individual biomarkers. Although most exhaled VOCs are formed endogenously via metabolic processes and oxidative stress, some compounds also have exogenous origins, which must be taken into consideration. More than 200 different VOCs have been reported as potential biomarkers in the breath of LC patients, while the number of biomarkers per study were typically around 10-20 compounds. The 15 most common LC biomarkers were (from high to low frequency) acetone, isoprene, hexanal, benzene, butanone, styrene, ethylbenzene, 1-propanol, 2-propanol, toluene, pentanal, 2-pentanone, cyclohexane, nonanal and decane. Several methods showed, in combination with multivariate data analysis, potential to distinguish between LC patients and healthy controls.

Current Trends and Advances in Nanoplatforms-Based Imaging for Cancer Diagnosis

The intersection of nanotechnology and biomedical imaging has ushered in a new era in the early detection and diagnosis of cancer which has revolutionized biomedical imaging by enhancing sensitivity, resolution, and targeting capability. This review presents a comprehensive overview of the latest developments and innovations in nanoplatforms-based imaging for cancer diagnosis, a burgeoning field that holds significant potential in improving cancer detection and treatment. Recently multimodal imaging techniques utilizing the unique properties of different types of nanoparticles are providing comprehensive diagnostic information. This multi-pronged approach allows for more precise tumor localization, size estimation, and growth rate calculation, offering a holistic view of the tumor and its environment. The primary focus of this review is on the recent progress in various types of nanoparticle-based imaging modalities, including optical, magnetic resonance, ultrasound, and nuclear imaging. Specific advancements in nanomaterial design for targeted imaging are highlighted, showing the improvement of precision targeting as an impact on the detection of cancer cells, even in early-stage tumors. A keen examination on the integration of diagnostic and therapeutic capabilities into single nano-based platforms for theranostics, underscoring their potential in personalized medicine is provided. The current challenges in the field, such as issues related to toxicity, biodistribution, and clearance of nanoparticles, and it explores ongoing research aimed at overcoming these hurdles. The growing body of research in this field highlights the promising future of nanoplatforms in improving the early detection and treatment of cancer.

Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

The State of the Art on Graphene-Based Sensors for Human Health Monitoring through Breath Biomarkers

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.

Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

Helicene-Hydrazide Encapsulated Ethyl Cellulose as a Potential Fluorescence Sensor for Highly Specific Detection of Nonanal in Aqueous Solutions and a Proof-of-Concept Clinical Study in Lung Fluid

Over the past years, lung cancer has been one of the vital cancer-related mortalities worldwide and has inevitably exhibited the highest death rate with the subsequent need for facile and convenient diagnosis approaches to identify the severity of cancer. Previous research has reported long-chain aldehyde compounds such as hexanal, heptanal, octanal, and nonanal as potential biomarkers of lung cancer. Herein, the helicene dye-encapsulated ethyl cellulose (EC@dye-NH) nanosensors have been applied for the potentially sensitive and specific detection of long-chain aldehydes in aqueous media. The sensors contain the intrinsic hydrazide group of dye-NH, which is capable of reacting an aldehyde group via imine formation and the EC backbone. This offers the synergistic forces of hydrophobic interactions with alkyl long-chain aldehydes, which could induce self-assembly encapsulation of EC@dye-NH nanosensors and strong fluorescence responses. The addition of long-chain aldehyde would induce the complete micellar-like nanoparticle formation within 15 min in acetate buffer pH 5.0. The limit of detection (LOD) values of EC@dye-NH nanosensors toward heptanal, octanal, and nonanal were 40, 100, and 10 μM, respectively, without interference from the lung fluid matrices and short-chain aldehydes. For practical applicability, this sensing platform was developed for quantification of the long-chain aldehydes in lung fluid samples with 98-101% recoveries. This EC@dye-NH nanosensor was applied to quantify nonanal contents in lung fluid samples. The results of this method based on EC@dye-NH nanosensors were then validated using standard gas chromatography-mass spectrometry (GC-MS), which gave results consistent with the proposed method. With intracellular imaging application, the EC@dye-NH nanosensors demonstrated excellent intracellular uptake and strong green fluorescence emission upon introducing the nonanal into the lung cancer cells (A549). Thus, the developed nanosensing approach served as the potential fluorescent probes in medical and biological fields, especially for lung cancer disease diagnosis based on highly selective and sensitive detection of long-chain aldehydes.

