Modular Design in Triboelectric Sensors: A Review on the Clinical Applications for Real-Time Diagnosis.

Triboelectric nanogenerators (TENGs) have garnered considerable interest as a promising technology for energy harvesting and stimulus sensing. While TENGs facilitate the generation of electricity from micro-motions, the modular design of TENG-based modular sensing systems (TMSs) also offers significant potential for powering biosensors and other medical devices, thus reducing dependence on external power sources and enabling biological processes to be monitored in real time. Moreover, TENGs can be customised and personalized to address individual patient needs while ensuring biocompatibility and safety, ultimately enhancing the efficiency and security of diagnosis and treatment. In this review, we concentrate on recent advancements in the modular design of TMSs for clinical applications with an emphasis on their potential for personalised real-time diagnosis. We also examine the design and fabrication of TMSs, their sensitivity and specificity, and their capabilities of detecting biomarkers for disease diagnosis and monitoring. Furthermore, we investigate the application of TENGs to energy harvesting and real-time monitoring in wearable and implantable medical devices, underscore the promising prospects of personalised and modular TMSs in advancing real-time diagnosis for clinical applications, and offer insights into the future direction of this burgeoning field.

Diagnosis of pharyngeal cancer on endoscopic video images by Mask region-based convolutional neural network.

OBJECTIVES: We aimed to develop an artificial intelligence (AI) system for the real-time diagnosis of pharyngeal cancers. METHODS: Endoscopic video images and still images of pharyngeal cancer treated in our facility were collected. A total of 4559 images of pathologically proven pharyngeal cancer (1243 using white light imaging and 3316 using narrow-band imaging/blue laser imaging) from 276 patients were used as a training dataset. The AI system used a convolutional neural network (CNN) model typical of the type used to analyze visual imagery. Supervised learning was used to train the CNN. The AI system was evaluated using an independent validation dataset of 25 video images of pharyngeal cancer and 36 video images of normal pharynx taken at our hospital. RESULTS: The AI system diagnosed 23/25 (92%) pharyngeal cancers as cancers and 17/36 (47%) non-cancers as non-cancers. The transaction speed of the AI system was 0.03 s per image, which meets the required speed for real-time diagnosis. The sensitivity, specificity, and accuracy for the detection of cancer were 92%, 47%, and 66% respectively. CONCLUSIONS: Our single-institution study showed that our AI system for diagnosing cancers of the pharyngeal region had promising performance with high sensitivity and acceptable specificity. Further training and improvement of the system are required with a larger dataset including multiple centers.

In vivo real-time diagnosis of endoscopic ultrasound-guided needle-based confocal laser endomicroscopy in gastric subepithelial lesions.

BACKGROUND AND AIM: The effect of real-time analysis of needle-based confocal laser endomicroscopy (nCLE) for gastric subepithelial lesions (SELs) on the diagnostic value is unclear. The study aimed to investigate the diagnostic efficacy of real-time nCLE for gastric SELs and to assess the technical aspects and safety of real-time nCLE. METHODS: Consecutive patients with gastric SELs ≥ 1 cm were prospectively investigated by endoscopic ultrasound (EUS), followed by nCLE. During EUS-nCLE, real-time nCLE diagnosis was made by an expert endoscopist. The procedure-relative adverse events were assessed and recorded. One-month washout period later, nCLE videos were reviewed off-line by the same endoscopist. The nCLE diagnoses were compared with corresponding pathological results. Additionally, image quality and interobserver agreements for the criteria were evaluated by three experienced endomicroscopists. RESULTS: Except for one failing to be punctured, 60 patients completed EUS-nCLE procedures successfully. Real-time nCLE had high diagnostic accuracies of ≥ 88.3% for gastric SELs. There were no significant differences between real-time and off-line nCLE diagnoses for gastric SELs (P > 0.05). The overall accuracy of real-time nCLE for diagnosis of gastric SELs was 86.7%. There were no procedure-relative adverse events occurred. In addition, the mean image quality score was 3.6 (1 = poor and 5 = excellent). The interobserver agreement was "almost perfect" for ectopic pancreas and "substantial" for gastrointestinal stromal tumor, leiomyoma, and carcinoma. CONCLUSIONS: Endoscopic ultrasound-nCLE could provide in vivo real-time diagnostic imaging with a high diagnostic accuracy. Meanwhile, real-time nCLE was feasible and had a satisfactory safety profile.

