Identification Of Early Cancer Research Articles

Applications Of Nanomaterials In Improving The Traditional Diagnostic Approach

The broad art of uses of nanomaterials or nanoparticles and nanodevices that are used in medical healthcare to diagnose and cure a variety of diseases has recently developed as a result of recent advancements in pharmaceutical research.So, in this review article, we will discuss the various art of nanomaterials that are used in various forms to develop various nano-devices and nano technologies that are widely used in medical applications, such as cantilevers, which are highly stable devices that are integrated into highly sensitive disease markers in diagnostic detectors and display reliable performance for a long time. These nanoparticles are also employed in the creation of various dosage forms that are used to either cure or diagnose diseases. These nanotechnologies are frequently used as sophisticated tools or gadgets in the early identification of cancer and atherosclerosis in the human body, where subsequent therapy such as nano-surgery may be used to cure them. These are well-known superior materials that are necessary for many fields due to their nano size.   &nbsp

OA35 Early identification of large vessel vasculitis: a specific benefit of a non-specific symptom pathway?

Abstract Background/Aims Since December 2020 in Cornwall there has been a Non-Specific Symptom Pathway (NSSP): a 2-week-wait pathway by which GPs can access a CT thorax, abdomen & pelvis (CT-TAP) to assess non-specific symptoms where there is a suspicion of malignancy, but conventional 2-week-wait referral criteria are not met. An unexpected consequence of this pathway was the finding of cases of suspected large vessel vasculitis (LVV) being referred to rheumatology outpatients. Methods Cases with a new diagnosis of LVV or cancer were identified using the NSSP database over the period of December 2021- May 2022. Clinical and laboratory features were analysed and compared. During this time period a Logiq E10 US machine was acquired by the department, enabling axillary US to be performed for some of the suspected LVV cases that were referred to rheumatology. Results Of the 241 accepted referrals, 14 patients (5.81%) had a subsequent definite cancer diagnosis, 6 (2.49%) had LVV diagnosed as a direct consequence of the NSSP. 5 (83%) cases of LVV had CRP >60 versus 4 (33%) of the cancer patients where measured. Mean CRP was 38.9 in cancer patients and 82.3 in LVV (normal 0-5mg/L). Mean platelet count was 401 in cancer patients, 510 in LVV patients (normal 150-400x109). 3 LVV patients had immunoglobulins and serum protein electrophoresis tested, of which 2 had significantly raised IgG and 1 was confirmed as polyclonal. In one case, significant thoracic aneurysmal dilatation was identified; a 59-year-old previously healthy woman presented to primary care with fatigue, night sweats and arm pain. Investigations showed anaemia, thrombocytosis, polyclonal high immunoglobulins, CRP 74. CT-TAP via NSSP revealed thoracic aorta wall thickening with aneurysmal dilatation, diameter being 4.5cm at the aortic arch. Subsequently the patient was referred to vascular surgeons and rheumatology. Rheumatology clinic review noted no symptoms of cranial giant cell arteritis or polymyalgia rheumatica. Examination revealed significant blood pressure discrepancy between right and left arms (diastolic difference of 62mmHg) and bilateral axillary bruits. Bilateral axillary artery ultrasound revealed significant intima-media thickening: right distal 1.19mm, left distal 1.37mm (normal less than 0.9mm) consistent with LVV. 10mg Prednisolone and subsequently leflunomide 10mg were initiated with resolution of inflammatory markers and symptoms. Surgical intervention was not felt to be required but regular surveillance continues. Conclusion Polyclonal raised IgG, unexplained raised CRP and thrombocytosis, arm blood pressure discrepancy and axillary bruits may be useful indicator tests for referral to the NSSP. LVV diagnosis normally requires extensive inpatient investigations in patients with pyrexia-of-unknown-origin and can be challenging in the absence of PMR or cranial GCA symptoms. As well as early identification of cancer, NSSP has the additional benefit of detecting cases of LVV that may have otherwise gone unnoticed, including the cases with secondary aneurysmal change. Disclosure R. Machin: None. M. Hughes: None.

