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Metamaterials for refractive index sensing applications have garnered significant attention in recent years. However, achieving an optimal balance between high sensitivity and quality factor, which together determine the Figure of Merit of sensors, necessitates extensive optimization efforts. In this paper, we present the metaheuristic optimization of a refractive index-based plasmonic sensor operating at a wavelength of 1500 nm, which has the potential to open new avenues for applications in biomedical sensing and beyond. Particle Swarm Optimization is utilized to maximize the performance of the proposed infrared metamaterial sensor. The proposed design features two overlapping E-shaped silver patches placed on top of a grounded Al 2 O 3 substrate. This configuration results in a narrow-band absorption spectrum with peak resonances caused by the confinement of the electric field within the gaps of the E-shaped metallic arms. The metaheuristic optimization process yielded sensor dimensions that achieve a high sensitivity of 834.7 nm/RIU,Q = 865 and a Figure of Merit of 481.34 RIU - 1 , demonstrating outstanding performance in cancer cell detection. [ABSTRACT FROM AUTHOR]

In this paper, we present a graphene-based band-pass filter featuring rectangular-shaped resonators, based on research into the sensing characteristics of surface plasmon polaritons (SPP). The finite-difference time-domain (FDTD) method and simulations reveal that the transmission spectrum of this structure exhibits a dual-mode spectrum. This structure operates at two functional modes with wavelengths of 1135 nm and 1705 nm, and its compact size measures 330 nm × 480 nm. We investigated the effect of geometric parameters on the pass bands, finding transmission rates as high as 0.97 and 0.88, sensitivities of 2166.6 nm/RIU and 2000 nm/RIU, and figures of merit (FOM) of 50.38 and 38.46 RIU−1. Due to the minimal difference in the RI of cancer cells and the high sensitivity of the sensor, it is highly effective for cancer cell detection. Additionally, the accuracy of the sensor's performance is validated using a one-sample T-test, which confirms the precision and reliability of the designed sensor. [ABSTRACT FROM AUTHOR]

Variational autoencoders (VAEs) are generative models known for learning compact and continuous latent representations of data. While they have proven effective in various applications, using latent representations for classification tasks presents challenges. Typically, a straightforward approach involves concatenating the mean and variance vectors and inputting them into a shallow neural network. In this paper, we introduce a novel approach for variational autoencoders, named VAE-GNA, which integrates Gaussian neurons into the latent space along with attention mechanisms. These neurons directly process mean and variance values through a suitable modified sigmoid function, not only improving classification, but also optimizing the training of the VAE in extracting features, in synergy with the classification network. Additionally, we investigate both additive and multiplicative attention mechanisms to enhance the model's capabilities. We applied the proposed method to automatic cancer detection using near-infrared (NIR) spectral data, showing that the experimental results of VAE-GNA surpass established baselines for spectral datasets. The results obtained indicate the feasibility and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A novel design of an optical biosensor based on surface plasmon resonance using tin selenide (SnSe) and graphene is presented. Including biological solutions, the performance of the obtained biosensor over a wide range of refractive index variations is promising. It is designed such that its key parameters are optimized to detect different cancerous cells with high selectivity and sensing ability. The design and analysis of the sensor have been carried out by implementing a finite element method–based simulation platform. The results revealed that the designed sensor can provide a sensitivity of 121.0 deg.RIU−1, 134.2 deg.RIU−1, 142.9 deg.RIU−1, 143.6 deg.RIU−1, and 72.2 deg.RIU−1 for sensing skin, cervical, blood, adrenal gland, and breast cancerous cells respectively with maximum quality factor of 408 RIU−1 and a detection accuracy of 5.71. The results obtained using the proposed model of the SPR biosensor are promising. It could be a potential candidate for several sensing applications such as detecting different types of cancer disease in the early stages. Such detection is anticipated to save the lives of cancer patients. Comparing our results to work published recently, we were able to achieve an enhanced angular sensitivity (S) ranging from 72 to 143.6 deg.RIU−1, a detection accuracy (DA) ranging from 2.66 to 5.71, and a quality factor (QF) ranging from 190 to 408 RIU−1. [ABSTRACT FROM AUTHOR]

An innovative photonic crystal fiber (PCF) biosensor using surface plasmon resonance (SPR) to diagnose six distinct kinds of cancers (skin cancer, cervical cancer, adrenal gland cancer, blood cancer, and breast cancer types 1 and 2) in cells is demonstrated here and incorporates with two microchannels and a bimetallic configuration. The numerical analysis utilizes the finite element method (FEM) combined with perfectly matched layers (PML). The plasmonic material employed in the biosensor is gold (Au), and a supportive material, titanium dioxide (TiO2), is combined with Au. By combining these two materials, the sensor's performance is improved. The numerical calculations indicate that breast cancer type 2 has the highest wavelength sensitivity (24,285.71 nm/RIU) and amplitude sensitivity (3959 RIU−1). The rest of the cancer cells' wavelength sensitivities are 11,000.00 nm/RIU (skin cancer), 13,333.33 nm/RIU (cervical cancer), 15,000.00 nm/RIU (blood cancer), 17,142.85 nm/RIU (adrenal gland cancer), and 21,428.57 nm/RIU (breast cancer type 1). This advanced biosensor has several uses in biological sensing and medical technology, and it has the potential to revolutionize cancer identification and medical diagnosis. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is being used increasingly to detect fatal diseases such as cancer. The potential reduction in human error, rapid diagnosis, and consistency of judgment are the primary motives for using these applications. Artificial Neural Networks and Convolution Neural Networks are popular AI techniques being increasingly used in diagnosis. Numerous academics have explored and evaluated AI methods used in the detection of various cancer types for comparison and analysis. This study presents a thorough evaluation of the AI techniques used in cancer detection based on extensively researched studies and research trials published on the subject. The manuscript offers a thorough evaluation and comparison of the AI methods applied to the detection of five primary cancer types: breast cancer, lung cancer, colorectal cancer, prostate cancer, skin cancer, and digestive cancer. To determine how well these models compare with medical professionals’ judgments, the opinions of developed models and of experts are compared and provided in this paper. [ABSTRACT FROM AUTHOR]

Breast cancer is the most widespread form of cancer diseases among women. Such oncoviral cancer starts in the epithelial lining of the lobules or ducts in the breast gland tissue. Identifying and classifying breast cancer presents a significant challenge for researchers and scientists. Neural networks have emerged as a powerful tool for classifying cancer data through feature extraction. This paper addresses the challenge of accurately classifying breast cancer using a novel approach that combines a hybrid sigmoid activation function (HSAF) with transfer learning, utilizing the pre-trained EfficientNetB6 model. The HSAF is specifically designed to capture complex patterns within histopathological images, while transfer learning leverages prior knowledge from the pre-trained model. In our experimental approach, we employ a breast histopathological image dataset, dividing it into three segments: 60% for training, 20% for validation, and 20% for testing. Furthermore, data augmentation techniques are performed to increase the size of training data. The experimental results of this research indicate an impressive precision, recall, and F1 score of 91%. Furthermore, our proposed model is compared to existing methods, demonstrating its efficiency. We also conduct a comparative study of activation functions (AFs), highlighting the classification performance of HSAF for breast cancer. This research not only advances our ability to classify breast cancer more accurately but also serves as a catalyst for raising awareness and alleviating concerns related to breast cancer. By integrating advanced technology and innovative techniques, this paper aims to make a meaningful contribution to the early detection and effective treatment of this widespread and life-affecting disease. [ABSTRACT FROM AUTHOR]