Types of spectroscopy and microscopy techniques for cancer diagnosis: a review

Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis.

Isotopic gas analysis by means of mid-infrared photoacoustic spectroscopy targeting human exhaled air

There is a great need for cost-efficient non-invasive medical diagnostic tools for analyzing humanly exhaled air. Compared to present day methods, photoacoustic spectroscopy (PAS) can provide a compact and portable (bedside), sensitive and inexpensive solution. We demonstrate a novel portable photoacoustic spectroscopic platform for isotopic measurements of methane (CH₄). We identify and discriminate the ¹²CH₄- and ¹³CH₄ isotopologues and determine their mixing ratio. An Allan deviation analysis shows that the noise equivalent concentration for CH₄ is 200 ppt (pmol/mol) at 100 s of integration time, corresponding to a normalized noise equivalent absorption coefficient of 5.1×10^-9Wcm^-1Hz^-1/2, potentially making the PAS sensor a truly disruptive instrument for bedside monitoring using isotope tracers by providing real-time metabolism data to clinical personnel.

High performance exhaled breath biomarkers for diagnosis of lung cancer and potential biomarkers for classification of lung cancer

Exhaled breath analysis has emerged as a promising non-invasive method for diagnosing lung cancer (LC), whereas reliable biomarkers are lacking. Herein, a standardized and systematic study was presented for LC diagnosis, classification and metabolism exploration. To improve the reliability of biomarkers, a validation group was included, and quality control for breath sampling and analysis, comprehensive pollutants analysis, and strict biomarker screening were performed. The performance of exhaled breath biomarkers was shown to be excellent in diagnosing LC even in early stages (stage I and II) with surpassing 0.930 area under the receiver operating characteristic (ROC) curve (AUC), 90% of sensitivity and 88% of specificity both in the discovery and validation analyses. Meanwhile, in these two groups, diagnosing subtypes of LC attained AUCs over 0.930 and reached 1.00 in the two subtypes of adenocarcinomas. It is demonstrated that the metabolism changes in LC are possibly related to lipid oxidation, gut microbial, cytochrome P450 and glutathione S-transferase, and glutathione pathways change in LC progression. Overall, the reliable biomarkers contribute to the clinical application of breath analysis in screening LC patients as well as those in early stages.

Applications of Near Infrared Photoacoustic Spectroscopy for Analysis of Human Respiration: A Review

In this review, applications of near-infrared photoacoustic spectroscopy are presented as an opportunity to evaluate human respiration because the measurement of breath is fast, intact and simple to implement. Recently, analytical methods for measuring biomarkers in exhaled air have been extensively developed. With laser-based photoacoustic spectroscopy, volatile organic compounds can be identified with high sensitivity, at a high rate, and with very good selectivity. The literature review has shown the applicability of near-infrared photoacoustic spectroscopy to one of the problems of the real world, i.e., human health. In addition, the review will consider and explore different breath sampling methods for human respiration analysis.

Versatile photoacoustic spectrometer based on a mid-infrared pulsed optical parametric oscillator

We demonstrate the usefulness of a nanosecond-pulsed single-mode mid-infrared (MIR) optical parametric oscillator (OPO) for photoacoustic (PA) spectroscopic measurements. The maximum wavelength ranges for the signal and idler are 1.4 μm to 1.7 μm and 2.8 μm to 4.6 μm, respectively, with a MIR output power of up to 500 mW, making the OPO useful for different spectroscopic PA trace-gas measurements targeting the major market opportunity of environmental monitoring and breath gas analysis. We perform spectroscopic measurements of methane (CH₄), nitrogen dioxide (NO₂), and ammonia (NH₃) in the 2.8 μm to 3.7 μm wavelength region. The measurements were conducted with a constant flow rate of 300 mL/min, thus demonstrating the suitability of the gas sensor for real-time trace-gas measurements. The acquired spectra are compared with data from the HITRAN database, and good agreement is found, demonstrating a resolution bandwidth of 1.5  cm¹. An Allan deviation analysis shows that the detection limit for methane at optimum integration time for the PA sensor is 8 ppbV (nmol/mol) at 105 s of integration time, corresponding to a normalized noise equivalent absorption coefficient of 2.9×10^-7  W cm^-1  Hz^-1/2.

