Applications In Cancer Detection Research Articles

Prostate Cancer Imaging: What We Already Know and What Is on the Horizon.

Prostate Cancer Imaging: What We Already Know and What Is on the Horizon.

Properties and Applications of Graphene and Its Derivatives in Biosensors for Cancer Detection: A Comprehensive Review.

Cancer is one of the deadliest diseases worldwide, and there is a critical need for diagnostic platforms for applications in early cancer detection. The diagnosis of cancer can be made by identifying abnormal cell characteristics such as functional changes, a number of vital proteins in the body, abnormal genetic mutations and structural changes, and so on. Identifying biomarker candidates such as DNA, RNA, mRNA, aptamers, metabolomic biomolecules, enzymes, and proteins is one of the most important challenges. In order to eliminate such challenges, emerging biomarkers can be identified by designing a suitable biosensor. One of the most powerful technologies in development is biosensor technology based on nanostructures. Recently, graphene and its derivatives have been used for diverse diagnostic and therapeutic approaches. Graphene-based biosensors have exhibited significant performance with excellent sensitivity, selectivity, stability, and a wide detection range. In this review, the principle of technology, advances, and challenges in graphene-based biosensors such as field-effect transistors (FET), fluorescence sensors, SPR biosensors, and electrochemical biosensors to detect different cancer cells is systematically discussed. Additionally, we provide an outlook on the properties, applications, and challenges of graphene and its derivatives, such as Graphene Oxide (GO), Reduced Graphene Oxide (RGO), and Graphene Quantum Dots (GQDs), in early cancer detection by nanobiosensors.

Application of an Ultrasensitive NGS-Based Blood Test for the Diagnosis of Early-Stage Lung Cancer: Sensitivity, a Hurdle Still Difficult to Overcome.

Simple SummaryCurrently, an accurate diagnosis of lung cancer relies on the microscopic examination of tissue biopsies. These samples can, however, only be obtained by invasive procedures. The aim of our study was to evaluate the use of a liquid biopsy for early-stage lung cancer detection in patients with a lung lesion on imaging. This approach would be particularly relevant for suspected lung lesions that are difficult to reach for a tissue-based diagnosis. Despite technical improvements for the use of liquid biopsy-based cell-free DNA analysis, its application for the detection of early-stage lung cancer is currently limited by sensitivity and a biological background of somatic variants.Diagnosis of lung cancer requires histological examination of a tissue sample, which in turn requires an invasive procedure that cannot always be obtained. Circulating tumor DNA can be reliably detected in blood samples of advanced-stage lung cancer patients and might also be a minimally invasive alternative for early-stage lung cancer detection. We wanted to explore the potential of targeted deep sequencing as a test for the diagnosis of early-stage lung cancer in combination with imaging. Mutation detection on cell-free DNA from pretreatment plasma samples of 51 patients with operable non-small cell lung cancer was performed and results were compared with 12 control patients undergoing surgery for a non-malignant lung lesion. By using a variant allele frequency threshold of 1%, somatic variants were detected in 23.5% of patients with a median variant allele fraction of 3.65%. By using this threshold, we could almost perfectly discriminate early-stage lung cancer patients from controls. Our study results are discussed in the light of those from other studies. Notwithstanding the potential of today’s techniques for the use of liquid biopsy-based cell-free DNA analysis, sensitivity of this application for early-stage lung cancer detection is currently limited by a biological background of somatic variants with low variant allele fraction.

Circular polarized two-element textile antenna with high isolation and polarization diversity for wearable applications

AbstractA two-element textile antenna with high isolation and polarization diversity is presented for 5 G communications, wearable technology and biomedical cancer detection applications. The flexible material is used with an orthogonally placed microstrip-feed radiator antenna responsible for polarization diversity. High isolation and low correlation are achieved between antenna elements with defected ground-based on diagonal and open-stubs, which perturbs the surface current and facilitates the achievement of circular polarization. The 10 dB impedance bandwidth (|S11|) of a two-element antenna varies from 3.3 to 4.3 GHz with more than 24 dB isolation (|S21|). The envelope correlation coefficient (ECC) and channel capacity loss (CCL) of multi-input and multi-output (MIMO) antenna parameters are less than 0.2 and 0.35 bits/s/Hz respectively, which validates the diversity performance of the antenna.