We propose a dual-core, highly sensitive PCF SPR biosensor with C-shaped grooves based on a fusion of graphene and gold film. The performance of our sensor is enhanced by our circular fibre lattice with perforated air holes. We evaluate the biosensor's sensitivity to variations in refractive index (RI) in cancer cells numerically using the Finite Element Method (FEM) in COMSOL Multiphysics. After enhancing fiber parameters and using numerical results from amplitude method and spectral interrogation methods, we found that the biosensor demonstrates the highest sensitivity for MCF7 cells, 2142.86 nm/RIU under spectral interrogation. With HeLa cells, the biosensor shows a sensitivity of – 1058.039 1/RIU under the amplitude interrogation approach. Moreover, for MCF7, the biosensor reaches a resolution of 04.60 × 1E–5 RIU. [ABSTRACT FROM AUTHOR]

Cancer is an invasive and malignant growth of cells and is known to be one of the most fatal diseases. Its early detection is essential for decreasing the mortality rate and increasing the probability of survival. This study presents an efficient machine learning approach based on the state vector machine (SVM) to diagnose and classify tumors into malignant or benign cancer using the online lymphographic data. Further, two types of neural network architectures are also implemented to evaluate the performance of the proposed SVM-based approach. The optimal structures of the classifiers are obtained by varying the architecture, topology, learning rate, and kernel function and recording the results' accuracy. The classifiers are trained with the preprocessed data examples after noise removal and tested on the unknown cases to diagnose each example as positive or negative. Further, the positive cases are classified into different stages including metastases, malign lymph, and fibrosis. The results are evaluated against the feed-forward and generalized regression neural networks. It is found that the proposed SVM-based approach significantly improves the early detection and classification accuracy in comparison to the experienced physicians and the other machine learning approaches. The proposed approach is robust and can perform sub-class divisions for multipurpose tasks. Experimental results demonstrate that the two-class SVM gives the best results and can effectively be used for the classification of cancer. It has outperformed all other classifiers with an average accuracy of 94.90%. [ABSTRACT FROM AUTHOR]

Cancer, the world's second most common cause of death, has seen a recent surge in mortality. Harnessing biomarkers for diagnosis hold promise for early cancer detection and treatment. Biosensors, particularly using Surface-Plasmon-Resonance (SPR) technology for label-free detection, are key to this strategy. This research introduces an innovative biosensor concept based on SPR technology, recognized for its exceptional sensitivity in detecting cancer cells. The biosensor features a unique layer of Gold, with an enhanced hexagonal lattice design featuring air holes. Through advanced numerical simulations employing the Finite Element Model, refractive index (RI) changes related to malignancy were observed via wavelength and amplitude-based interrogation methods. Notably, the biosensor demonstrated a highest sensitivity measuring 7142.86 nm/RIU (nm per RIU) during spectral interrogation for MCF7 cells, along with a sensitivity of − 4220.99 per unit of RIU amplitude-based interrogation method for HeLa cells. Additionally, this biosensor achieved a significant resolution of 3.33 × 10–5 RIU for Basal cells. In summary, this study unveils a cutting-edge biosensor rooted in SPR technology, showcasing high sensitivity and efficacy in detecting cancer cells. Rigorous numerical analysis underscores its potential for early cancer detection and precise diagnostics. [ABSTRACT FROM AUTHOR]

RNA modifications affect fundamental biological processes and diseases and are a research hotspot. Several micro-RNAs (miRNAs) exhibit genetic variant-targeted RNA modifications that can greatly alter their biofunctions and influence their effect on cancer. Therefore, the potential role of these miRNAs in cancer can be implicated in new prevention and treatment strategies. In this study, we determined whether RMvar-related miRNAs were closely associated with tumorigenesis and identified cancer-specific signatures based on these miRNAs with variants targeting RNA modifications using an optimized machine learning workflow. An effective machine learning workflow, combining least absolute shrinkage and selection operator analyses, recursive feature elimination, and nine types of machine learning algorithms, was used to screen candidate miRNAs from 504 serum RMvar-related miRNAs and construct a diagnostic signature for cancer detection based on 43,047 clinical samples (with an area under the curve value of 0.998, specificity of 93.1%, and sensitivity of 99.3% in the validation cohort). This signature demonstrated a satisfactory diagnostic performance for certain cancers and different conditions, including distinguishing early-stage tumors. Our study revealed the close relationship between RMvar-related miRNAs and tumors and proposed an effective cancer screening tool. [ABSTRACT FROM AUTHOR]

A novel heterostructure of surface plasmon resonance (SPR) sensor has been proposed in the current study for detecting various cancer cells. The proposed sensor is highly sensitive to the refractive index change of the sensing medium. The suggested sensor consists of Ag, PtSe2, and graphene. For the single layer of PtSe2 and graphene layer, maximum sensitivity and figure of merit for breast type II cancer are 235 Deg/RIU and 41.14 RIU−1 and have been achieved, respectively. The highest value of detection accuracy is 0.194 Deg−1 for basal cells, which causes skin cancer. This is a simulation-based study utilizing the finite element method. To analyze the electric field strength close to the interface between PtSe2 and the sensing medium, the proposed structure is simulated using the COMSOL Multiphysics system. [ABSTRACT FROM AUTHOR]

Cancer is the most fatal diseases in the world which has highest mortality rate as compared to other type's human diseases. The most common and dangerous types of cancers are lung cancer, skin cancer, brain tumors, breast cancer, Colorectal cancer, Prostate Cancer, Blood cancer and many other. The millions of person lose their life due to these highly dangerous, fatal types of disease. Hence it is required to provide the solution using computer added automatic cancer detection technique in early stage, for the research gap analysis is required. In this paper we have studied the various cancer detection techniques based on traditional machine learning (ML) and deep learning (DL) techniques and summarize the research gap for the various cancers detection techniques. The study has been conducted based the types of technique uses, types of features utilized, dataset used and accuracy of the cancer detection achieved using best technique. In this study we have conducted the reviewed over 100 recently published research papers and focused on 7 types of most fatal cancer types such as lung cancer, breast cancer, skin cancer, brain tumor, colorectal, prostate, and Leukemia (Blood cancer). The study also used the state-of-art table to compare the previous and current study conducted on cancer detection techniques. We have visualized using separate comparison table for 7 types of cancer detection using traditional ML method and DL methods also visualized through the bar chart. The best accuracy result obtained using ML and DL methods are 100% and the most commonly used ML classifier is Support Vector Machine whereas CNN is most commonly used DL classifier. The main challenges we observed are the data imbalance issue, varieties of feature extraction techniques, small medical dataset, classifier parameters optimizations, Execution time, Adaptive classifier, and common technique for segmentation and classification. The main objective of this review is to investigate the existing methods used for the various types of cancer detection and finding the research gap, challenges, and recent advancement mainly in the use of ML and DL models which may help the researchers to find the better solutions. [ABSTRACT FROM AUTHOR]

