Abnormal Mass Of Tissue Research Articles

“A Study To Assess The Effectiveness Of Planned Teaching Programme On Ovarian Germ Cell Tumor Among Adolescent Girls In A Selected College At Udaipur.”

Background Of The Study Adolescence is a transitional stage of physical and mental development generally occurring between puberty and legal adulthood, but largely characterized as beginning and ending with the teenage stage. Reaching girls during adolescence is critical decisions made and behaviors established during this period affect their horizons later in life. Many serious diseases in adulthood have their roots in adolescence. Germ cell tumors include a diverse group of tumors that arise from primitive germinal cells in either the gonads (ovaries, testicles).Germ cell tumors can occur almost anywhere in the body and can be either benign or malignant. Ovarian germ cell tumors are abnormal mass of tissue that forms in germ (egg) cells in the ovary. These tumors usually occur in teenage girls or young women, usually affect just one ovary and can be benign. Ovarian germ cell tumors represent 15 to 20 % of all ovarian tumors. They are rapidly growing neoplasm that arises from primordial germ cells derived from the embryo gonad. Of all gynecological tumors, ovarian malignancies represent the greatest clinical challenge. Ovarian cancer is the second most common malignancy of the female reproductive system and one of the leading causes of death among gynaecologic malignancies. Ovarian cancer represents a major surgical challenge requiring intensive and often complex therapies. It has the highest fatality-to-case ratio of all the gynaecological malignancies. Proper education is absolutely necessary in order to improve the adolescents’ knowledge. Early detection of ovarian germ cell tumor is essential to decrease the chances of complications. The aim of this study was to determine the effectiveness of planned teaching programme on ovarian germ cell tumor among adolescent girls in a selected college at Udaipur

Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI

ABSTRACT A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%.

Design Optimization and Tradeoff Analysis of an Actuated Continuum Probe for Pulmonary Nodule Localization and Resection.

Pulmonary nodules are abnormal tissue masses in the lungs, typically less than 3.0 cm in diameter, commonly detected during imaging of the chest and lungs. While most pulmonary nodules are not cancerous, surgical resection may be required if growth is detected between scans. This resection is typically performed without the benefit of intraoperative imaging, making it difficult for surgeons to confidently provide appropriate margins. To enhance the efficacy of wedge resection, researchers have developed a modified ultrasound imaging approach that utilizes both multiple scattering (MS) and single scattering (SS) to enhance the accuracy of margin delineation. Clinical deployment of this novel ultrasound technology requires a highly maneuverable ultrasound probe, ideally one that could be deployed and actuated with minimal invasiveness. This study details the design optimization and tradeoff analysis of an actuated continuum probe for pulmonary nodule localization and resection. This device, deployed through intercostal ports, would enable the intraoperative imaging and precise mapping of nodules for improved margin delineation and patient outcomes. To achieve this objective, multiple objective genetic algorithms (MOGAs) and a design of experiments (DOE) study are used to explore the design space and quantify key dimensional relationships and their effects on probe actuation.

OTHER-13 A RETROSPECTIVE STUDY OF BRAIN TUMOURS IN YAOUNDE CAMEROON.

Abstract A brain tumour (BT) or intracranial tumour, is an abnormal mass of tissue where cells grow uncontrollably and unaffected by typical cell regulatory systems. In Sub-Saharan Africa, however, these tumours continue to go unnoticed. The aim of this 5-year retrospective study (January 2018 to March 2023), was to determine the relative frequency and histologic types of brain tumours that exist, and to analyze the costs of treatment and diagnostic methods used. The study was conducted within the oncology and neurosurgery departments of 2 Cameroonian hospitals: Yaoundé Central Hospital and Yaoundé General Hospital. A total of 78 complete case files, or 1.05% of all admissions to the various units, were analyzed. The male to female ratio was 0.95 (38M: 40F). 15.4% of cases involved patients below the age of 14. 84.6% involved patients 15years of age and above. 94.9% of the cases were primary brain tumours. In 60.3% of instances, individuals with primary brain tumours were between the ages of 22 and 57. Meningioma (30.8%) was the most common histological form, followed by astrocytoma (25.6%) and pituitary adenomas (16.7%). Most patients experienced symptoms of increased intracranial pressure, with headaches occurring 94.9% of the time. The average diagnostic cost was 235 000 CFA, average operation cost was 337 500 CFA, and average treatment cost was 750 000 CFA, putting the total cost at 1 322 500 CFA. The country's minimum wage is estimated at 40 000CFA (net income), with average household income of 100 000CFA.The age group that is most impacted is young adults, who were more likely to develop meningioma, astrocytoma, and pituitary adenomas than any other types of brain tumours. The total cost of treatment is 33 times the minimum wage or 13 times the average income. To evaluate the long-term outcome of patients with BTs, more research is required.