Double-sided polishing photonic crystal fiber biosensor based on surface plasmon resonance for cancer single cell detection

In this paper, we propose a high sensitivity double-sided polishing photonic crystal fiber biosensor based on surface plasmon resonance. The performance of the sensor was analyzed by the finite element method, including the influence of different pore sizes and positions, polishing depth, pore spacing, metal thickness, refractive index (RI), and manufacturing error (5%) on the sensing performance. And the performance of practical application in cancer detection was analyzed. The use of a double-sided polishing structure and coating with a gold layer not only increases the contact area between the analyte and gold layer but also provides a large-enough space for the analysis channel, which is conducive to the actual manufacture and use. The sensor has a detection range of 1.35–1.39, maximum wavelength sensitivity of 21,500 nm/RIU, average wavelength sensitivity of 9550 nm/RIU, amplitude sensitivity of − 1057.1756 RIU − 1 , R 2 of 0.98265, figure of merit of 250, and maximum resolution of 4.68 × 1 0 − 6 , which can be used in cancer detection and other biological areas.

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study

Introduction The clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation. Material and methods Breath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites. Results The discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have p-value smaller than .1. Discussion Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data. Conclusion We believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites. Clinical significance Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future. Key messages In the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer. We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.

Polarization memory rate as a metric to differentiate benign and malignant tissues.

Non-invasive optical methods for cancer diagnostics, such as microscopy, spectroscopy, and polarimetry, are rapidly advancing. In this respect, finding new and powerful optical metrics is an indispensable task. Here we introduce polarization memory rate (PMR) as a sensitive metric for optical cancer diagnostics. PMR characterizes the preservation of circularly polarized light relative to linearly polarized light as light propagates in a medium. We hypothesize that because of well-known indicators associated with the morphological changes of cancer cells, like an enlarged nucleus size and higher chromatin density, PMR should be greater for cancerous than for the non-cancerous tissues. A thorough literature review reveals how this difference arises from the anomalous depolarization behaviour of many biological tissues. In physical terms, though most biological tissue primarily exhibits Mie scattering, it typically exhibits Rayleigh depolarization. However, in cancerous tissue the Mie depolarization regime becomes more prominent than Rayleigh. Experimental evidence of this metric is found in a preliminary clinical study using a novel Stokes polarimetry probe. We conducted in vivo measurements of 20 benign, 28 malignant and 59 normal skin sites with a 660 nm laser diode. The median PMR values for cancer vs non-cancer are significantly higher for cancer which supports our hypothesis. The reported fundamental differences in depolarization may persist for other types of cancer and create a conceptual basis for further developments in polarimetry applications for cancer detection.

Advances with metal oxide-based nanoparticles as MDR metastatic breast cancer therapeutics and diagnostics.

Metal oxide nanoparticles have attracted increased attention due to their emerging applications in cancer detection and therapy. This study envisioned to highlight the great potential of metal oxide NPs due to their interesting properties including high payload, response to magnetic field, affluence of surface modification to overcome biological barriers, and biocompatibility. Mammogram, ultrasound, X-ray computed tomography (CT), MRI, positron emission tomography (PET), optical or fluorescence imaging are used for breast imaging. Drug-loaded metal oxide nanoparticle delivered to the breast cancer cells leads to higher drug uptake. Thus, enhanced the cytotoxicity to target cells compared to free drug. The drug loaded metal oxide nanoparticle formulations hold great promise to enhance efficacy of breast cancer therapy including multidrug resistant (MDR) and metastatic breast cancers. Various metal oxides including magnetic metal oxides and magnetosomes are of current interests to explore cancer drug delivery and diagnostic efficacy especially for metastatic breast cancer. Metal oxide-based nanocarrier formulations are promising for their usage in drug delivery and release to breast cancer cells, cancer diagnosis and their clinical translations.

