Method For Detection Of Cancer Research Articles

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

Detection of Brain Cancer through Enhanced Particle Swarm Optimization in Artificial Intelligence Approach

Brain cancer is deadly and requires prompt detection and treatment. We propose a complete brain cancer detection method using binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization (PSO), wavelet transform, and neural networks. First, binary encoding converts categorical patient data and medical history information into binary vectors for fast analysis. Adaptive thresholding then handles image lighting and contrast to optimize brain image segmentation. Brain tumor boundaries are identified via edge-based segmentation. This method isolates tumor areas for investigation by recognizing significant pixel intensities. Particle swarm optimization optimizes segmentation algorithm settings, enhancing efficiency and accuracy. Wavelet transform captures local and global brain picture changes, extracting tumor-related information. This method gives a complete visual representation, improving categorization. Finally, utilizing the collected attributes, a neural network model classifies brain pictures as malignant or non-cancerous. The neural network learns the complicated correlations between retrieved variables and brain cancer to classify accurately and automatically. A dataset of brain pictures, comprising malignant and non-cancerous instances, evaluates the proposed approach. The proposed approach accurately detects brain tumors in experiments. Binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization, wavelet transform, and neural networks can help medical professionals diagnose and treat brain cancer early.

Esophageal cancer detection framework based on time series information from smear images

The gold standard for esophageal cancer diagnosis and treatment is the Thinprep Cytologic Test (TCT) of suspected sections. TCT refers to analyze the features of the lesion areas using tissue regions stained with hematoxylin and eosin. However, pathologists are currently subjected to high workloads and limited experiences. Moreover, automatic esophageal cancer detection methods typically screen the smears by focusing on suspicious lesions identified by cytotechnicians, cell misclassification often results in inaccurate and weak robust diagnoses, smear utilization rate is also reduced. Computer-assisted diagnosis also faces challenges such as information loss, non-quantitative analysis, and nonstandard diagnosing processes. To address these issues, this article proposes a deep learning based detection framework called the Esophageal Time Series Thinprep Cytologic Test (ETS-TCT) to provide a quantitative and robust screening of Whole Slide Imaging (WSI) for esophageal cancer. Our system is divided into three modules: Coarse–fine detection model, quantitative analysis model, and Fusion Long Short Term Memory-Support Vector Machine (FLSTM-SVM) model. These modules are organized to map the pathological information from cell-level to smear-level and offer interpretable predictions to pathologists. For each smear, we design the coarse–fine segmentation model for adaptively locating and classifying target cells, give mathematical descriptions for cell Deoxyribonucleic Acid (DNA) values using the quantitative analysis model, establish an early risk screening model for esophageal cancer using the optimized FLSTM-SVM network. To validate the methodology, we use two clinical datasets containing 580 samples, achieving the accuracy, sensitivity, specificity, and area under ROC (AUC) of 92.0%, 93.4%, 94.4%, and 0.913, respectively. In short, the framework provides robust and accurate diagnosis of esophageal cancer WSIs using the time series features. This screening can assist the pathologists in strengthening the diagnosis by robustly presenting the quantitative intermediate results as well as visual interpretations. Moreover, we clearly present and expatiate the concepts of time series features, highlighting the relationship between these features to physiological states of patients. Our results indicate the possibility of using the framework as a second system to the computer-assisted esophageal cancer screening.

Quantum Dot Nanomaterials: Preparation, Characterization, Advanced Bio-Imaging and Therapeutic Applications.