A photothermal driven chemotherapy for the treatment of metastatic melanoma

Solid tumors are abnormal mass of tissue, which affects the organs based on its malignancy and leads to the dysfunction of the affected organs. The major problem associated with treatment of solid tumors is delivering anticancer therapeutics to the deepest layers/core of the solid tumor. Deposition of excessive extracellular matrix (ECM) hinders the therapeutics to travel towards the core of the tumor. Therefore, conventional anticancer therapeutics can only reduce the tumor size and that also for a limited duration, and tumor recurrence occurs once the therapy is discontinued. Additionally, by the time the cancer is diagnosed, the cancer cells already started affecting the major organs of the body such as lung, liver, spleen, kidney, and brain, due to their ability to metastasize and lung is the primary site for them to be infiltrated. To facilitate the anticancer therapeutics to penetrate the deeper layers of tumor, and to provide concurrent treatment of both the solid tumor and metastasis, we have designed and developed a Bimodal Light Assisted Skin Tumor and Metastasis Treatment (BLAST), which is a combination of photothermal and chemotherapeutic moieties. The BLAST is composed of 2D boron nitride (BN) nanosheet with adsorbed molecules of BCL-2 inhibitor, Navitoclax (NAVI) on its surface, that can breakdown excessive ECM network and thereby facilitate dissociation of the solid tumor. The developed BLAST was evaluated for its ability to penetrate solid tumors using 3D spheroids for the uptake, cytotoxicity, growth inhibition, reactive oxygen species (ROS) detection, penetration, and downregulation of proteins upon laser irradiation. The in vivo therapeutic studies on a skin cancer mice model revealed that the BLAST with and without laser were able to penetrate the solid tumor, reduce tumor volume in mice, dissociate the protein network, and prevent lung metastasis as confirmed by immunohistochemistry and western blot analysis. Post analysis of serum and blood components revealed the safety and efficacy of BLAST in mice. Hence, the developed BLAST holds strong promise in solid tumor treatment and metastasis prevention simultaneously.

Modeling One Dimensional Two-Cell Model with Tumor Interaction Using Krylov Subspace Methods

A brain tumor occurs when abnormal cells grow, sometimes very rapidly, into an abnormal mass of tissue. The tumor can infect normal tissue, so there is an interaction between healthy and infected cell. The aim of this paper is to propose some efficient and accurate numerical methods for the computational solution of one-dimensional continuous basic models for the growth and control of brain tumors. After computing the analytical solution, we construct approximations of the solution to the problem using a standard second order finite difference method for space discretization and the Crank-Nicolson method for time discretization. Then, we investigate the convergence behavior of Conjugate gradient and generalized minimum residual as Krylov subspace methods to solve the tridiagonal toeplitz matrix system derived.

A study about the nasal obstruction present in cases of malignancy in ent.

Tumours in the nasal cavity and paranasal sinuses were recognized during the time of Hippocrates. Whillis (1948) defined tumours as “an abnormal mass of tissue, the growth of which exceeds and is coordinated with that of the normal tissues and persists in the same excessive manner after cessation of the stimuli which evoked the Change. From the table I it is evident that only 5 cases of malignant and 3 cases of fungal Granuloma, there was bony erosion. In 19 cases of antro chonal polyp and antrum and ethmoid sinus is found to be hazy. From the table I, on radiological study, sinuses are found to be hazy in 86.4% cases of antro choanal polyp and 89.4% cases of ethmoidal polyp. This finding is in accordance with that of miles (1971) who started that usually there is some degree of opacity of sinuses either due to thickening of mucosa or superadded infection. erosion of bone is found in 70% cases of malignancy and the 3 case of fungal granuloma.

Observation of various symptoms of non-neoplastic nasal mass in ent

Tumors in the nasal cavity and paranasal sinuses were recognized during the time of Hippocrates. Whillis (1948) defined tumors as “an abnormal mass of tissue, the growth of which exceeds and is coordinated with that of the normal tissues and persists in the same excessive manner after cessation of the stimuli which evoked the Change” From the table I it is evident that only 5 cases of malignant and 3 cases of fungal Granuloma, there was bony erosion. In 19 cases of antro chonal polyp and antrum and ethmoid sinus is found to be hazy. From the table I, on radiological study, sinuses are found to be hazy in 86.4% cases of antro choanal polyp and 89.4% cases of ethmoidal polyp. This finding is in accordance with that of miles (1971) who started that usually there is some degree of opacity of sinuses either due to thickening of mucosa or superadded infection. erosion of bone is found in 70% cases of malignancy and the 3 case of fungal granuloma.

Association between molecular subtype of local advanced breast cancer with Ca 15-3 level.