Detection of thrombosis in a magnetically levitated blood pump by vibrational excitation of the impeller.

The use of contactless support technology for the impeller has led to an increase in the durability of ventricular assist devices (VADs), and these have been in clinical use worldwide. However, pump thrombosis and stroke are still issues to be solved. We have developed a method for detecting the thrombosis in a magnetically levitated blood pump without the need for additional sensors or other equipment. In the proposed method, a sinusoidal current is applied to the electromagnets used for the magnetic bearing, resulting in vibration of the impeller. The phase difference between the current and displacement of the impeller increases with pump thrombosis. First, we describe the principle by which the pump thrombosis is detected. Pump thrombosis reduces the narrowest fluid gap in the pump and this gives rise to a change in the phase difference. Second, we report on experiments in which we changed the narrowest fluid gap using oriented polypropylene tape and showed that decreasing the narrowest fluid gap resulted in an increase in phase difference. For these experiments, the measurements were repeated three times for each condition. Third, we examine the relationship between the pump thrombosis and the phase difference evaluated by observations of the underside of the impeller when operating the pump with porcine blood. Since light was unable to penetrate the blood layer, the erythrocytes were removed for this observation. Only one observation was made. The results showed the phase difference rapidly increased at the same moment when the pump thrombosis was observed. This implies the proposed method has the potential to detect the early stages of pump thrombosis. Finally, in vitro experiments to detect thrombosis when using whole porcine blood in the pump were conducted. The experiment was carried out five times. To intentionally form a thrombus inside the pump, the activated clotting time was controlled to be less than 200 s. In every case, the phase difference increased by more than one degree after tens of minutes. Then, the pump was disassembled and a small amount of pump thrombosis was observed. We conclude that real-time diagnosis of pump thrombosis may be realized by measuring the phase difference without the need for additional sensors.

Noninvasive assessment of oral lesions using elastic light single-scattering spectroscopy: a pilot study.

PURPOSE: In the present study, we investigated the potential application of elastic light single-scattering spectroscopy (ELSSS) as a noninvasive, adjunctive tool to differentiate between malignant and benign oral lesions in vivo. METHODS: ELSSS spectra were acquired from 52 oral lesions of 47 patients prior to surgical biopsy using a single optical fiber probe. The sign of the spectral slope was used as a diagnostic parameter and was compared to the histopathology findings to obtain sensitivity and specificity of the ELSSS system in differentiating between benign and malignant tissues. RESULTS: The sign of the spectral slope was positive for the benign tissues and negative for the malignant tissues. Nine malignant lesions and one high-grade dysplasia were correctly classified as cancerous. Six out of the ten low-grade dysplasia were correctly classified as cancerous, and four of them were misclassified as benign. Thirty benign lesions were correctly classified as benign, and two were misclassified as malignant. Our results indicate that the sign of the spectral slope enables the differentiation between malignant and benign oral lesions with a sensitivity of 80% and specificity of 94%. CONCLUSIONS: ELSSS has the potential to be developed as an adjunctive screening tool in the noninvasive evaluation of oral lesions in vivo. This new diagnostic system may reduce the number of unnecessary biopsies.

Preclinical ex vivo evaluation of the diagnostic performance of a new device for in situ label-free fluorescence spectral analysis of breast masses.