The bio-imaging technology is one of the most significant modern applications used in several fields, including early diagnosis of many illnesses that are most important diseases facing humanity and other vital uses. The primary advancement in nanotechnology is the creation of innovative fluorescence probes called quantum dots (QDs). The use of molecular tagging in research, in vivo, and in vitro studies is revolutionized by quantum dots. The application of QD indicates conversion in natural imaging and photography has demonstrated extraordinary appropriateness in bio-imaging, the discovery of novel drugs, and delivery of targeted genes, biosensing, photodynamic therapy, and diagnosis. New potential methods of early cancer detection and treatment management are being researched as a result of the special physical and chemical characteristics of QD probes. The bio-imaging technique depends on the fluorescent emission of the used materials, which is paired with living cells that are easy to see it in 3D without any surgical intervention. Therefore, the use of QDs many types that have unique and appropriate properties for use in that application; In terms of fluorescent emission strength, duration and luminosity.This review article displays some methods of preparation for QDs nanomaterials and the devices used in this. In addition, it presentssome of challenges that must be avoided for the possibility of using them in the bio-imaging field; as toxicity, bio-compatibility, and hydrophilization. It's reviewed some of the devices that use QDs in bio-imaging technique, the QDs application in cell analysis-imaging, and QDs application in vivo imaging.

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

<span lang="EN-US">Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.</span>

Detection of CYFRA 21-1 in human serum by an electrochemical immunosensor based on UiO-66-NH2@CMWCNTs and CS@AuNPs.

In this study, an electrochemical immunosensor was constructed to detect the cytokeratin 19 fragment antigen 21-1 (CYFRA 21-1) in human serum. CYFRA 21-1 is the most sensitive tumor marker of non-small cell lung cancer (NSCLC), its content in normal human serum should be less than 3.3ng/mL. When lung cancer cells dissolve or die, a myriad of CYFRA 21-1 is released into a tumor patient's blood circulation, and its serum content elevates strikingly. Consequently, detecting CYFRA 21-1 by an electrochemical biosensor is expected to provide a new method for the early detection and prevention of lung cancer. In this study, a composite of UiO-66-NH2 and carboxylated multi-walled carbon nanotubes (CMWCNTs) was used as the substrate material of a sensor; the resulting sensor had a large specific surface area and strong electrical conductivity. Moreover, gold nanoparticles (AuNPs) were used to bind to antibodies through an Au-S bonds. Also, a supersensitive detection of CYFRA 21-1 was achieved through the specific bindings of antigens and antibodies. Under optimal detection conditions, the change of current signal intensity of the immunosensor was proportional to the logarithm of CYFRA 21-1 concentration and had a linear relation in the range of 0.005-400ng/mL, while the detection limit was 1.15pg/mL (S/N=3). The proposed immunosensor had high precision, stability, and selectivity. More importantly, the sensor was been successfully applied to detect CYFRA 21-1 in human serum with high recovery, providing a new method for early screening and dynamic monitoring of lung cancer.

Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability. In recent years, deep learning-based ML approaches developed to improve the diagnostic capabilities of CAD systems. This study reviews image enhancement, ML and DL methods for breast cancer detection and diagnosis using mammogram images and provides an overview of these methods. The analysis of different ways of ML and DL shows that the usages of traditional ML approaches are limited. However, DL techniques have an excellent future for implementing medical image analysis and improving the ability to exist CAD systems. Despite the significant advancements in deep learning methods for analyzing medical images to detect breast cancer, challenges still exist regarding data quality, computational cost, and prediction accuracy.

Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.

An Update on Potential Molecular Biomarkers of Dietary Phytochemicals Targeting Lung Cancer Interception and Prevention.

Since ancient times, dietary phytochemicals are known for their medicinal properties. They are broadly classified into polyphenols, terpenoids, alkaloids, phytosterols, and organosulfur compounds. Currently, there is considerable interest in their potential health effects against various diseases, including lung cancer. Lung cancer is the leading cause of cancer deaths with an average of five-year survival rate of lung cancer patients limited to just 14%. Identifying potential early molecular biomarkers of pre-malignant lung cancer cells may provide a strong basis to develop early cancer detection and interception methods. In this review, we will discuss molecular changes, including genetic alterations, inflammation, signal transduction pathways, redox imbalance, epigenetic and proteomic signatures associated with initiation and progression of lung carcinoma. We will also highlight molecular targets of phytochemicals during lung cancer development. These targets mainly consist of cellular signaling pathways, epigenetic regulators and metabolic reprogramming. With growing interest in natural products research, translation of these compounds into new cancer prevention approaches to medical care will be urgently needed. In this context, we will also discuss the overall pharmacokinetic challenges of phytochemicals in translating to humans. Lastly, we will discuss clinical trials of phytochemicals in lung cancer patients.