Lung cancer remains a prevalent and deadly disease, claiming numerous lives annually. Early detection plays a pivotal role in significantly improving survival rates, by up to 50–70%. Therefore, developing a robust lung cancer detection system holds immense potential to positively impact human survival. Computed tomography (CT) scan images offer invaluable information about lung nodules, and the emergence of machine learning and deep learning techniques has empowered radiologists in their diagnostic tasks. In this study, we propose a new ensemble of deep learning models to accurately classify the severity of lung nodules. Our approach leverages deep transfer learning and adopts an ensemble learning approach. Specifically, three state-of-the-art convolutional neural networks (CNN) models, namely ResNet-152, DenseNet-169, and EfficientNet-B7, are employed. To enhance the ensemble method's performance, we introduce a novel scheme for selecting and assigning weights to each base model. Unlike conventional methods that often rely on manual experimentation to set weights, our approach fuses the scores of two standard assessment metrics, ROC-AUC score and F1-score, for a more accurate weight vector determination. To evaluate the effectiveness of our method, we conduct extensive testing using the publicly available CT scan dataset, LIDC-IDRI. Our proposed ensemble achieves an accuracy of 97.23%, surpassing various recent methods and outperforming commonly used ensemble techniques. Furthermore, our novel weight optimization strategy significantly reduces false negatives, leading to a sensitivity of 98.6%. The codes for the proposed work are available at https://github.com/iabh1shekbasu/LungCancerDetectionEnsemble. [ABSTRACT FROM AUTHOR]

Far infrared (FIR) irradiation is commonly used as a convenient, non-contact, non-invasive treatment for diseases such as myocardial ischemia, diabetes, and chronic kidney disease. In this review, we focus on reviewing the potential therapeutic mechanisms of FIR and its cutting-edge applications in cancer detection. Firstly, we searched the relevant literature in the last decade for systematic screening and briefly summarized the biophysical properties of FIR. We then focused on the possible mechanisms of FIR in wound healing, cardiovascular diseases, and other chronic diseases. In addition, we review recent applications of FIR in cancer detection, where Fourier transform infrared spectroscopy and infrared thermography provide additional diagnostic methods for the medical diagnosis of cancer. Finally, we conclude and look into the future development of FIR for disease treatment and cancer detection. As a high-frequency non-ionizing wave, FIR has the advantages of safety, convenience, and low cost. We hope that this review can provide biological information reference and relevant data support for those who are interested in FIR and related high-frequency non-ionizing waves, to promote the further application of FIR in the biomedical field. [ABSTRACT FROM AUTHOR]

Background: Cell-free DNA (cfDNA) has shown promise in detecting various cancers, but the diagnostic performance of cfDNA end motifs for multiple cancer types requires verification. This study aimed to assess the utility of cfDNA end motifs for multi-cancer detection. Methods: This study included 206 participants: 106 individuals with cancer, representing 20 cancer types, and 100 healthy individuals. The participants were divided into training and testing cohorts. All plasma cfDNA samples were profiled by whole-genome sequencing. A random forest model was constructed using cfDNA 4 bp-end-motif profiles to predict cancer in the training cohort, and its performance was evaluated in the testing cohort. Additionally, a separate random forest model was developed to predict immunotherapy responses. Results: In the training cohort, the model based on 4 bp-end-motif profiles achieved an AUC of 0.962 (95% CI 0.936–0.987). The AUC in the testing cohort was 0.983 (95% CI 0.960–1.000). The model also maintained excellent predictive ability in different tumor sub-cohorts, including lung cancer (AUC 0.918, 95% CI 0.862–0.974), gastrointestinal cancer (AUC 0.966, 95% CI 0.938–0.993), and other cancer cohort (AUC 0.859, 95% CI 0.776–0.942). Moreover, the model utilizing 4 bp-end-motif profiles exhibited sensitivity in identifying responders to immunotherapy (AUC 0.784, 95% CI 0.609–0.960). Conclusion: The model based on 4 bp-end-motif profiles demonstrates superior sensitivity in multi-cancer detection. Detection of 4 bp-end-motif profiles may serve as potential predictive biomarkers for cancer immunotherapy. [ABSTRACT FROM AUTHOR]

In this article, a silicon-graphene-based absorber in the terahertz (THz) spectrum is provided for the synchronic surveilling of blood components such as water, glucose, cells, and so on. Our presented absorber has three resonant absorption frequencies at 0.865, 1.43, and 2 THz with a max Q-factor of 15.82. Although it has the advantages of miniaturized, low sample consumption, and cost-effectiveness it is simpler and faster than other conventional methods. Triple resonant absorption peak provides a highly sensitive sensor, reduces the quantification errors, and achieves higher precision to the change of samples dielectric constant in comparison with single or double band biosensors. So by attaching a test medium (analyte) and applying the various refractive index, it has been shown that the suggested biosensor sensitivity is approximated by the absorbency properties with average absorbance of 99.58% for the suggested biomedical sensor that is distinguished among the reported biomedical sensors structures. The impact of dielectric properties, operating frequency, and sample thickness on the operation of the graphene-based absorber is analyzed by the full wave FEM method. The proposed absorber with a narrow absorption spectrum and good sensitivity potentially can be used for biochemical sensing applications. [ABSTRACT FROM AUTHOR]

Due to singularity of Raman Spectroscopy (RS) measurements in revealing molecular biochemical changes between cancerous and normal tissues and cells, RS was recently demonstrated to be a non-destructive method of cancer diagnosis. The quantity and quality of tissue samples for RS are crucial for accurate prediction when developing computational methods for cancer detection. The training of the classifier with a small number of samples is difficult and frequently leads to overfitting. As a result, increasing the number of samples is crucial for better training classifiers that can accurately classify cancer tissue. This study proposes a novel method for the detection of spine cancer using a high-sensitivity biosensor and edge elimination based on Raman spectroscopy by Multi photon micro material analysis. Using a high-sensitivity biosensor and Raman spectroscopy, the input tumour image edge is removed. F-fluorodeoxy glucose PET imaging (FDG-PET) applied images are used to identify the cancer-affected region in this edge-minimized image, and a Lasso regressive-based reinforcement neural network is used to analyse the 511-keV photons. The accuracy, F-measure, recall, dice coefficient, Areas under the curve (AUC), and neuron-specific enolase (NSE) are all used in the experimental analysis. proposed technique attained accuracy of 98%, F-measure of 75%, Recall of 65%, dice coefficient of 55%, AUC of 63% and NSE of 59%. [ABSTRACT FROM AUTHOR]

Purpose: There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. Methods: In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018–2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. Results: Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. Conclusion: The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives. [ABSTRACT FROM AUTHOR]

The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine. Nevertheless, for these models to be extremely generalizable and perform well, they need to be applied to a vast corpus of data. In order to train transfer learning networks with limited datasets, data augmentation techniques are frequently used due to the difficulties in getting data. The use of these methods is crucial in the medical industry in order to enhance the number of cancer-related magnetic resonance imaging pathology scans. This study evaluates the results of data augmentation methods on three deep transfer learning networks, such as InceptionV3, VGG16, and DenseNet169, for brain tumor identification. To demonstrate how data augmentation approaches affect the performance of the models, networks were trained both before and after the application of these methods. The outcomes revealed that the image augmentation strategies have a big impact on the networks before and after using techniques, such as the accuracy of VGG16 is 77.33% enhanced to 96.88%, and InceptionV3 changed from 86.66 to 98.44%, and DenseNet169 changed from 85.33 to 96.88% the accuracy percentage increase of the models are 19.55%, 11.78%, and 11.55%, respectively. [ABSTRACT FROM AUTHOR]