OBJECTIVES: To assess the diagnostic performance of a new device for in situ label-free fluorescence spectral analysis of breast masses in freshly removed surgical specimens, in preparation for its clinical development. METHODS: Sixty-four breast masses from consenting patients who had undergone either a lumpectomy or a mastectomy were included. Label-free fluorescence spectral acquisitions were obtained with a 25G fibre-containing needle inserted into the mass. Data from benign and malignant masses were compared to establish the most discriminating thresholds and measurement algorithms. Accuracy was verified using the bootstrap method. RESULTS: The final histological examination revealed 44 invasive carcinomas and 20 benign lesions. The maximum intensity of fluorescence signal was discriminant between benign and malignant masses (p < .0001) whatever their sizes. Statistical analysis indicated that choosing five random measurements per mass was the best compromise to obtain high sensitivity and high negative predictive value with the fewest measurements. Thus, malignant tumours were identified with a mean sensitivity, specificity, negative and positive predictive value of 98.8%, 85.4%, 97.2% and 93.5%, respectively. CONCLUSION: This new in situ tissue autofluorescence evaluation device allows accurate discrimination between benign and malignant breast masses and deserves clinical development. KEY POINTS: • A new device allows in situ label-free fluorescence analysis of ex vivo breast masses • Maximum fluorescence intensity discriminates benign from malignant masses (p < .0001) • Five random measurements allow a high negative predictive value (97.2%).

Molecular typing of Meningiomas by Desorption Electrospray Ionization Mass Spectrometry Imaging for Surgical Decision-Making.

Meningiomas are the most frequent intracranial tumors. The majority is benign slow-growing tumors but they can be difficult to treat depending on their location and size. While meningiomas are well delineated on magnetic resonance imaging by their uptake of contrast, surgical limitations still present themselves from not knowing the extent of invasion of the dura matter by meningioma cells. The development of tools to characterize tumor tissue in real or near real time could prevent recurrence after tumor resection by allowing for more precise surgery, i.e. removal of tumor with preservation of healthy tissue. The development of ambient ionization mass spectrometry for molecular characterization of tissue and its implementation in the surgical decision-making workflow carry the potential to fulfill this need. Here, we present the characterization of meningioma and dura mater by desorption electrospray ionization mass spectrometry to validate the technique for the molecular assessment of surgical margins and diagnosis of meningioma from surgical tissue in real-time. Nine stereotactically resected surgical samples and three autopsy samples were analyzed by standard histopathology and mass spectrometry imaging. All samples indicated a strong correlation between results from both techniques. We then highlight the value of desorption electrospray ionization mass spectrometry for the molecular subtyping/subgrouping of meningiomas from a series of forty genetically characterized specimens. The minimal sample preparation required for desorption electrospray ionization mass spectrometry offers a distinct advantage for applications relying on real-time information such as surgical decision-making. The technology here was tested to distinguish meningioma from dura mater as an approach to precisely define surgical margins. In addition we classify meningiomas into fibroblastic and meningothelial subtypes and more notably recognize meningiomas with NF2 genetic aberrations.

Real-time artificial intelligence-based texture analysis of muscle ultrasound data for neuromuscular disorder assessment.

Objective: Many artificial intelligence approaches to muscle ultrasound image analysis have not been implemented on usable devices in clinical neuromuscular medicine practice, owing to high computational demands and lack of standardised testing protocols. This study evaluated the feasibility of using real-time texture analysis to differentiate between various pathological conditions. Methods: We analysed 17,021 cross-sectional ultrasound images of the biceps brachii of 75 participants, including 25 each with neurogenic disorders, myogenic disorders, and healthy controls. The size and location of the regions of interest were randomly selected to minimise bias. A random forest classifier utilising texture features such as Dissimilarity and Homogeneity was developed and deployed on a mobile PC, enabling real-time analysis. Results: The classifier distinguished patients with an accuracy of 81 %. Echogenicity and Contrast from the Co-Occurrence Matrix were significant predictive features. Validation on 15 patients achieved accuracies of 78 %/93 % per image/patient over 15-second videos, respectively. The use of a mobile PC facilitated real-time estimation of the underlying pathology during ultrasound examination, without influencing procedures. Conclusions: Real-time automatic texture analysis is feasible as an adjunct for the diagnosis of neuromuscular disorders. Significance: Artificial intelligence using texture analysis with a light computational load supports the semi-quantitative evaluation of neuromuscular ultrasound.