Giuseppe peano fractal loaded multiband antenna using parasitic strips for THz applications

Multi - functional antennas are desired for future wireless applications like medical imaging, cancer detection, sensing, explosive detection, weather monitoring, automotive and industrial applications etc. In this paper, Multi band terahertz (THz) antenna is integrated on micro device Rogers RT / Duroid 6010 dielectric material having 5 µm thickness and is designed biasing on very thin graphene layer acts as radiating element having thickness of 0.5 µm. Giuseppe peano fractal (GPF) loaded on conventional rectangular microstrip patch in addition of two parasitic strips is simulated using Ansys High frequency structure simulator (HFSS) for multiband applications. This antenna attributes with compact dimensions of 20 µm × 20 µm and resonates with six bands at 3.58 THz, 5.86 THz, 6.28 THz, 6.64 THz, 7.45 THz and 7.96 THz. Designed antenna attains impedance bandwidth (IBW) of 80 GHz (3.53 – 3.61 THz), 80 GHz (5.81 – 5.89THz), 50 GHz (6.25 – 6.30 THz), 170 GHz (6.57 – 6.74 THz), 80 GHz (7.43 – 7.51 THz) and 170 GHz (7.88 – 8.05 THz) with small bandwidth ratio’s (BR) of 1.022, 1.013, 1.008, 1.025, 1.010 and 1.021 at resonant bands respectively. Radiation efficiency (η) at corresponding resonant frequencies are 90.16 %, 88.80 %, 87.25 %, 94.14 %, 83.87 % and 93.73 %. The electromagnetic characteristics such as reflection coefficient (S11), bandwidth (BW), voltage standing wave ratio (VSWR), group delay (td), input impedance (Zin), radiation patterns, gain (G) and radiation efficiency (η) are appraised in this paper.

Aplikasi Mobile Deteksi Dini Kanker Kulit Berdasarkan Image Processing

Currently, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year (WHO, 2017). Skin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. Besides, consultations with dermatologists have better prognosis (Avilés-Izquierdo et. al., 2016). Due to that, we built an early skin cancer detection application with dermatologist consultation. Our application helps to diagnose skin cancer before it grows into a life-threatening condition and is crucial to preserving lifestyle, future health, and aesthetics. Besides, thanks to online doctor consultations we have, however, getting diagnosed, prescribed and treated for your issues without spending time travelling to and from the doctors and waiting in queues can be just as effective. We used three management techniques such as machine learning to create data pipelines, build a model, and convert the model to TensorFlow lite with post-training quantization. Android to deploy the TensorFlow lite model and create the application. The application has a real-time connection using firebase. Moreover, cloud to create a simple database for doctor and diagnosis services on firebase.

Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.

Bio-Conjugated Quantum Dots for Cancer Research: Detection and Imaging.

Ultrasound, computed tomography, magnetic resonance, and gamma scintigraphy-based detection and bio-imaging technologies have achieved outstanding breakthroughs in recent years. However, these technologies still encounter several limitations such as insufficient sensitivity, specificity and security that limit their applications in cancer detection and bio-imaging. The semiconductor quantum dots (QDs) are a kind of newly developed fluorescent nanoparticles that have superior fluorescence intensity, strong resistance to photo-bleaching, size-tunable light emission and could produce multiple fluorescent colors under single-source excitation. Furthermore, QDs have optimal surface to link with multiple targets such as antibodies, peptides, and several other small molecules. Thus, QDs might serve as potential, more sensitive and specific methods of detection than conventional methods applied in cancer molecular targeting and bio-imaging. However, many challenges such as cytotoxicity and nonspecific uptake still exist limiting their wider applications. In the present review, we aim to summarize the current applications and challenges of QDs in cancer research mainly focusing on tumor detection, bio-imaging, and provides opinions on how to address these challenges.

Elucidating Raman Image-Guided Differential Recognition of Clinically Confirmed Grades of Cervical Exfoliated Cells by Dual Biomarker-Appended SERS-Tag.