Neoplasm is an abnormal mass of tissue that grows excessively and not coordinated with normal tissue growth and continues to do so even though the stimulation that triggered the change has stopped. Breast cancer can be known by using tumor marker, which has been used is mucin-like glycoprotein Carcinoma Antigen (CA 15-3) which is a tumor marker that is specific to breast cancer. This study is a cross-sectional study to determine the association between molecular subtypes of locally advanced breast cancer with CA 15-3 level at Abdul Wahab Sjahranie Samarinda Hospital. The population in this study were all breast cancer patients that were confirmed by histopathological examination. A total of 75 patients were included for this study, 29 patients (38.7%) known as Overexpression HER2, 18 patients (24.0%) were Luminal B with HER2 (+), 11 patients (14.7%) were Luminal B with HER2 (-), 11 patients (14.7%) were Basal-like/TNBC, and 6 patients (8,0%) were Luminal A. From the ANOVA test, the value of p =0.045 (p <0.05) means there was an association between Ca 15-3 level and molecular subtypes in patients with locally advanced breast cancer at the Abdul Wahab Sjahranie Hospital in Samarinda 2017. In this study Ca 15-3 levels were obtained on average for Luminal A 16.98U/mL, Luminal B with HER2 (-) 42.41U/mL, Luminal B with HER2 (+) 73.75U/mL, Overexpression HER2 47.73U/mL, and Basal Like /TNBC 63.50U/mL. Statistically, it was found that there was an association between Ca 15-3 levels and molecular subtypes in patients with locally advanced breast cancer at the Abdul Wahab Sjahranie Hospital in Samarinda 2017.

Retrospective study of histopathological examinations performed on bitches with breast tumors at the veterinary hospital

Neoplasia is an abnormal mass of tissue with atypical growth and without biological purpose that can reach any tissue of the animal organism. However, in dogs, breast neoplasms are more frequent, especially in bitches aged over 8 years. Therefore, it is important to know epidemiological aspects regarding this disease for the best therapeutic choice and, consequently, for the success of the treatment. Thus, the present study aimed to carry out a survey of the reports of histopathological exams performed at the Veterinary Hospital of Uberaba from January 2016 to March 2018 and to verify the most affected breeds, the tumor characteristics, such as malignancy, aspects of the neoplasm, metastatic index and recurrences, the reports of imaging exams and the most frequent hematological changes. Among the most affected animals in this period are the mixed breed dogs, Poodle, BassetHound and Labrador. Elderly animals over 8 years old had higher prevalence (84.8%) compared to adult dogs. The most important hematological changes due to the number of occurrences were thrombocytosis and normochromic normocytic anemia. Breast carcinoma and mixed mammary tumor were the most frequent 12.0 and 27.2% respectively. With this study we saw the need for a good staging of neoplasms, assisting the clinician in the therapeutic decision, improving the prognosis of cancer patients.

Radioiodination of Pimonidazole as a Novel Theranostic Hypoxia Probe.

Tumors are defined as abnormal tissue masses, and one of the most important factors leading to the growth of these abnormal tissue masses is Vascular Endothelial Growth Factor, which stimulates angiogenesis by releasing cells under hypoxic conditions. Hypoxia has a vital role in cancer therapy, thus it is important to monitor hypoxia. The hypoxia marker Pimonidazole (PIM) is a candidate biomarker of cancer aggressiveness. The study aimed to perform radioiodination of PIM with Iodine-131 (131I) to join a theranostic approach. For this purpose, PIM was derived as PIM-TOS to be able to be radioiodinated. PIM was derived via a tosylation reaction. Derivatization product (PIM-TOS) was radioiodinated by using iodogen method and was analyzed by High-Performance Liquid Chromatography and Liquid chromatography-mass spectrometry. Thin layer radiochromatography was utilized for its quality control studies. PIM was derived successfully after the tosylation reaction. The radioiodination yield of PIM-TOS was over 85%. In the current study, radioiodination potential of PIM with 131I, as a potential theranostic hypoxia agent was investigated. Further experimental studies should be performed for developing a novel hypoxia probe including theranostics approaches.

The Tumor Microenvironment of Mif Deficient and Wild Type Mouse Injected of Colon Carcinoma

Tumor is an abnormal mass of tissue, which could be solid, or fluid filled. Migration Inhibitory Factor (MIF) is a ubiquitously expressed cytokine in tumor. High levels of MIF expression have been observed in several human cancers and expression correlates with tumor grading and clinical prognosis. This experiment was designed to see the size of tumor, cell population, arginase and nitric oxide activity and production and presence of pro and anti-inflammatory cytokine in cell and serum of MIF wild type and MIF- /- mouse injected of colon carcinoma tumor cell line.