Detection of melanoma in the early stage can provide the patients with more promising treatments. Numerous methods have been proposed for detecting melanoma using computational approaches; however, the application of deep learning in this field is incipient and needs to be assessed further. In this paper, we introduce MDDC, a four-step deep learning-based pipeline for melanoma detection using digital images. MDDC removes image noise using discrete wavelet transform, pre-processes and extends the generalizability using a Gaussian blur filter and image transformation techniques, and finally computes the probability of melanoma in digital images using a convolutional neural network. The model performance is evaluated by considering various CNN architectures and three validation scenarios to find the best CNN architecture. Moreover, we used the transfer learning technique to improve the model performance and generalize it for working on another dataset. MDDC can handle noisy images and is insensitive to image transformations. The validation of MDDC led to 0.96 Accuracy, 0.96 Recall, 0.954 Specificity, 0.99 AUC, and 0.99 AUPR. In addition, further evaluation of the proposed method with state-of-the-art methods verifies that MDDC outperforms other methods. Consequently, MDDC can efficiently and quickly detect melanoma in digital images. [ABSTRACT FROM AUTHOR]

There has been a rapid development of THz technology—sources, detectors and various THz imaging and sensing techniques. The THz technology demonstrates great potential as a modality for early, label free, non-ionizing and non-invasive detection of cancer. Some progressive technological development milestones have been achieved in this regard, however, to become clinically competitive and to provide the sought after real operational convenience, there is need for further research and development to overcome the existing challenges. This paper provides recent trends and perspectives through identification of existing challenges for the development of THz imaging and sensing systems that can evolve into actual medical modalities. We provide an overview of various aspects of THz technology, including techniques for imaging and sensing, mechanisms for THz image contrast and models for tissue dielectric responses to THz waves. The THz imaging application for detection of various cancers is briefed. The advantages of THz cancer imaging and sensing as well as the existing challenges are identified, with recommendations provided in contribution to future research. Further, some recent THz imaging and sensing developments such as the near-field methods to break the diffraction limit including waveguides, resonance and plasmonic metasurfaces are discussed. We emphasize the contribution of analytical algorithms that are based on machine learning, in particular, deep learning for the development of THz technology. [ABSTRACT FROM AUTHOR]

Early, express, and reliable detection of cancer can provide a favorable prognosis and decrease mortality. Tumor biomarkers have been proven to be closely related to tumor occurrence and development. Conventional tumor biomarker detection based on genomic, proteomic, and metabolomic methods is time and equipment-consuming and always needs a specific target marker. Surface-enhanced Raman scattering (SERS), as a non-invasive ultrasensitive and label-free vibrational spectroscopy technique, can detect cancer-related biomedical changes in biofluids. In this paper, 110 serum samples were collected from 30 healthy controls and 80 cancer patients (including 30 bladder cancer (BC), 30 adrenal cancer (AC), and 20 acute myeloid leukemia (AML)). One microliter of blood serum was mixed with 1 μl silver colloid and then was air-dried for SERS measurements. After spectral data augmentation, one-dimensional convolutional neural network (1D-CNN) was proposed for precise and rapid identification of healthy and three different cancers with high accuracy of 98.27%. After gradient-weighted class activation mapping (Grad-CAM) based spectral interpretation, the contributions of SERS peaks corresponding to biochemical substances indicated the most potential biomarkers, i.e., L-tyrosine in bladder cancer; acetoacetate and riboflavin in adrenal cancer and phospholipids, amide-I, and α-Helix in acute myeloid leukemia, which might provide an insight into the mechanism of intelligent diagnosis of different cancers based on label-free serum SERS. The integration of label-free SERS and deep learning has great potential for the rapid, reliable, and non-invasive detection of cancers, which may significantly improve the precise diagnosis in clinical practice. [ABSTRACT FROM AUTHOR]

Breast cancer is the second leading cause of death in females. As such, women have high incidence and mortality rates of breast cancer. The incidence rate has been on the rise over time. The earlier breast cancer is caught, the better it shows prognosis and the lower the mortality rate is. For this reason, many researchers and medical doctors have heeded a lot of attention to the CAD systems to detect and classify breast cancer. They have proposed a myriad of methods and techniques. Among them, the CAD system based on artificial intelligence (AI) can process plenty of information fast, and its performance is evaluated to be high. As an AI algorithm, YOLO has excellent detection performance and can detect objects effectively in real time. In this paper, we proposed an anomaly detection model of mammography using a YOLOv4-based histogram. In terms of breast cancer diagnosis, mammography features a fast diagnosis time and an inexpensive cost. For this reason, it is often applied to breast cancer diagnosis. Mammography, however, generates an image only with brightness values, so that a mammogram image has a lot of noise and image edges are dim. To enhance these image edges, we create a difference through histogram and brightness range control and threshold-based region removal methods and expand the single channel of mammogram images using the generated images. Through the expansion, the image edges are enhanced and converted into a single channel again and are learned through YOLO. For performance evaluation, the method proposed in this study is compared with ResNet18, ResNet50, GoogleNet, and VGG16. According to an experiment, the proposed method had the highest accuracy, or 95.74%, followed by GoogleNet (89.9%), VGG16 (88.93%), ResNet50 (87.77%), and ResNet18 (87.67%) in order. [ABSTRACT FROM AUTHOR]

Purpose: There is currently no consensus regarding the optimal number of multiparametric magnetic resonance imaging (MRI)-targeted biopsy (TB) cores and their spatial distribution within the MRI lesion. We aim to determine the number of TB cores and location needed to adequately detect csPCa. Methods: We conducted a retrospective cohort study of 505 consecutive patients undergoing TB for positive MRI lesions defined by a PI-RADS score ≥ 3 between June 2016 and January 2022. Cores chronology and locations were prospectively recorded. The co-primary outcomes were the first core to detect clinically significant prostate cancer (csPCa) and the first highest ISUP grade group. The incremental benefit of each additional core was evaluated. Analysis was then performed by distinguishing central (cTB) and peripheral (pTB) within the MRI lesion. Results: Overall, csPCa was detected in 37% of patients. To reach a csPCa detection rate of 95%, a 3-core strategy was required, except for patients with PI-RADS 5 lesions and those with PSA density ≥ 0.2 ng/ml/cc who benefited from a fourth TB core. At multivariable analysis, only a PSA density ≥ 0.2 ng/ml/cc was an independent predictive factor of having the highest ISUP grade group on the fourth TB cores (p = 0.03). No significant difference in the cancer detection rate was found between cTB and pTB (p = 0.9). Omitting pTB would miss 18% of all csPCa. Conclusion: A 3-core strategy should be considered for TB to optimize csPCa detection with additional cores needed for PI-RADS 5 lesions and high PSA density. Biopsy cores from both central and peripheral zones are required. [ABSTRACT FROM AUTHOR]

Surface plasmon resonance (SPR) technique is an excellent method for providing optical and label-free detection of target bioanalytes. In this research, we suggest a novel SPR biosensor based on a hybrid TiO2/Au layer that offers improved sensitivity and detection capabilities for cancer cells. The biosensor is based on a hybrid TiO2/Au layer, which enhances the performance of the sensor by making the gold coating and fiber more adherent to each other. Our prototype's fiber is strategically drilled with circular air holes, which enhances the sensor's performance. To validate the proposed design, numerical analysis was performed using the finite element model (FEM) technique of COMSOL Multiphysics Simulation tool. The refractive index changes of cancer cells are discovered using the wavelength-interrogation and amplitude-interrogation approaches. Based on the numerical findings of spectrum interrogation and amplitude techniques, we found that the greatest sensitivity of this biosensor is 4078.43 nm/RIU for Hela using spectral interrogation and 4285.71 1/RIU for MCF7 cell utilizing amplitude-interrogation techniques. This sensor also displays the highest resolution for basal cells of 4.0 × 10−5 RIU. [ABSTRACT FROM AUTHOR]