Ultrasensitive detection of cancer biomarkers via single-cell analysis through Raman imaging is an impending approach that modulates the possibility of early diagnosis. Cervical cancer is one such type that can be monitored for a sufficiently long period toward invasive cancer phenotype. Herein, we report a surface-enhanced Raman scattering (SERS) nanotag (SERS-tag) for the simultaneous detection of p16/K-i67, a dual biomarker persisting in the progression of squamous cell carcinoma of human cervix. A nanoflower-shaped SERS-tag, constituted of hybrid gold nanostar with silver tips to achieve maximum fingerprint enhancement from the incorporated reporter molecule, was further functionalized with the cocktail monoclonal antibodies against p16/K-i67. The recognition by the SERS-tag was first validated in cervical squamous cell carcinoma cell line SiHa as a foot-step study and subsequently implemented to different grades of clinically confirmed exfoliated cells including normal cell (NC), high-grade intra-epithelial lesion (HC), and squamous cell carcinoma (CC) samples of the cervix. Precise Raman mapped images were constituted based on the average intensity gradient of the signature Raman peaks arising from different grades of exfoliated cells. We observed a distinct intensity hike of around 10-fold in the single dysplastic HC and CC samples in comparison to NC specimen, which clearly justify the prevalence of p16/Ki-67. The synthesized probe is able to map the abnormal cells within 20 min with high reproducibility and stability for 1 mm × 1 mm mapping area with good contrast. Amidst the challenges in Raman image-guided modality, the technique was further complemented with the gold standard immunocytochemistry (ICC) dual staining analysis. Even though both are time-consuming techniques, tedious steps can be avoided and real-time readout can be achieved using the SERS mapping unlike immunocytochemistry technique. Therefore, the newly developed Raman image-guided SERS imaging emphasizes the approach of uplifting of SERS in practical utility with further improvement for clinical applications for cervical cancer detection in future.

Dual-band THz antenna with SNG loads for biosensing and early skin cancer detection with Fano response: A numerical study

THz spectrum is one of the safest areas of light for the body which can be considered in biomedical sensing applications in the human body and can be utilized for high sensitivity detection. In this paper, a THz antenna with a single negative (SNG) load is developed for early cancer detection application. The SNG load is useful to increase the gain and efficiency of the antenna. The primary antenna has a circular slot on the ground plane. Metamaterial element is placed on the cylindrical dielectric. The total size of the antenna is 400 × 400 μm 2 and the loss-less quartz substrate is used to design the proposed antenna. In addition, the SNG load provides dual-band characteristics, and the proposed antenna covers 0.85 and 1 THz with high directivity and efficiency which is more than 90%. The achieved results proved that the antenna has higher sensitivity at a lower frequency. Moreover, we showed the proposed dual-band antenna can be used to increase the sensing accuracy of the device to detect the progress of cancer in a specific sample.

Mobile Applications in Skin Cancer Detection: A Descriptive Analysis.

Mobile Applications in Skin Cancer Detection: A Descriptive Analysis.

Theranostic Mesoporous Silica Nanoparticles Loaded With a Curcumin-Naphthoquinone Conjugate for Potential Cancer Intervention.

A novel theranostic molecule, derived from curcumin (Cur) and naphthoquinone (NQ), allowing for cancer targeting, detection and treatment was previously described and termed CurNQ. To allow for enhanced theranostic capabilities, advanced drug delivery techniques are required. To this end, mesoporous silica nanoparticles (MSN) were synthesized and CurNQ was loaded into its pores to form the novel nanosystem MSN_CurNQ. The formation of the nanosystem aimed to augment the drug delivery of CurNQ through the EPR effect and sustained release. Moreover, the loading of CurNQ into its pores, formed a fluorescent nanoparticle that can be tracked, detected and visualized. Herein, the synthesis of a novel nanosystem is described and its theranostic potential are explored in vitro. MSN with an average size of 108 d.nm, a zeta potential of −42 mV and a PDI of 0.150 were synthesized and were impregnated with CurNQ to form the novel nanosystem MSN_CurNQ. MSN_CurNQ was demonstrated to have pH-responsivity whereby after 96 h, at pH 7.4, 31.5% of CurNQ was released from the MSN compared to 57% release at pH 6.8, corresponding to an increase of 25.5% in release with a 0.6 pH drop. The innate fluorescence was then characterized through confocal and fluorescence microscopy. Microscopy images illustrated the distinct, high intensity innate fluorescence with a high background to target ratio, thus confirming detection capabilities and potentially extending MSN_CurNQ’s application to molecular imaging purposes. Moreover, the chemotherapeutic potential of MSN_CurNQ was demonstrated as cell viability was reduced to below 50% in OVCAR-5, CACO-2, CHLA, and MCF-7 cell lines. Furthermore, MSN_CurNQ displayed tumor specific toxicity whereby the cell viability was reduced to a far greater extent in the cancer cell lines compared to a healthy fibroblast cell line (p = 0.000). Indeed, the novel MSN_CurNQ nanosystem has potential for applications in cancer targeting, detection and treatment.