Corrigendum: Clinical feasibility of miniaturized Lissajous scanning confocal laser endomicroscopy for indocyanine green-enhanced brain tumor diagnosis.

[This corrects the article DOI: 10.3389/fonc.2022.994054.].

LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis.

BACKGROUND AND OBJECTIVES: Dynamic handwriting analysis, due to its noninvasive and readily accessible nature, has emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease (PD). An essential step involves analysing subtle variations in signals to quantify PD dysgraphia. Although previous studies have explored extracting features from the overall signal, they may ignore the potential importance of local signal segments. In this study, we propose a lightweight network architecture to analyse dynamic handwriting signal segments of patients and present visual diagnostic results, providing an efficient diagnostic method. METHODS: To analyse subtle variations in handwriting, we investigate time-dependent patterns in local representation of handwriting signals. Specifically, we segment the handwriting signal into fixed-length sequential segments and design a compact one-dimensional (1D) hybrid network to extract discriminative temporal features for classifying each local segment. Finally, the category of the handwriting signal is fully diagnosed through a majority voting scheme. RESULTS: The proposed method achieves impressive diagnostic performance on the new DraWritePD dataset (with an accuracy of 96.2%, sensitivity of 94.5% and specificity of 97.3%) and the well-established PaHaW dataset (with an accuracy of 90.7%, sensitivity of 94.3% and specificity of 87.5%). Moreover, the network architecture stands out for its excellent lightweight design, occupying a mere 0.084M parameters, with only 0.59M floating-point operations. It also exhibits nearly real-time CPU inference performance, with the inference time for a single handwriting signal ranging from 0.106 to 0.220 s. CONCLUSIONS: We present a series of experiments with extensive analysis, which systematically demonstrate the effectiveness and efficiency of the proposed method in quantifying dysgraphia for a precise diagnosis of PD.

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

Determination of esophageal squamous cell carcinoma and gastric adenocarcinoma on raw tissue using Raman spectroscopy.

BACKGROUND: Cancer detection is a global research focus, and novel, rapid, and label-free techniques are being developed for routine clinical practice. This has led to the development of new tools and techniques from the bench side to routine clinical practice. In this study, we present a method that uses Raman spectroscopy (RS) to detect cancer in unstained formalin-fixed, resected specimens of the esophagus and stomach. Our method can record a clear Raman-scattered light spectrum in these specimens, confirming that the Raman-scattered light spectrum changes because of the histological differences in the mucosal tissue. AIM: To evaluate the use of Raman-scattered light spectrum for detecting endoscop-ically resected specimens of esophageal squamous cell carcinoma (SCC) and gastric adenocarcinoma (AC). METHODS: We created a Raman device that is suitable for observing living tissues, and attempted to acquire Raman-scattered light spectra in endoscopically resected specimens of six esophageal tissues and 12 gastric tissues. We evaluated formalin-fixed tissues using this technique and captured shifts at multiple locations based on feasibility, ranging from six to 19 locations 200 microns apart in the vertical and horizontal directions. Furthermore, a correlation between the obtained Raman scattered light spectra and histopathological diagnosis was performed. RESULTS: We successfully obtained Raman scattered light spectra from all six esophageal and 12 gastric specimens. After data capture, the tissue specimens were sent for histopathological analysis for further processing because RS is a label-free methodology that does not cause tissue destruction or alterations. Based on data analysis of molecular-level substrates, we established cut-off values for the diagnosis of esophageal SCC and gastric AC. By analyzing specific Raman shifts, we developed an algorithm to identify the range of esophageal SCC and gastric AC with an accuracy close to that of histopathological diagnoses. CONCLUSION: Our technique provides qualitative information for real-time morphological diagnosis. However, further in vivo evaluations require an excitation light source with low human toxicity and large amounts of data for validation.

Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks.