Metasurface biosensors have become the core label-free and rapid-detection technology in bioanalysis. Lung cancer and brain cancer are the first leading causes of cancer death among adults and adolescents, respectively, where poor early diagnosis results from expensive detection costs and time consumption. To tackle the above problems, here, we introduce a terahertz-domain metasurface biosensor for cancer diagnosis, relying on a perfectly symmetrical periodic surface structure, which significantly exhibits polarization-insensitivity at 2.05 THz and the high-sensitivity of 504 GHz/RIU (RIU = refractive index unit). According to the frequency shifts and transmittance variations, four cell types are successfully distinguished from each other. The minimum number of cells is required for thousands of cells to display the differences of spectra, which is 1/30 of clinical methods. Furthermore, the results were consistent with pathological results (the gold standard in clinic) by Gaussian curve fitting. The proposed biosensor has really achieved the characterization of cells in normal and cancerous state. This detection strategy dramatically reduced the cost of detection by reuse and time consumption was reduced to 1/20 of the pathology testing. In addition, it is flexible to set samples and easy to realize automatic operation due to the great polarization-insensitivity of the proposed biosensor, which can further reduce labor costs in the future. It is envisioned that the proposed biosensor will present immense potential in the fields of cancer detection, distinguishing different cancers, and identifying primary lesion cancer or metastatic cancer. [ABSTRACT FROM AUTHOR]

This paper presents two flexible monopole antennas implemented on cotton substrate as sensors designed for radar-based microwave imaging and particularly breast imaging systems. The flexible antennas are basic building block of a radar-based microwave imaging system that could be integrated in clothes. Thus, these low-cost textile sensors could be used by women for self-monitoring and early breast cancer detection at an affordable price. The ultra-wideband fully textile sensors are designed as rectangular and circular monopole antennas. Both sensors have an impedance bandwidth ≤ − 10 dB from 2.2 to 8 GHz with an overall footprint of 50 × 50 mm2. Simulations for antenna in proximity to breast model with and without tumor as well as bending capacity are performed. The simulations are complemented with fabrication of a breast phantom and a tumor sample with parameters that mimic these of the human breast's healthy and malignant tissue. Measurements are compared to simulation results as well as the performance of antenna before and after subjected to washing. Finally, the specific absorption rate is also calculated and measured to ensure safety for on-body deployment. The proposed work demonstrates the potential to develop wearable microwave imaging system using fully textile antennas. [ABSTRACT FROM AUTHOR]

Although early cancer identification is essential for better patient outcomes, traditional diagnostic procedures are not always sensitive enough to find biomarkers at low quantities. Thanks to its great sensitivity and ability to detect in real-time, surface plasmon resonance (SPR) sensors—especially those that include photonic crystal fibers (PCFs)—have become attractive instruments for bio-sensing applications. A further way to improve the performance of SPR sensors is to include sophisticated nanomaterials into them. Utilizing hybrid nanocomposite layers including gold, graphene, and Ti3C2Tx-MXene, we describe the design and construction of an ultra-sensitive PCF-SPR sensor for cancer detection in this paper. The hybrid nanocomposite layers are applied to a PCF structure in the suggested sensor architecture. The main plasmonic layer is made of gold, which is well-known for its exceptional plasmonic capabilities. Graphene and Ti3C2Tx-MXene are used to increase sensitivity and stability. By simulating the sensor’s operation using the finite element method (FEM), we were able to improve the PCF shape and nanocomposite layer thickness. To measure the refractive index (RI), researchers looked at how the resonance wavelength changed as a function of the analyte’s concentration of cancer biomarkers. The improved sensor outperformed conventional SPR sensors with a sensitivity of 15,000 nm/RIU (refractive index unit). A resonant wavelength shift of 300 nm for a refractive index change of 0.02 RIU was achieved after the addition of the hybrid nanocomposite layer increased the electric field enhancement. With a detection limit of 1.2 × 10−6 RIU, the sensor proved to be an excellent tool for identifying trace amounts of cancer biomarkers in patient samples. A significant step forward in cancer detection technology is provided by the ultra-sensitive PCF-SPR sensor, which employs a hybrid nanocomposite layer of graphene, Ti3C2Tx-MXene, and gold. The sensor’s sensitivity is greatly improved by the combination of these nanomaterials, which enables the early detection of cancer biomarkers at minimum quantities. As a non-invasive, real-time and very accurate way for early cancer diagnosis, this sensor has enormous promise for incorporation into clinical diagnostic tools. If this technique is refined and confirmed, it has the potential to greatly enhance patient outcomes by allowing for earlier identification and treatment. [ABSTRACT FROM AUTHOR]

This work presents a photonic crystal fiber (PCF)–based surface plasmon resonance (SPR) biosensor with dual cores and bilateral-surface detection capability to enhance the early identification of cancerous cells. The biosensor processes cell samples including both malignant cancerous cells and healthy normal cells through varying refractive indices. The optimized design of the sensor achieves enhanced wavelength and amplitude sensitivities and identification accuracy in terms of resolution. The bilateral-surface precision PCF SPR biosensor allows for continuous tracking of dynamic biomolecular interactions with the plasmonic region by detecting changes in resonance conditions. The suggested biosensor is evaluated numerically using the finite element method (FEM)–based COMSOL tool. Its performance is assessed for several malignant cells such as MCF7, MDAMB231, PC12, HeLa, Jurkat, and Basal over a refractive index range of 1.360 to 1.401, and they were found to have wavelength sensitivities of 5714.28, 5714.28, 4285.72, 4285.71, 3333.33, and 3000 nm/RIU, respectively. The maximum wavelength sensitivity of 5714.28 nm/RIU is observed for the MCF7 and MDAMB231 cells, and the maximum amplitude sensitivity of 899.248 RIU-1\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$^{-1}$$\end{document} is achieved for the MCF7 cell. The greatest sensor resolution of 3.33 ×\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\times $$\end{document} 10-5\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$^{-5}$$\end{document} RIU is achieved for the Basal cell. Therefore, the proposed PCF SPR sensor with its simple geometry and overall high-performance characteristics is a promising candidate for the early detection of six different cancer cells. [ABSTRACT FROM AUTHOR]

Cancer is a leading cause of morbidity and mortality worldwide. Millions of individuals throughout the world suffer from cancer and other illnesses. This is a leading cause of human death each year. Huge efforts were made to develop simple, precise, and cost-effective technologies for detecting various diseases. As the detection of cancer and diabetic disorders has received a lot of interest in the field of biosensing, several methods have been developed to detect these diseases with high accuracy. This study investigates the possibility of surface plasmon resonance (SPR) for human breast cancer detection utilizing a bimetal silver with gold in permutation and hybrid organic–inorganic halide perovskites (MAPbX3 ≡ CH3NH3PbY3, with M = CH3, A = NH3, and Y = Br) sensor that operates at a specific wavelength of 633 nm. Early cancer identification is critical for improving patient outcomes, and SPR presents a promising path for label-free, sensitive detection of cancer biomarkers. [ABSTRACT FROM AUTHOR]