Abstract PO-005: An efficient digitized annotation platform for pathology-oriented dataset generation in AI research

Abstract BACKGROUND: In recent years AI and deep learning have transformed the ability to use large amounts of medical data to augment diagnosis and prognosis processes for cancer. For developing AI methodology, histopathologic assessment serves as the gold standard “labels”, enabling investigators to finely map (or annotate) biologically and clinically important features. Yet correlating high dimensional data (radiomic, morphometric, genomic, metabolomic, etc.) with expert histopathologic diagnosis for dataset generation remains a major challenge. CHALLENGE: Traditionally, labels have been extracted from a snapshot that contains all of the annotation layers overlaid on the original tissue through image processing techniques. This implies the use of distinct colors for annotation, which severely constrain the number of possible labels. Particularly, this is most noticeable for heterogeneous tissues like prostate that require complex annotation. Furthermore, the resolution with which the labels can be mapped is limited by the area of the extracted region. OBJECTIVE: Here we present a workflow for pathology-oriented dataset generation for AI studies that is compatible with standard annotation platforms, and addresses these limitations. We introduce a detailed multi-grade and multi-scale annotation protocol for prostate biopsies. The proposed method is capable of exporting labels as independent layers (representing specific grades of the pathology), and resampling them to the desired resolution. METHODS: A collection of 38 prostate biopsy sections from 19 patients fixed on slides were used. The proposed grading annotation protocol is based on the spatial distribution of cancer cells. Nine layers of annotation were considered depicting stroma, benign tissue, low grade (Gleason pattern 3) and high grade cancer (Gleason patterns 4 and 5), two mixed cancer patterns, prostatic intraepithelial neoplasia (PIN), intraductal carcinoma (IDC), and artifact. The coordinates of the annotation boundaries are post-processed and combined into a label image containing all 9 pathological classes. The metabolomic profiles of the prostate biopsies acquired by desorption electrospray ionization (DESI) is considered for data features in this study. The generated image labels are therefore spatially registered to corresponding DESI data of each slide. RESULTS: The generated dataset through proposed method is used in the application of prostate cancer detection. The dataset is validated through qualitative visualization and quantitative analysis. High correlation is observed between label images of the slides and unsupervised linear representation of corresponding DESI spectra. The pixel-based supervised identification of tissue types based on the DESI also shows high accuracy. CONCLUSION: The proposed digitized pathology annotation protocol and dataset generation workflow is compatible with AI oriented cancer research and is capable of handling large number of pathological classes and high dimensional imaging modalities. Citation Format: Amoon Jamzad, Tamara Jamaspishvili, Rachael Iseman, Martin Kaufmann, David Berman, Parvin Mousavi. An efficient digitized annotation platform for pathology-oriented dataset generation in AI research [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-005.

Low‐profile aperture stacked patch antenna for early‐stage breast cancer detection applications

In this paper, a low-profile aperture-stacked patch (LP-ASP) antenna for breast cancer detection applications is proposed. The antenna is embodied by three substrate layers, in which one layer is applied to feed all stacks and the other two are adjoined substrates with rectangular patches, mounted on the top of the feeding layer. The feeding layer consists of a fork-shaped strip on one side, and an H-shaped slot attached to a cross-shaped slot on the other side. By using the proposed feeding structure and a designed defected ground plane, an additional current path is provided which generates extra resonances and widens the impedance bandwidth from 2.5 to 15 GHz. The overall size of the antenna is 10 × 10 × 3.475 mm3, which is approximately 0.083λ × 0.083λ × 0.03λ at the antenna's lowest operating frequency. To evaluate the performance of the designed antenna in an imaging system, first, a breast model with an embedded spherical tumor (with a radius of 4 mm) is constructed. Then, a conformal array of the designed antenna with 17 elements on a cross-arranged lattice is placed around the breast model. The quantitative metrics of the reconstructed images show that the proposed antenna can be a good candidate for the breast cancer detection applications.

PERFORMANCE ANALYSIS OF CIRCULAR POLARIZED WEARABLE ANTENNA FOR BREAST CANCER DETECTION APPLICATION

PERFORMANCE ANALYSIS OF CIRCULAR POLARIZED WEARABLE ANTENNA FOR BREAST CANCER DETECTION APPLICATION