COMPARATIVE STUDY OF HEURISTIC-BASED SUPPORT VECTOR MACHINE AND NEURAL NETWORK FOR THERMOGRAM BREAST CANCER DETECTION WITH ENTROPY FEATURES

Thermography is a noncontact, noninvasive imaging technology that is commonly utilized in the medical profession. As early identification of cancer is critical, the computer-assisted method can enhance the diagnosis rate, curing, and survival of cancer patients. Early diagnosis is one of the major essential steps in decreasing the health and socioeconomic consequences of this condition, given the high cost of therapy and the large prevalence of afflicted people. Mammography is currently the majorly utilized procedure for detecting breast cancer. Yet, owing to the low contrast that occurs from a thick breast, mammography is not advised for young women, and alternate methods must be investigated. This work plans to develop a comparative evaluation of two well-performing heuristic-based expert systems for detecting thermogram breast cancer. The thermogram images are taken from the standard DMR dataset. Then, the given images are transferred to the pre-processing stage. Here, the input thermogram images are accomplished by contrast enhancement and mean filtering. Then the Gradient Vector Flow Snakes (GVFS) model is adopted for breast segmentation, and Optimized Fuzzy [Formula: see text]-Means Clustering (OFCM) is developed for abnormality segmentation. From the segmented region of interest, the entropy-based features are acquired. In the classification phase, the “Heuristic-based Support Vector Machine” (HSVM) and “Heuristic-based Neural Network” (HNN) are introduced, which diagnose the breast cancer-affected images. The modifications on SVM and NN are extended by the Oppositional Improvement-based Tunicate Swarm Algorithm (OI-TSA). Furthermore, the suggested models are compared to the traditional SVM and NN classifiers, as well as other classifiers, to validate their competitive performance. From the results, the better accuracy and precision of the designed OI-TSA–HNN model are found to be 96% and 98.4%, respectively. Therefore, the findings confirm that the offered approach shows effectiveness in thermogram breast cancer detection.

Knowledge, Attitude, And Practice Towards Colorectal Cancer Screening Among Primary Health Care Physicians In Buraydah, Qassim, Saudi Arabia.

Colorectal cancer is one of the common cancers globally. The knowledge of primary health care physicians (PHPs)regarding screening of colorectal cancer is important in the early identification of cancer and prompt treatment. Therefore, this study aimed to assess the attitude, practices, and knowledge of PHPs working at primary health care centers in Buraydah, Qassim, Saudi Arabia. This was a cross-sectional study conducted among PHPs in Buraydah, Saudi Arabia. Data was collected using a self-administrated validated questionnaire adapted from the National Cancer Institute in the United States of America (USA). Data was analyzed using SPSS version 21.0. Descriptive analysis was carried out to assess knowledge, attitude and practices. A total of 110 primary care physicians participated in the study. Two-thirds (67%) of the participants were Saudi nationals, and a similar proportion (66%) were male. The mean knowledge score was 6.93 ± 3.39. The mean score in the attitude domain was 10.58 ± 4.02. Around three fourth 75% of the physicians would start screening at the age of 45 years. We found a lower level of knowledge but a reasonable degree of attitude. This is an important finding that despite having a better attitude towards CRC screening, the physicians working in primary health care centers of Buraydah were deficient in knowledge which may have adverse impact on their practices. Further large-scale studies are recommended to generate robust evidence on the topic.

Multi-parameter Inputted Logic-Gating on Aptamer-Encoded Extracellular Vesicles for Colorectal Cancer Diagnosis.

Extracellular vesicles (EVs) have emerged as a potential biomarker in liquid biopsy. However, cancer heterogeneity poses significant challenge to precise molecular diagnosis based on single-parameter input. Hence, strategies for analyzing multiple inputs with molecular computing were developed with the aim of improving diagnostic accuracy in liquid biopsy. In the present study, based on the surface of aptamer-encoded EVs, three toe-hold extended DNA aptamers served as specific inputs to perform AND-logic-gating to distinguish between healthy and cancerous EVs. In addition, this strategy has been successfully employed to analyze circulating EVs in clinical samples from colorectal cancer patients and healthy donors. The developed method has a promising future in the analysis of multiplex EV membrane proteins and the identification of early cancer.