Surface plasmon resonance (SPR) is used in this article to introduce a novel multilayer configuration and investigate its efficacy as a cancer detection sensor. Our proposed structure comprises an analyte-containing sensing layer underneath an alloy (Au–Ag) and a WS2 layer on top. Utilizing angle interrogation for analysis and a BK7 prism set up in the Kretschmann configuration, SPR is induced. Sellmeier equations are utilized to compute reflectivity and additional parameters of the multilayer design. According to our analysis, the maximum sensitivity of 304°/RIU was achieved with alloy metal and 2*WS2 layered SPR sensor configuration, which is significantly higher than recent SPR-based sensors at 662-nm wavelength. The proposed sensor measured the refractive index (RI) of the sensing medium (SM) at 1.399, which is a tiny change, and found a penetration depth (PD) of 189.79 nm. The proposed sensor performance parameters for various cancer types, including skin, cervical, and breast cancers, have been examined. The proposed SPR sensor shows a high potential for accurately detecting different biomolecules, as evidenced by the 39.33/RIU FoM achieved at 1.399 RI of SM with 37 nm thickness of alloy metal layer. [ABSTRACT FROM AUTHOR]

In this work, we propose a THz metamaterial for biomedical applications. The full vectorial finite element method is used to design and analyze the reported biosensor. The proposed sensor is based on increasing the confinement of the electric and magnetic fields at the analyte layer at the resonance frequency. Hence, any slight variation of the optical properties of the analyte sample (typically the refractive index) can be detected. We demonstrate the potential of using the reported sensor for hemoglobin (Hb) concentration and early cancer detection. The geometrical parameters are studied to maximize the sensor sensitivity of the symmetric and asymmetric designs. An absorptivity of 0.98 is achieved at 1.1 THz, which depends on the analyte sample refractive index. High sensor sensitivity of 1.08 GHz/g/dL is obtained with high Q-factor of 13.2 and FWHM (full width at half maximum) of 140 GHz through hemoglobin (Hb) concentration change from 5 g/dL to 20 g/dL. Further, an average sensitivity of 556.325 GHz/RIU is realized for cancer early detection for basal cell, breast cell, Jurkat cell and Cervical cell. Therefore, the proposed design is a good candidate for biomedical applications. [ABSTRACT FROM AUTHOR]

Essentially, cancer refers to the formation of abnormal cells in any section or area of the body. The goal of early cancer detection is to identify patients who are showing signs early on in order to maximise their chances of a successful therapy. Early detection and treatment of cancer reduce the disease mortality. A wide range of image processing and machine learning techniques have been presented for the identification of cancer. There was no improvement in detection accuracy or efficiency with existing systems. To overcome these problems, we present the Least Mean Square Filterative Ricker Wavelet Transform-based Deep Convolutional Neural Learning Classifier Model (L-DCNLC). By using a fully connected max pooling deep convolutional network with higher accuracy and reduced time consumption, the L-DCNLC Model aims to identify cancer early. There are three hidden layers and one output layer in the fully linked max pooling deep convolutional network. The number of patient photographs in the database is used as input in the L-DCNLC Model's input layer. Following that, preprocessing is performed in hidden layer 1 to perform denoising in order to improve image quality. This is accomplished by employing the Least Mean Square Weiner Filtering process, which has a higher peak signal-to-noise ratio. The Continuous Ricker Wavelet Transform is then used to extract the image features from the preprocessed image in hidden layer 2. Finally, the classification process is performed in hidden layer 3 using the Kulczynksi Similarity Coefficient to detect the cancerous image by comparing the testing and extracted features. Thus, the cancerous image is detected and displayed with a low error rate in the output layer. The brain cancer and lung cancer datasets are analysed to determine peak signal to noise ratios (PSNR), cancer detection accuracy, and cancer detection time. The evaluation results indicate that the L-DCNLC Model improves accuracy and PSNR while requiring less computational time than previous works. [ABSTRACT FROM AUTHOR]

Background: The Toronto protocol for cancer surveillance in children with Li-Fraumeni syndrome has been adopted worldwide. Objective: To assess the diagnostic accuracy of the imaging used in this protocol. Materials and methods: We conducted a blinded retrospective review of imaging modalities in 31 pediatric patients. We compared imaging findings with the reference standards, which consisted of (1) histopathological diagnosis, (2) corresponding dedicated imaging or subsequent surveillance imaging or (3) clinical outcomes. We individually analyzed each modality's diagnostic performance for cancer detection and assessed it on a per-study basis for chest and abdominal regional whole-body MRI (n=115 each), brain MRI (n=101) and abdominal/pelvic US (n=292), and on a per-lesion basis for skeleton/soft tissues on whole-body MRI (n=140). Results: Of 763 studies/lesions, approximately 80% had reference standards that identified 4 (0.7%) true-positive, 523 (85.3%) true-negative, 5 (0.8%) false-positive, 3 (0.5%) false-negative and 78 (12.7%) indeterminate results. There were 3 true-positives on whole-body MRI and 1 true-positive on brain MRI as well as 3 false-negatives on whole-body MRI. Sensitivities and specificities of tumor diagnosis using a worst-case scenario analysis were, respectively, 40.0% (95% confidence interval [CI]: 7.3%, 83.0%) and 38.2% (95% CI: 29.2%, 48.0%) for skeleton/soft tissues on whole-body MRI; sensitivity non-available and 97.8% (95% CI: 91.4%, 99.6%) for chest regional whole-body MRI; 100.0% (95% CI: 5.5%, 100.0%) and 96.8% (95% CI: 90.2%, 99.2%) for abdominal regional whole-body MRI; sensitivity non-available and 98.3% (95% CI: 95.3, 99.4) for abdominal/pelvic US; and 50.0% (95% CI: 2.7%, 97.3%) and 93.8% (95% CI: 85.6%, 97.7%) for brain MRI. Conclusion: Considerations for optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading may enhance the diagnostic accuracy for tumor surveillance. [ABSTRACT FROM AUTHOR]

With increase in cancer burden worldwide and poor survival rates due to delayed diagnosis, it is pertinent to develop a device for early diagnosis. We report an electrochemical biosensor for quantification of miRNA-204 (miR-204) biomarker that is dysregulated in most of the cancers. The proposed methodology uses the gold nanoparticles-modified carbon screen-printed electrode for immobilization of single-stranded DNA probe against miR-204. Colloidal gold nanoparticles were synthesized using l-glutamic acid as reducing agent. Nanoparticles were characterized by UV–visible spectroscopy and transmission electron microscopy. Spherical gold nanoparticles were of 7–28 nm in size. Biosensor fabricated using these nanoparticles was characterized by cyclic voltammetry after spiking 0.1 fg/mL–0.1 µg/mL of miR-204 in fetal bovine serum. Response characteristics of the miR-204 biosensor displayed high sensitivity of 8.86 µA/µg/µL/cm2 with wide detection range of 15.5 aM to 15.5 nM. The low detection limit makes it suitable for early diagnosis and screening of cancer. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise. Methods: Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement. Results: We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively. Conclusions: Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement. [ABSTRACT FROM AUTHOR]

This paper has modelled and quantitatively analyzed a hollow-core PCF to evaluate its performance as a biosensor. This biosensor is specifically designed to sense four types of cancerous cells, namely Jurkat, HeLa, MCF-7, and Basal, representing the names of blood, cervical, breast, and skin cells. The designed PCF has maintained a minimum of 0.2827 numerical aperture at 1.3 µm wavelength for the four types of cells. The effective absorption and confinement loss values have been exceptionally low for this PCF. The maximum values of these two parameters are only 2.43 × 10–7 cm−1 and 1.91 × 10–8 dB/m correspondingly at 1.3 µm. At the same wavelength, this biosensor has offered a higher value of relative sensitivity for the four types of cells that vary from 88.12% to 89.65%. In addition to these traditional values of performance indices, the simple PCF structure offers a broad likelihood of implementation using the persisting fabrication process. [ABSTRACT FROM AUTHOR]