Beyond liver cancer, more application scenarios for alpha-fetoprotein in clinical practice.

Alpha-fetoprotein (AFP) is a commonly used clinical biomarker. Before 1970, the two-way agar diffusion method was mainly used, and the specificity of AFP in the diagnosis of primary liver cancer was satisfactory. However, its positivity rate was not very high. The diagnostic value of AFP is changing with the evolution of detection methods. Here, we performed a literature search to identify English-language publications. The search was performed from January 2015 to April 2023 using the PubMed database and the following terms in [Titles/Abstracts]: alpha-fetoprotein, clinical practice, detection, etc. The references of retrieved articles were also screened to broaden the search. Studies referring to liver cancer and AFP detection methods were excluded. In this review, several clinical application scenarios for AFP were systematically reviewed, and its potential detection value in the future was discussed.

Optimizing Drug Delivery Systems for Enhanced Therapeutic Efficacy in Cancer Treatment: A Multidisciplinary Approach

This research aims to develop an early detection method for breast cancer through the analysis of specific biomarkers. The method leverages advanced drug delivery technology to enhance the efficacy of cancer therapy. Employing a multidisciplinary approach, this study integrates medical and pharmaceutical sciences to achieve optimal results. Specific biomarkers utilized in this research will be identified to improve the sensitivity and specificity of cancer detection. The findings of this research are anticipated to make a significant contribution to enhancing early breast cancer detection capabilities and optimizing therapy utilization in patients.

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis.

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Fallopian tube cytology as a diagnostic tool for adnexal malignancy: the CytoSaLPs score.

During the past decade, the theory that high-grade extrauterine pelvic tumors originate from the fallopian tube has been strongly suggested. Our study aims to illuminate the possible role of tubal cytology as an accessory identification tool for gynecologic extrauterine malignancies, allowing in the long term the implementation of population-level cytologic tube evaluation during all benign gynecologic surgeries that do not result in salpingectomy. We exvivo collect salpingeal epithelial cells from the fibria directly from fresh fallopian tube specimens from women undergoing salpingectomy for any indication. The cytomorphologic characteristics of the salpingeal cells are subsequently evaluated and categorized into malignant and non-malignant. Finally, the ipsilateral adnexa are examined with the SEE-FIM (Sectioning and Extensively Examining the FIMbriated End) protocol and the pathology reports are corelated with the cytologic findings. Our research protocol is ongoing and is designed to include a total of 300 patients in order to confirm the sensitivity and specificity of salpingeal cytology as a method in the early diagnosis of extrauterine gynecologic malignancies. So far, we have obtained 343 salpingeal brushings from a total of 214 patients. The sensitivity of cytology regarding distinguishing malignant from non-malignant tumors is 69.64% (95% CI: 55.90%-81.22%), and its specificity 75.96% (95% CI: 70.59%-80.79%). Cytology's positive predictive value (PPV) is 16.33% (95% CI: 12.57%-20.67%), while the negative predictive value (NPP) reached 92.77% (95% CI: 89.56%-95.04%). In general, the diagnostic accuracy of the cytologic evaluation reaches 74.93% (95% CI: 66.99%-79.43%). Salpingeal cytomorphologic evaluation appears to be a promising method for early detection of adnexal cancer.

Peripheral Blood Serum NMR Metabolomics Is a Powerful Tool to Discriminate Benign and Malignant Ovarian Tumors.