A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Early cancer identification is regarded as a challenging problem in cancer prevention for the healthcare community. In addition, ensuring privacy-preserving healthcare data becomes more difficult with the growing demand for sharing these data. This study proposes a novel privacy-preserving non-invasive cancer detection method using Deep Learning (DL). Initially, the clinical data is collected over the Internet via wireless channels for diagnostic purposes. It is paramount to secure personal clinical data against eavesdropping by unauthorized users that may exploit it for personalized interests. Therefore, the collected data is encrypted before transmission over the channel to prevent data theft. Various security measures, including correlation, entropy, contrast, structural content, and energy, are used to assess the proposed encryption method's efficiency. In this paper, we proposed using the Convolutional Neural Network (CNN)-based model and Magnetic Resonance Imaging (MRI) with different techniques, including transfer learning, fine-tuning, and K-fold analysis cancer detection. Extensive experiments are carried out to evaluate the performance of the proposed DL techniques with regard to traditional machine learning approaches such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results show that the CNN-based model has achieved an accuracy of 98.9% and outperforms conventional ML algorithms. Further experiments demonstrate the efficiency of both encryption schemes, achieving entropy, contrast, and energy of 7.9999, 10.9687, and 0.0151, respectively.

Potential of Zinc Oxide Nanoparticles as an Anticancer Agent: A Review

According to reports, one of the leading causes of mortality is cancer. Over the years, numerous approaches have been devised to lessen chronic pain and death as well as to elevate the quality of life. However, a scarcity persists in the effectiveness of cancer treatments. Early cancer identification and medication delivery with excellent specificity to reduce toxicities are two critical elements in ensuring effective cancer treatment. As a result of severe systemic toxicities and issues with current cancer diagnostic and treatment procedures, alternative nanotechnology-based techniques are being employed to improve detection and minimize disease severity. Nanotechnology has shown promising breakthroughs in cancer therapy by eliminating tumours with minimal damage to surrounding healthy cells. Since zinc is one of the necessary trace elements found in large amounts in human body tissues, zinc oxide nanoparticles (ZnO NPs) are said to be the most cost-effective and have the least hazardous characteristics of all metal oxide nanoparticles. In addition, ZnO NPs have several biological uses, notably in the field of drug administration. In this review, we tried to explore the advantage of ZnO NPs in the biomedical field, particularly in the treatment of cancer which can help to facilitate future research progress.

Artificial neural network for lung cancer detection using CT images

Lung Cancer is the second most serious disease in today’s world due to which the mortality rate is increasing every year. Accurate and early identification of cancer in the lung could help people live longer. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. CT scan images provide the complete imaging of tumor development inside the lungs and are extensively used. A lung cancer diagnosis is usually done manually by skilled specialists, and while these approaches are particularly useful in advanced stage detection, it is also a time-consuming operation that is highly reliant on the person. This increases the risk of detecting inaccuracy due to human error procedures, necessitating the use of an automated system. As a result, it is necessary to identify lung cancer using an automated approach to decrease human manual error and enhance the accuracy and convenience of the process. In this proposed method image processing techniques and an artificial neural network (ANN) were employed to create an automatic method for accurate lung cancer detection. In the first step, image noise reduction, the histogram for image enhancement,

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen

The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments. At the same time, early cancer identification is becoming increasingly important in order to minimize fatality. Deep learning (DL) techniques have recently advanced to the point where they can be used to diagnose and classify cancer using biological images. This paper describes a hybrid optimization method that combines in-depth transfer learning-based cancer detection with multispectral photoacoustic imaging. The goal of the PS-ACO-RNN approach is to use ultrasound images to detect and classify the presence of cancer. Bilateral filtration (BF) is often used as a noise removal approach in image processing. In addition, lightweight LEDNet models are used to separate the biological images. A feature extractor with particle swarm with ant colony optimization (PS-ACO) paradigm can also be used. Finally, biological images assign appropriate class labels using a recurrent neural network (RNN) model. The effectiveness of the PS-ACO-RNN technique is verified using a benchmark database, and test results show that the PS-ACO-RNN approach works better than current approaches.