Quantitative Evaluation of Broadband Photoacoustic Spectroscopy in the Infrared with an Optical Parametric Oscillator

We evaluate the spectral resolution and the detection thresholds achievable for a photoacoustic spectroscopy (PAS) system in the broadband infrared wavelength region 3270 n m ≲ λ ≲ 3530 n m driven by a continuous wave optical parametric oscillator (OPO) with P ¯ ≈ 1.26 W . The absorption spectra, I PAS ( λ i ) , for diluted propane, ethane and methane test gases at low concentrations ( c ∼ 100 ppm ) were measured for ∼1350 discrete wavelengths λ i . The I PAS ( λ i ) spectra were then compared to the high resolution cross section data, σ FTIR , obtained by Fourier Transform Infrared Spectroscopy published in the HITRAN database. Deviations of 7.1(6)% for propane, 8.7(11)% for ethane and 15.0(14)% for methane with regard to the average uncertainty between I PAS ( λ i ) and the expected reference values based on σ FTIR were recorded. The characteristic absorption wavelengths λ res can be resolved with an average resolution of δ λ res ∼ 0.08 nm . Detection limits range between 7.1 ppb (ethane) to 13.6 ppb (methane). In an additional step, EUREQA, an artificial intelligence (AI) program, was successfully applied to deconvolute simulated PAS spectra of mixed gas samples at low limits of detection. The results justify a further development of PAS technology to support e.g., biomedical research.

Multivariate Analysis as a Tool to Identify Concentrations from Strongly Overlapping Gas Spectra

We applied a multivariate analysis (MVA) to spectroscopic data of gas mixtures in the mid-IR in order to calculate the concentrations of the single components which exhibit strongly overlapping absorption spectra. This is a common challenge in broadband spectroscopy. Photoacoustic (PA) measurements of different volatile organic compounds (VOCs) in the wavelength region of 3250 nm to 3550 nm served as the exemplary detection technique. Partial least squares regression (PLS) was used to calculate concentrations from the PA spectra. After calibration, the PLS model was able to determine concentrations of single VOCs with a relative accuracy of 2.60%.

Photoacoustic Drug Delivery

Photoacoustic (PA) technology holds great potential in clinical translation as a new non-invasive bioimaging modality. In contrast to conventional optical imaging, PA imaging (PAI) enables higher resolution imaging with deeper imaging depth. Besides applications for diagnosis, PA has also been extended to theranostic applications. The guidance of PAI facilitates remotely controlled drug delivery. This review focuses on the recent development of PAI-mediated drug delivery systems. We provide an overview of the design of different PAI agents for drug delivery. The challenges and further opportunities regarding PA therapy are also discussed.

Synergetic Resonance Matching of a Microphone and a Photoacoustic Cell

We propose an approach to match the resonant characteristics of a photoacoustic cell with that of a microphone in order to enhance the signal-to-noise ratio in the photoacoustic sensor system. The synergetic resonance matching of a photoacoustic cell and a microphone was achieved by observing that photoacoustic cell resonance is merged with microphone resonance, in addition to conducting numerical and analytical simulations. Using this approach, we show that the signal-to-noise ratio was increased 3.5-fold from the optimized to non-optimized cell in the photoacoustic spectroscopy system. The present work is expected to have a broad impact on a number of applications, from improving weak photoacoustic signals in photoacoustic spectroscopy to ameliorating various sensors that use acoustic resonant filters.