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising approach for enhancing DR diagnosis. OCT provides detailed retinal morphology information, while CNNs analyze OCT images for automated detection and classification of DR. This paper reviews the current research on OCT imaging and CNNs for DR diagnosis, discussing their technical aspects and suitability. It explores CNN applications in detecting lesions, segmenting microaneurysms, and assessing disease severity, showing high sensitivity and accuracy. CNN models outperform traditional methods and rival expert ophthalmologists' results. However, challenges such as dataset availability and model interpretability remain. Future directions include multimodal imaging integration and real-time, point-of-care CNN systems for DR screening. The integration of OCT imaging with CNNs has transformative potential in DR diagnosis, facilitating early intervention, personalized treatments, and improved patient outcomes. Abbreviations: DR = Diabetic Retinopathy, OCT = Optical Coherence Tomography, CNN = Convolutional Neural Network, CMV = Cytomegalovirus, PDR = Proliferative Diabetic Retinopathy, AMD = Age-Related Macular Degeneration, VEGF = vascular endothelial growth factor, RAP = Retinal Angiomatous Proliferation, OCTA = OCT Angiography, AI = Artificial Intelligence.

Clinical feasibility of miniaturized Lissajous scanning confocal laser endomicroscopy for indocyanine green-enhanced brain tumor diagnosis.

Background: Intraoperative real-time confocal laser endomicroscopy (CLE) is an alternative modality for frozen tissue histology that enables visualization of the cytoarchitecture of living tissues with spatial resolution at the cellular level. We developed a new CLE with a "Lissajous scanning pattern" and conducted a study to identify its feasibility for fluorescence-guided brain tumor diagnosis. Materials and methods: Conventional hematoxylin and eosin (H&E) histological images were compared with indocyanine green (ICG)-enhanced CLE images in two settings (1): experimental study with in vitro tumor cells and ex vivo glial tumors of mice, and (2) clinical evaluation with surgically resected human brain tumors. First, CLE images were obtained from cultured U87 and GL261 glioma cells. Then, U87 and GL261 tumor cells were implanted into the mouse brain, and H&E staining was compared with CLE images of normal and tumor tissues ex vivo. To determine the invasion of the normal brain, two types of patient-derived glioma cells (CSC2 and X01) were used for orthotopic intracranial tumor formation and compared using two methods (CLE vs. H&E staining). Second, in human brain tumors, tissue specimens from 69 patients were prospectively obtained after elective surgical resection and were also compared using two methods, namely, CLE and H&E staining. The comparison was performed by an experienced neuropathologist. Results: When ICG was incubated in vitro, U87 and GL261 cell morphologies were well-defined in the CLE images and depended on dimethyl sulfoxide. Ex vivo examination of xenograft glioma tissues revealed dense and heterogeneous glioma cell cores and peritumoral necrosis using both methods. CLE images also detected invasive tumor cell clusters in the normal brain of the patient-derived glioma xenograft model, which corresponded to H&E staining. In human tissue specimens, CLE images effectively visualized the cytoarchitecture of the normal brain and tumors. In addition, pathognomonic microstructures according to tumor subtype were also clearly observed. Interestingly, in gliomas, the cellularity of the tumor and the density of streak-like patterns were significantly associated with tumor grade in the CLE images. Finally, panoramic view reconstruction was successfully conducted for visualizing a gross tissue morphology. Conclusion: In conclusion, the newly developed CLE with Lissajous laser scanning can be a helpful intraoperative device for the diagnosis, detection of tumor-free margins, and maximal safe resection of brain tumors.

Mass Spectrometry-Based Differentiation of Oral Tongue Squamous Cell Carcinoma and Nontumor Regions With the SpiderMass Technology.