Integral Multielement Signals by DNA-Programmed UCNP-AuNP Nanosatellite Assemblies for Ultrasensitive ICP-MS Detection of Exosomal Proteins and Cancer Identification.

Exosomes are expected to be used as cancer biomarkers because they carry a variety of cancer-related proteins inherited from parental cells. However, it is still challenging to develop a sensitive, robust, and high-throughput technique for simultaneous detection of exosomal proteins. Herein, three aptamers specific to cancer-associated proteins (CD63, EpCAM, and HER2) are selected to connect gold nanoparticles (AuNPs) as core with three different elements (Y, Eu, and Tb) doped up-conversion nanoparticles (UCNPs) as satellites, thereby forming three nanosatellite assemblies. The presence of exosomes causes specific aptamers to recognize surface proteins and release the corresponding UCNPs, which can be simultaneously detected by inductively coupled plasma-mass spectrometry (ICP-MS). It is worth noting that rare earth elements are scarcely present in living systems, which minimize the background for ICP-MS detection and exclude potential interferences from the coexisting species. Using this method, we are able to simultaneously detect three exosomal proteins within 40 min, and the limit of detection for exosome is 4.7 × 103 particles/mL. The exosomes from seven different cell lines (L-02, HepG2, GES-1, MGC803, AGS, HeLa, and MCF-7) can be distinguished with 100% accuracy by linear discriminant analysis. In addition, this analytical strategy is successfully used to detect exosomes in clinical samples to distinguish stomach cancer patients from healthy individuals. These results suggest that this sensitive and high-throughput analytical strategy based on ICP-MS has the potential to play an important role in the detection of multiple exosomal proteins and the identification of early cancer.

Smart Digital Mammographic Screening System for Bulk Image Processing

Treating breast cancer is easier at early stages. However, proper diagnosis is essential for this purpose. Mammography helps in early detection of cancer cells. Existence of masses, calcification and mammogram are the evidences that help radiologists in early cancer identification. This paper proposes a smart digital mammographic screening system for processing images in large volumes irrespective of the nature of images. Watershed segmentation is performed based on appropriate selection of internal and external markers using multiple threshold extended maxima transformations in this technique. Distinguishing between healthy breast tissue and masses can be performed efficiently using a two-stage classifier. Extreme Learning Machine based single layer feed forward network along with Bayesian classifier is used for reducing false positive areas. Feature vector with features like texture and contrast are calculated using these approaches. Digital Mammography Screening database (DMS) is created with 100 mammographic images for the purpose of evaluation. Further, online databases like Breast Cancer Database (BCDB) and BreakHis are also used for analysis. Overall sensitivity of the datasets using the Bayesian classifier and Extreme Learning Machine is found to be 85% and 90% respectively.

A Review of Exhaled Volatile Organic Compounds as Biomarkers for Thoracic Malignancies

Lung cancer, malignant pleural mesothelioma, and esophageal cancer are the most common thoracic malignancies and are responsible for substantial cancer-related morbidity and mortality worldwide. Early cancer identification prompts earlier intervention and can therefore improve patient survival. Traditional diagnostics are costly and invasive, however, creating an urgent need for alternative methods. Over the past 30 years, breath analysis has emerged as a rapid, minimally invasive, and cost-effective approach. Metabolites in exhaled breath, known as volatile organic compounds (VOCs), reflect internal biomolecular processes and their composition has been shown to vary in association with numerous pathological states. This review provides an overview on the use of VOCs in exhaled breath for the early screening and diagnosis of thoracic malignancies. Study design, methodology, and significant results from over sixty studies published since 1990 are specified and summarized. A total of 439 significant VOCs are reported in the literature, mainly consisting of aromatic compounds, aldehydes, alkanes, lipids, ketones, and sulfur-containing compounds. Diagnostic sensitivities and specificities range from 51-100% and 68.8 – 100%, respectively. Cancer-specific VOC profiles and associations of clinical interest (e.g., comorbidities, histology, and staging) are emphasized and discussed. While there is considerable evidence to support the diagnostic utility of VOCs, the lack of standardization and external validation in large independent cohorts remain key barriers to clinical translation. However, efforts to address these limitations are currently underway.