Ovarian cancer is the major cause of death from gynecological cancer and the third most common gynecological malignancy worldwide. Despite a slight improvement in the overall survival of ovarian carcinoma patients in recent decades, the cure rate has not improved. This is mainly due to late diagnosis and resistance to therapy. It is therefore urgent to develop effective methods for early detection and prognosis. We hypothesized that, besides being able to distinguish serum samples of patients with ovarian cancer from those of patients with benign ovarian tumors, 1H-NMR metabolomics analysis might be able to predict the malignant potential of tumors. For this, serum 1H-NMR metabolomics analyses were performed, including patients with malignant, benign and borderline ovarian tumors. The serum metabolic profiles were analyzed by multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. A metabolic profile associated with ovarian malignant tumors was defined, in which lactate, 3-hydroxybutyrate and acetone were increased and acetate, histidine, valine and methanol were decreased. Our data support the use of 1H-NMR metabolomics analysis as a screening method for ovarian cancer detection and might be useful for predicting the malignant potential of borderline tumors.

An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images

Breast cancer is the second major reason of death among women around the world. Early and accurate breast cancer detection is important for proper treatment planning to save a life. In this paper, a deep learning-based ensemble classifier is proposed for the detection of breast cancer. The primary contributions are: (1) an efficient deep learning-based breast cancer detection method that can exhibit admirable performance with a small dataset; (2) the integration of three efficient transfer learning models (AlexNet, ResNet, and MobileNetV2), which lead to more accurate results; (3) the use of residual learning, depthwise separable convolution, and inverted residual bottleneck structure to make the system faster, as well as skip connection to make optimization easier and lastly, employing Laplacian of Gaussian (LoG) and modified high-boosting to improve performance. The experimental results convey that the suggested scheme gives superior classification performance by achieving an accuracy of 99.17% to detect abnormality and 97.75% to detect malignancy on the mini-DDSM dataset. Similarly, on the ultrasound dataset (BUSI), it provides accuracies of 96.92% and 94.62% to detect abnormality and malignancy, respectively. It also gives the best performance in another ultrasound dataset, BUS2, with 97.50% accuracy. Therefore, because of its versatility and reliability, the proposed model can be used for breast cancer detection in multimodal datasets.

Detection method has independent prognostic significance in the PLCO lung screening trial

Prognostic models in cancer use patient demographic and tumor characteristics to predict survival and dynamic disease prognosis. Past work in breast cancer has shown that cancer detection method, screen-detected or symptom-detected, has prognostic significance. We investigate this phenomenon in the lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial. Patients were randomized to intervention, receiving four annual chest x-rays (CXRs), or to control, receiving usual care. Patients were followed for a total of approximately 13 years. In PLCO, lung cancer detection method has independent prognostic value exceeding that of variables commonly used in lung cancer prognostic models, including sex, histology, and age. Results are robust to cohort selection and type of predictive model. These results imply that detection method should be considered when developing prognostic models in lung cancer studies, and cancer registries should routinely collect cancer detection method.

COVID-19 related change in breast cancer diagnosis, stage, treatment, and case volume: 2019–2021

PurposeEvaluate the COVID-19 pandemic impact on breast cancer detection method, stage and treatment before, during and after health care restrictions.MethodsIn a retrospective tertiary cancer care center cohort, first primary breast cancer (BC) patients, years 2019–2021, were reviewed (n = 1787). Chi-square statistical comparisons of detection method (patient (PtD)/mammography (MamD), Stage (0-IV) and treatment by pre-pandemic time 1: 2019 + Q1 2020; peak-pandemic time 2: Q2-Q4 2020; pandemic time 3: Q1-Q4 2021 (Q = quarter) periods and logistic regression for odds ratios were used.ResultsBC case volume decreased 22% in 2020 (N = 533) (p = .001). MamD declined from 64% pre-pandemic to 58% peak-pandemic, and increased to 71% in 2021 (p < .001). PtD increased from 30 to 36% peak-pandemic and declined to 25% in 2021 (p < .001). Diagnosis of Stage 0/I BC declined peak-pandemic when screening mammography was curtailed due to lock-down mandates but rebounded above pre-pandemic levels in 2021. In adjusted regression, peak-pandemic stage 0/I BC diagnosis decreased 24% (OR = 0.76, 95% CI: 0.60, 0.96, p = .021) and increased 34% in 2021 (OR = 1.34, 95% CI: 1.06, 1.70, p = .014). Peak-pandemic neoadjuvant therapy increased from 33 to 38% (p < .001), primarily for surgical delay cases.ConclusionsThe COVID-19 pandemic restricted health-care access, reduced mammography screening and created surgical delays. During the peak-pandemic time, due to restricted or no access to mammography screening, we observed a decrease in stage 0/I BC by number and proportion. Continued low case numbers represent a need to re-establish screening behavior and staffing.