Contribution of quantum dots (QDs) and nano dots (NDs) in boosting the plasmonic behavior of noble metal nano cavity structure and its refractive index (RI) sensing is proposed across an ultrabroad RI range via first, second, and third near-infrared windows. An optimized refractive index sensor based on plasmonic-photonic interaction arising from the extraordinary optical transmission (EOT) through nano cavity is reported. Herein, the sensing platform comprised of an array of silver square nano disc and ring having cavity in between. By the introduction of dots, the absorption profile of entire system is dramatically modified resulting from the plasmon-exciton/plasmon-plasmon coupling and thereby attains higher tunability in sensing parameters together with an enhanced figure of merit (FOM), all confirmed by finite element method (FEM) stimulations. The tailorable hybrid structure demonstrating absorbance enhancement and resonant peak shift with cavity area, embedded material permittivity, presence of dots, etc., proposes an open platform for optical-based chemical and biosensing applications. [ABSTRACT FROM AUTHOR]

Today, the cost of treating cancer is very high at the medical center. So, early detection of the cancer is essential, and one of the possible ways to diagnose cancer in the early stages is to use sensors that are made in the terahertz area. The proposed absorber is a multilayer structure in which graphene is used in the upper and middle layer and the middle layer has a ribbon form. A disk form element is used for the upper layer. Finally, a ring is added to the structure. In the final absorber, the ring actually encompasses the field, and this will increase the Q-factor at 6.7 THz. The interaction of layers on each other and their effect on reflection is studied to modify the proposed absorber by variation of the chemical potential of the graphene layer in the range of 0.2 to 0.6 eV. Finally, the proposed absorber is used to discriminate cancer and healthy breast tissue. The effect of the thickness and distance of the tissue from the absorber is examined to realize the material effect on sensitivity and figure of merit (FOM) as two main factors for detecting the cancer tissue. [ABSTRACT FROM AUTHOR]

Purpose: Unintended weight loss (UWL) is a common presenting symptom in the emergency department (ED) with several etiologies. Our study looks to evaluate the diagnostic utility of computed tomography (CT) in the evaluation of UWL in the ED.Methods: We identified all patients who underwent CT of the chest, abdomen, or pelvis in the ED at our institution for the diagnosis of UWL from 2004 to 2020 and retrospectively reviewed their clinical history and imaging. CT findings were organized into 4 types: (1) definite cause for UWL identified, (2) possible findings for UWL, (3) incidental findings unrelated to UWL, and (4) normal scan. Associations between clinical and laboratory findings with positive CT scans were also examined.Results: One hundred seventy-three eligible patients were identified; 40 patients were excluded due to history of malignancy or inadequate follow-up. One hundred thirty-three patients were included in the final cohort. Overall, the most common causes of UWL were non-malignant gastrointestinal (GI) conditions (n = 41, 30%) and cancer (n = 30, 23%). True-positive CT findings were identified in 48.8% of patients (65/133). Elevated white blood cell counts (p = <0.0001) and physical exam abnormalities (p = 0.02) were both significantly associated with CT abnormalities.Conclusion: The use of CT scanning in the evaluation of UWL in the ED yielded a diagnosis in approximately half of all cases, indicating good diagnostic value. The most common causes of UWL were non-malignant GI conditions and cancer in this cohort. [ABSTRACT FROM AUTHOR]

Nowadays, early cancer identification and surveillance have become vital problems. This research paper explores the development of a small, three-band sensor harnessing the potential of terahertz (THz) technology and metamaterials (MTMs) to diagnose blood cancer. The proposed sensor holds the promise of a paradigm shift in the diagnosis of blood cancer by offering a non-invasive and highly accurate approach. Terahertz radiation, occupying the unique “THz gap” in the electromagnetic spectrum, is now accessible due to recent technological breakthroughs. This work simplifies the design of multiple-band metamaterial absorbers, enhancing their effectiveness and expanding their sensing capabilities. Through the integration of THz technology, metamaterial engineering, and cancer detection, the suggested sensor seeks to launch a new phase of rapid, precise, and non-invasive blood cancer diagnosis. The proposed structure is capable of distinguishing cancer and normal cell with 1 GHz sensitivity, which would be more pronounced when we consider the THz technology devices. This work represents a significant step forward in non-invasive, accurate diagnostics for blood cancer, promising to revolutionize the way this disease is diagnosed and treated. The proposed novel strategy has a lot of promise to advance medical diagnostics and enhance patients’ outcomes. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening disease and has witnessed a substantial increase during the past few years. With a further expected increase in the future, it poses a huge challenge for the medical industry to design accurate detection approaches. Earlier and accurate detection of these cancers increases the surviving chances of patients. Computer-aided diagnosis (CAD) can provide significant help in assisting medical experts to make timely and accurate diagnoses. Deep learning models show great promise but are limited by the need for larger datasets for training and higher computational complexity. This study proposes a resource-efficient EfficienetNetB4 model that incorporates local binary pattern features to enhance the detection accuracy of cancer. The proposed approach integrates histopathological images and texture-based features with EfficientNetB4. Extensive experiments involving several pre-trained convolutional neural network variants are carried out. Results show the superior results of the proposed EfficientNetB4 model with a 99.8% accuracy. In addition, precision, recall, and F1 scores are 99.9% each thereby indicating the exceptional results for cancer detection exceeding the existing state-of-the-art approaches. Using a 5-fold cross-validation provides an average 99.88% accuracy further validating the performance of EfficientNetB4. Furthermore, the use of Shapley additive explanations helps comprehend the results and increases the transparency of the decision-making process. This study potentially contributes to further research in CAD-based resource-efficient cancer detection. [ABSTRACT FROM AUTHOR]

Purpose: Oral squamous cell carcinoma (OSCC) has not seen a substantial improvement in patient survival despite therapeutic advances, making accurate detection and characterization of the disease a clinical priority. Here, we aim to demonstrate the effectiveness of magnetic resonance imaging (MRI) with the targeted MRI contrast agent MT218 specific to extradomain-B fibronectin (EDB-FN) in the tumor microenvironment for detection and characterization of aggressive OSCC tumors.Procedures: EDB-FN expression was evaluated in human normal tongue and OSCC specimens with immunohistochemistry. Invasiveness of human CAL27, HSC3, and SCC4 OSCC cells was analyzed with spheroid formation and transwell assays. EDB-FN expression in the cells was analyzed with semiquantitative real-time PCR, western blotting, and a peptide binding study with confocal microscopy. Contrast-enhanced MRI with MT218 was performed on subcutaneous OSCC mouse models at a dose of 0.04 mmol/kg, using gadoteridol (0.1 mmol/kg) as a control.Results: Strong EDB-FN expression was observed in human untreated primary and metastatic OSCC, reduced expression in treated OSCC, and little expression in normal tongue tissue. SCC4 and HSC3 cell lines demonstrated high invasive potential with high and moderate-EDB-FN expression, respectively, while CAL27 showed little invasive potential and low-EDB-FN expression. In T1-weighted MRI, MT218 produced differential contrast enhancement in the subcutaneous tumor models in correlation with EDB-FN expression in the cancer cells. Enhancement in the high-EDB-FN tumors was greater with MT218 at 0.04 mmol/kg than gadoteridol at 0.1 mmol/kg.Conclusions: The results suggest EDB-FN has strong potential as an imageable biomarker for aggressive OSCC. MRMI results demonstrate the effectiveness of MT218 and the potential for differential diagnostic imaging of oral cancer for improving the management of the disease. [ABSTRACT FROM AUTHOR]