Oral cavity cancers are the 15th most common cancer with more than 350,000 new cases and ~178,000 deaths each year. Among them, squamous cell carcinoma (SCC) accounts for more than 90% of tumors located in the oral cavity and on oropharynx. For the oral cavity SCC, the surgical resection remains the primary course of treatment. Generally, surgical margins are defined intraoperatively using visual and tactile elements. However, in 15-30% of cases, positive margins are found after histopathological examination several days postsurgery. Technologies based on mass spectrometry (MS) were recently developed to help guide surgical resection. The SpiderMass technology is designed for in-vivo real-time analysis under minimally invasive conditions. This instrument achieves tissue microsampling and real-time molecular analysis with the combination of a laser microprobe and a mass spectrometer. It ultimately acts as a tool to support histopathological decision-making and diagnosis. This pilot study included 14 patients treated for tongue SCC (T1 to T4) with the surgical resection as the first line of treatment. Samples were first analyzed by a pathologist to macroscopically delineate the tumor, dysplasia, and peritumoral areas. The retrospective and prospective samples were sectioned into three consecutive sections and thaw-mounted on slides for H&E staining (7 µm), SpiderMass analysis (20 µm), and matrix-assisted laser desorption ionization (MALDI) MS imaging (12 µm). The SpiderMass microprobe collected lipidometabolic profiles of the dysplasia, tumor, and peritumoral regions annotated by the pathologist. The MS spectra were then subjected to the multivariate statistical analysis. The preliminary data demonstrate that the lipidometabolic molecular profiles collected with the SpiderMass are significantly different between the tumor and peritumoral regions enabling molecular classification to be established by linear discriminant analysis (LDA). MALDI images of the different samples were submitted to segmentation for cross instrument validation and revealed additional molecular discrimination within the tumor and nontumor regions. These very promising preliminary results show the applicability of the SpiderMass to SCC of the tongue and demonstrate its interest in the surgical treatment of head and neck cancers.

MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning.

BACKGROUND AND OBJECTIVES: 12 leads electrocardiogram (ECG) are widely used to diagnose myocardial infarction (MI). Generally, the symptoms of MI can be reflected by waveforms in the heartbeat, and the contribution of different ECG leads to different types of MI is different. Therefore, it is significant to use the heartbeat waveform features and the lead relationship features for multi-category MI diagnosis. Moreover, the challenge of individual differences and lightweight algorithms also need to be further resolved and explored in the ECG automatic diagnosis system. METHODS: This paper presents a lightweight MI diagnosis system named multi-feature-branch lead attention neural network (MFB-LANN) via 12 leads ECG signals. It is designed based on the characteristics of the ECG lead. Specifically, 12 independent feature branches correspond to different leads, and each branch contains different convolutional layers to extract features in the heartbeat, then a novel attention module is developed named lead attention mechanism (LAM) to assign different weights to each feature branch. Finally all the weighted feature branches are fused for classification. Furthermore, to overcome individual differences, patient-specific scheme and active learning (AL) are used to train and update the model iteratively. RESULTS: Experimental results based on Physikalisch-Technische Bundesanstalt (PTB) database shows that the MFB-LANN achieved satisfactory results with accuracy of 99.63% based on 5-fold cross validation under the intra-patient scheme. The patient-specific experiment yielded an average accuracy of 96.99% compared to the state-of-the-art. By contrast, the model achieved acceptable results on the hybrid database (PTB and PTB-XL), especially achieving 94.19% accuracy after the update. Moreover, the system can complete the update process and real-time diagnosis on the ARM Cortex-A72 platform. CONCLUSIONS: Experiments show that the proposed method for MI diagnosis has more obvious advantages compared to other recent methods, and it has great potential to be applied to the mobile medical field.

Mass spectrometry-based intraoperative tumor diagnostics.

In surgical oncology, decisions regarding the amount of tissue to be removed can have important consequences: the decision between preserving sufficient healthy tissue and eliminating all tumor cells is one to be made intraoperatively. This review discusses the latest technical innovations for a more accurate tumor margin localization based on mass spectrometry. Highlighting the latest mass spectrometric inventions, real-time diagnosis seems to be within reach; focusing on the intelligent knife, desorption electrospray ionization, picosecond infrared laser and MasSpec pen, the current technical status is evaluated critically concerning its scientific and medical practice.

Intraoperative assessment of laryngeal malignancy using elastic light single-scattering spectroscopy: A pilot study.