Conducting polymer composite-based biosensing materials for the diagnosis of lung cancer: A review

The development of a simple and fast cancer detection method is crucial since early diagnosis is a key factor in increasing survival rates for lung cancer patients. Among several diagnosis methods, the electrochemical sensor is the most promising one due to its outstanding performance, portability, real-time analysis, robustness, amenability, and cost-effectiveness. Conducting polymer (CP) composites have been frequently used to fabricate a robust sensor device, owing to their excellent physical and electrochemical properties as well as biocompatibility with nontoxic effects on the biological system. This review brings up a brief overview of the importance of electrochemical biosensors for the early detection of lung cancer, with a detailed discussion on the design and development of CP composite materials for biosensor applications. The review covers the electrochemical sensing of numerous lung cancer markers employing composite electrodes based on the conducting polyterthiophene, poly(3,4-ethylenedioxythiophene), polyaniline, polypyrrole, molecularly imprinted polymers, and others. In addition, a hybrid of the electrochemical biosensors and other techniques was highlighted. The outlook was also briefly discussed for the development of CP composite-based electrochemical biosensors for POC diagnostic devices.

Optimal lung cancer detection based on CNN optimized and improved Snake optimization algorithm

Lung cancer is an ailment that lessens the patient's capability to breathe because of the excessive spread of lung tissue cells. The cancer spreads after genetic damage to DNA. This genetic damage results in the excessive distribution and proliferation of cells, which ultimately creates a mass called a tumor. Using automatic machine vision techniques is an effective method for lung cancer detection. A novel lung cancer detection methodology for the automatic identification of lung cancer in CT images is proposed in this work This technique is developed by convolutional neural networks (CNNs). Here, the lung cancer images are first preprocessed before being included in the designed CNN. To provide optimal CNN design, a novel modified version of Snake Optimizer is designed and applied to the CNN structure. The proposed model is then tested against the “IQ-OTH/NCCD-Lung Cancer Dataset”. This strategy is also validated by comparing it with some different published methods in the literature. The findings demonstrated that the offered strategy outperformed the others.

Effectiveness of early cancer detection method: magnetic resonance imaging and X-ray technique

Cancer is a major problem plaguing human society today. With the research and development of nano-drug delivery technology and new non-surgical treatment methods, many types of early cancers can be cured or effectively controlled. However, the current treatment methods for advanced cancer are still limited, and efficient early cancer identification is crucial for enhancing patients prognosis and survival rates. Magnetic resonance imaging (MRI) and X-ray technologies are currently the mainstream and generally applicable means of early cancer detection. However, there is a lack of unified comparison and interpretation for their respective applicable cancer detection types. Herein, the paper first provides a comprehensive comparison and explanation of the working principles of the two technologies, as well as their advantages and disadvantages. Further, this article introduces the application of MRI and X-ray technology in the early detection of different common cancer types, including lung, breast, and brain cancers. The paper found that MRI is crucial in the early detection of brain cancer, and X-ray is a common method for lung cancer screening. With further advances in technology, cancer-related deaths can be further curbed.