Cancer surveillance strategies in ulcerative colitis

The risk of colorectal cancer is increased in patients with long-standing ulcerative colitis. Traditional surveillance has centered around regular standard white-light colonoscopy, with multiple biopsies aimed at detecting dysplasia or the identification of early cancer. This has resulted in only a modest reduction in cancer incidence and mortality. A better understanding of disease risk factors may allow endoscopic resources to be more focused on patients at higher risk. In addition, advanced endoscopic techniques have the potential to improve dysplasia detection, minimize the need for routine biopsies, and allow for the removal of dysplastic lesions, avoiding the need for surgery. Techniques such as magnification colonoscopy, chromoendoscopy, narrow band imaging, autofluorescence, and confocal endomicroscopy may all have a role to play in improving the benefits of endoscopic surveillance. Revised endoscopic surveillance strategies are proposed, incorporating aspects of risk stratification, a well-established practice in noncolitis-related colorectal cancer screening, and some of these new technologies.

Cancer genetics in rural primary care: a pilot nurse-led service using a new mobile IT system

Somerset has a relatively high incidence of cancer. The county is very rural with pockets of deprivation and sizeable areas of poor access to services. The Cancer Plan advocates early identification of cancer and identified the genetic revolution as having the potential to predict risk, and detect and diagnose cancer early. This innovative Public Health led pilot takes specialist cancer genetics services into primary care. It explores a new service model that is patient focused, provides services close to home and reduces hospital visits and pressure on genetics departments. People who believe they are at increased risk of developing cancer, due to family history, are assessed by nurse specialists at their local surgery and triaged into population, medium and high risk groups. Patient pathways have been streamlined for breast, ovarian and colorectal cancers, which are known to have a genetic link and there may be advantage in increased surveillance and/or genetic testing. People are given the opportunity to understand their personal level of risk and make choices about lifestyles and interventions. To support the delivery of cancer genetics services in primary care a bespoke software package has been developed building on a commercial programme already established in many genetics departments. Electronic transmission of pedigrees from primary to tertiary care assists the lean pathways and the incorporation of a new risk calculator based on NICE guidelines for familial breast cancer is being assessed.

Novel approaches to identification of biomarkers for detection of early stage cancer.

Implementation of routine cancer screening accompanied by treatment of early stage disease is paramount to realizing reductions in cancer mortality rates. While morphologic assessment of relevant cells has traditionally been used to identify individuals with cancer, this approach is not feasible to identify cancers at inaccessible sites, such as the ovary or pancreas. For these and many other cancers, cancer mortality remains high, as sensitive and specific screening assays to detect in-situ or early stage disease are as yet unavailable. Identification of novel markers for early identification of cancer is an established priority of the National Cancer Institute. The rapid expansion of genomic-based technologies developed over the last decade, and the development of sensitive and specific blood-based assays for the detection of molecular changes associated with cancer, begin to offer the means for achieving this goal. Below, we review current approaches used in the effort to identify biomarkers appropriate for early cancer detection.

Gastrointestinal Laser Endoscopy—Future Horizons

The laser, especially the neodymium:yttrium-aluminum-garnet device, has been a dominant influence on the development of gastrointestinal therapeutic endoscopy. More than 2,000 such procedures were performed in the first 5 years of experience with laser endoscopy at the Mayo Clinic. The major areas for future development are (1) the control of acute and chronic gastrointestinal bleeding, (2) the palliation of malignant gastrointestinal neoplasms, and (3) the management of benign and malignant obstructive lesions of the biliary tract. Refinements in laser devices, delivery systems, and techniques such as photodynamic therapy will be needed to achieve more selective tissue destruction. Improvements in the new adjunctive endoscopic methods of electronic (video) endoscopy and ultrasonography may enhance evolving laser applications by more accurately identifying diseased tissues and guiding their destruction.