OBJECTIVES/HYPOTHESIS: The elastic light single-scattering spectroscopy (ELSSS) system is a new tool for the real-time diagnosis of cancerous lesions. In the current study, we have employed ELSSS to investigate its ability in differentiation between normal and cancerous larynx tissues ex vivo. STUDY DESIGN: Basic science study in assessment of laryngeal malignancy using spectroscopy. METHODS: ELSSS spectra of the larynx tissue were acquired using a single-fiber optical probe. Ex vivo spectroscopic measurements were acquired on 95 laryngeal lesions of 40 patients. Average slopes of the spectra in the wavelength range of 450 to 750 nm were calculated. The signs of the spectral slopes were positive for benign and negative for cancerous larynx tissues. Histopathology results were used as a gold standard to define sensitivity and specificity. RESULTS: The ELSSS system correctly defined 38 out of 41 malignant tissues as cancerous; three of them were misclassified as benign. All benign tissues were correctly classified. Moderate, severely dysplastic, and malignant tissues were correctly classified as cancerous. The system could not classify mild dysplastic tissues either benign or cancerous, whereas nearly half of them were classified as benign and the other half as malignant. The signs of the spectral slopes were used as a discrimination parameter between benign and cancerous (moderate, severely dysplastic, and malignant) lesions with a sensitivity and specificity of 94% and 100%, respectively. CONCLUSIONS: The ELSSS system has the potential to be used as an adjunctive tool in the diagnosis of cancerous laryngeal tissues in real time and noninvasively. This new diagnostic technique may reduce the number of negative biopsies. LEVEL OF EVIDENCE: NA Laryngoscope, 127:611-615, 2017.

Raman Spectroscopy for the Endoscopic Diagnosis of Esophageal, Gastric, and Colonic Diseases.

Globally white-light endoscopy with biopsy sampling is the gold standard diagnostic modality for esophageal, gastric, and colonic pathologies. However, there is overwhelming evidence to highlight the deficiencies of an approach based predominantly on eyeball visualization. Biopsy sampling is also problematic due in part to excessive sampling and hence attendant cost. Various innovations are currently taking place in the endoscopic domain to aid operators in diagnosis forming. These include narrow band imaging which aims to enhance the surface anatomy and vasculature, and confocal laser endomicroscopy which provides real time histological information. However, both of these tools are limited by the skill of the operator and the extensive learning curve associated with their use. There is a gap therefore for a new form of technology that relies solely on an objective measure of disease and reduces the need for biopsy sampling. Raman spectroscopy (RS) is a potential platform that aims to satisfy these criteria. It enables a fingerprint capture of tissue in relation to the protein, DNA, and lipid content. This focused review highlights the strong potential for the use of RS during endoscopic gastroenterological examination.

Multi-modal detection of colon malignancy by NIR-tagged recognition polymers and ultrasound contrast agents.

To increase colonoscopy capability to discriminate benign from malignant polyps, we suggest combining two imaging approaches based on targeted polymeric platforms. Water-soluble cationized polyacrylamide (CPAA) was tagged with the near infrared (NIR) dye IR-783-S-Ph-COOH to form Flu-CPAA. The recognition peptide VRPMPLQ (reported to bind specifically to CRC tissues) was then conjugated with the Flu-CPAA to form Flu-CPAA-Pep which was then incorporated into echogenic microbubbles (MBs) made of polylactic acid (PLA) that are highly responsive to ultrasound. The ultimate design includes intravenous administration combined with local ultrasound and intra-colon inspection at the NIR range. In this proof of principle study PLA MBs were prepared by the double emulsion technique and loaded with several types of Flu-CPAA-Pep polymers. After insonation the submicron PLA fragments (SPF)-containing Flu-CPAA-Pep were examined in vitro for their ability to attach to colon cancer cells and in vivo (DMH induced rat model) for their ability to attach to colon malignant tissues and compared to the specific attachment of the free Flu-CPAA-Pep. The generation of SPF-containing Flu-CPAA-Pep resulted in a tissue attachment similar to that of the free, unloaded Flu-CPAA-Pep. The addition of VRPMPLQ to the polymeric backbone of the Flu-CPAA reduced cytotoxicity and improved the specific binding.