It is well-known that near-infrared (NIR) light sources are appropriate to ablate benign tumor irreversibly using heat treatment even in deep tissues. The laser light penetration into the skin in these wavelengths is deep (3–5 mm). Applying new stable materials for emitting NIR wavelengths in tumor positions can help cancer treatment. In this paper, synthesis of the conjugated core-multishell Ag/SiO2/Ag and Au/SiO2/Au quantum dots (QDs) with indocyanine green (ICG) is done and their theoretical and experimental absorptions and emissions in the NIR region are investigated. Thus, heat generation (high-resolution medical imaging capabilities) and emission enhancement are explained and described based on the FRET model for the proposed core-multishell QDs and it is shown that Ag/SiO2/Ag with ICG presents 4 times higher emission rate versus ICG alone in NIR region. Also, because of the plasmon hybridization and also resonance light penetration enhancement, the temperature in tissues increases that is useful for photothermal therapy and NIR high-resolution medical imaging for deep tissues. As an alternative application, these nanoparticles with amazing features are used as a heat source in cancer treatment for shallow and deep tissues. Finally, it is shown that Ag/SiO2/Ag QDs are the best solution for this purpose. [ABSTRACT FROM AUTHOR]

This IRB-approved prospective pilot study evaluates the safety and feasibility of performing stereotactic robot-assisted transperineal MRI-US fusion targeted prostate biopsy under local anaesthesia (LA) with sedation. 30 patients who underwent robotic transperineal prostate biopsy between September 2017 and June 2018 were recruited. All biopsies were performed with the iSR'obot Mona Lisa® and BK3000 ultrasound system. Intravenous paracetamol 1 g, with midazolam and fentanyl were given at positioning. After administration of 5 mL of 1%-lidocaine into the perineal skin 2 cm above and lateral to the anus, periapical prostatic block with 10 mL mixture of 1%-Lidocaine and 0.5%-Marcaine was given. The median age of patients was 66 years (range 53–80 years). Median PSA and mean prostate volume were 8.1 ng/ml (range 4.2–20.6 ng/ml) and 40.1 cc (range 18.6–70 cc). 24 (80.0%) patients had targeted prostate biopsy, with median number of targeted cores of 8 (range 5–16). All patients had saturation biopsy and median number of saturation cores was 21 (range 9–48). Mean dose of intravenous midazolam given was 1.5 mg (range 0–5 mg) and intravenous fentanyl was 75 mcg (10–150 mcg). No patient required conversion to GA. Two patients required motion compensation of 3 mm and 7.5 mm, respectively, due minor movement. Immediate post-operative pain score was 0 for all patients. 29 of 30 patients (96.7%) were discharged within 24 h of procedure. There were no immediate severe complications. Adenocarcinoma was detected in 19/30 (63.3%) cases. This pilot feasibility study showed that stereotactic robotic transperineal MRI-US fusion targeted prostate biopsy can be safely and accurately performed under LA with sedation. [ABSTRACT FROM AUTHOR]

Molecular biology studies on cancer, using gene expression datasets, have revealed that the datasets have a very small number of samples. Obtaining medical data is difficult and expensive due to privacy constraints. Accuracy of classifiers depends greatly on the quality and quantity of input data. The problem of small sample size or small data size has been addressed by augmentation. Owing to the sensitivity of synthetic data samples for the cancer data classification for gene expression data, this paper is motivated to investigate data augmentation using GAN. GAN is based on the principle of two blocks (generator and discriminator) working in a collaborative yet adversarial way. This paper proposes modified generator GAN (MG-GAN) where the generator is fed with original data and multivariate noise to generate data with Gaussian distribution. As the generated data lie within latent space, we reach saddle point faster. GAN has been widely used in data augmentation for image datasets. As per our understanding, this is the first attempt of using GAN for augmentation on gene expression dataset. The performance merit of proposed MG-GAN was compared with KNN and Basic GAN. As compared to KNN and GAN, MG-GAN improves classification accuracy by 18.8% and 11.9%, respectively. The loss value of the error function for MG-GAN is drastically reduced, from 0.6978 to 0.0082, ensuring sensitivity of the generated data. Improved classification accuracy and reduction in the loss value make our improved MG-GAN method better suited for critical applications with sensitive data. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) remains one of the most common and deadly malignancies despite advancements in screening, diagnostic capabilities, and treatment. The ability to detect and remove precancerous and cancerous lesions via screening has altered the epidemiology of the disease, decreasing incidence, mortality, and late-stage disease presentation. The fecal immunochemical test (FIT) is a screening test that aims to detect human hemoglobin in the stool. FIT is the most common CRC screening modality worldwide and second most common in the United States. Its use in screening programs has been shown to increase screening uptake and improve CRC outcomes. However, FIT-based screening programs vary widely in quality and effectiveness. In health systems with high-quality FIT screening programs, only superior FIT formats are used, providers order FIT appropriately, annual patient participation is high, and diagnostic follow-up after an abnormal result is achieved in a timely manner. Proper utilization of FIT involves multiple steps beyond provider recommendation of the test. In this commentary, we aim to highlight ongoing challenges in FIT screening and suggest interventions to maximize FIT effectiveness. Through active engagement of patients and providers, health systems can use FIT to help optimize CRC screening rates and improve CRC outcomes. [ABSTRACT FROM AUTHOR]

A nanobiocomposite was prepared from multiwalled carbon nanotubes and zein nanoparticles. It was dispersed in water/ethanol and drop cast onto a glassy carbon electrode. The modified electrode can be used for electroreduction of H2O2 (typically at a working potential of −0.71 V vs. Ag/AgCl). The electrochemical properties of the electrode were investigated by cyclic voltammetry, linear sweep voltammetry, chronoamperometry and electrochemical impedance spectroscopy. Response to H2O2 is linear in the 0.049 to 22 μM concentration range, and the detection limit is 35 nM at pH 7.0. The sensor was successfully utilized for the measurement of H2O2 in a synthetic urine sample, and for monitoring the release of H2O2 from human dermal fibroblasts and human hepatocellular carcinoma cells. [ABSTRACT FROM AUTHOR]

A turn-on fluorometric assay is described for determination of the activity of enzyme telomerase. For this purpose, graphene quantum dots (GQDs) were first modified with the telomeric sequence (5′-amino-AATCCGTCGAGCAGAGTT-3′) via a condensation reaction. Injection of graphene oxide causes instant quenching of the blue fluorescence of the GQDs. Addition of cell extract containing telomerase, triggers the extension of telomer via addition of specific sequence (TTAGGG)n to its 3′ end. Fluorescence, best measured at excitation/emission wavelengths of 390/446 nm, is subsequently restored due to folding of the extended telomeric sequence into G-quadruplex structure. The method was applied to the determination of telomerase activity in crude cell extracts of as little as 10 HeLa cells. The linear dynamic range extends from 10 to 6500 cells. [ABSTRACT FROM AUTHOR]