Identification of Mutation in SCN4A, Hot-Spot for Periodic Paralyses, in a Large Chinese Family with a Typical Normokalemic PP Using the Whole-Exome Sequencing

Tumor is an abnormal mass of tissue, which could be solid, or fluid filled. Migration Inhibitory Factor (MIF) is a ubiquitously expressed cytokine in tumor. High levels of MIF expression have been observed in several human cancers and expression correlates with tumor grading and clinical prognosis. This experiment was designed to see the size of tumor, cell population, arginase and nitric oxide activity and production and presence of pro and anti-inflammatory cytokine in cell and serum of MIF wild type and MIF- /- mouse injected of colon carcinoma tumor cell line.

An Improved Lung Cancer Prediction System using Image Processing

Lung cancer is a disease that causes the cells present in the lungs which divide uncontrollably. This uncontrollable division of cells causes tumours which in turn decrease a person’s respiration. Early identification and diagnosis will help people to seek treatment and recover soon. Tumours are an abnormal mass of tissue that results when cells divide more than they should or do not die when they should. Identifying lung cancer in its early stages is very difficult but knowing about its symptoms is quite easy. Symptoms may be similar to those of respiratory problems or infections and sometimes there may be no symptoms at all. In this work mainly deals with the lung cancer detection using image processing techniques were involving all the intermediate stages such as pre-processing stage, noise removal, processing stage, post-processing stage which finally gives output image after all those stages. Doctors can categorize tumour stage as initial or advanced based on patient CT scan report. The abnormal images are subjected to segmentation (threshold segmentation, watershed transformation) to focus on tumour portion. It mainly deals with image quality and clarity. Gabor filter algorithm plays a vital role for image enhancement in removing noise from an image. The ANN method gives us the best performance as it neglects the background and displays the required portion of an image that we need. This image processing technique is one of the most efficient way of detecting lung cancer.

A robust grey wolf-based deep learning for brain tumour detection in MR images.

In recent times, the detection of brain tumours has become more common. Generally, a brain tumour is an abnormal mass of tissue where the cells grow uncontrollably and are apparently unregulated by the mechanisms that control cells. A number of techniques have been developed thus far; however, the time needed in a detecting brain tumour is still a challenge in the field of image processing. This article proposes a new accurate detection model. The model includes certain processes such as preprocessing, segmentation, feature extraction and classification. Particularly, two extreme processes such as contrast enhancement and skull stripping are processed under the initial phase. In the segmentation process, we used the fuzzy means clustering (FCM) algorithm. Both the grey co-occurrence matrix (GLCM) as well as the grey-level run-length matrix (GRLM) features were extracted in the feature extraction phase. Moreover, this paper uses a deep belief network (DBN) for classification. The optimized DBN concept is used here, for which grey wolf optimisation (GWO) is used. The proposed model is termed the GW-DBN model. The proposed model compares its performance over other conventional methods in terms of accuracy, specificity, sensitivity, precision, negative predictive value (NPV), the F1Score and Matthews correlation coefficient (MCC), false negative rate (FNR), false positive rate (FPR) and false discovery rate (FDR), and proves the superiority of the proposed work.

A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images

In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing. This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here. The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.

Reproductive Factors and Breast Cancer Risk

A neoplasm, also referred to as a tumor, is an abnormal mass of tissue, the growth of which exceeds and is uncoordinated with that of normal tissue and remains in the same excessive manner even after removal of the stimuli which evoked the changes. Tumors may be either benign or malignant. Benign tumors remain localized and are amenable to surgical removal, whereas malignant tumors, i.e. cancerous ones, adhere to any part in an obstinate manner, often invade surrounding tissue, and metastasize to distinct sites. Breast cancer occupies second place as the most occurring cancer in terms of incidence. Breast cancer is a heterogenous cancer, mostly linked with reproductive and hormonal factors in its occurrence. Nulliparous, younger age at menarche, early age pregnancy, older age at first live birth, late menopause, prolonged interval between menarche and first pregnancy, repeated abortions, first pregnancy after 35 years, and no/less breastfeeding are major risk factors for breast malignancies. Recent studies have shown that the implications of reproductive and hormonal factors in patients with breast cancer are associated mostly with BRCA1 (breast cancer gene 1) and BRCA2 (breast cancer gene 2) gene mutations.

A Robust Grey Wolf-based Deep Learning for Brain Tumour Detection in MR Images

In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing. This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here. The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F 1 Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.

Tumors: Benign and Malignant

A tumor is a pathologic disturbance of cell growth, characterized by excessive and abnormal proliferation of cells. Tumors are abnormal mass of tissue which may be solid or fluid filled. When the growth of tumor cells confined to the site of origin and are of normal physicality they are concluded as benign tumors. When the cells are abnormal and can grow uncontrollably, they are concluded as cancerous cells i.e. malignant tumor. Tumors are also called as ‘NEOPLASM’. To determine whether a tumor is benign or cancerous, a doctor can take a sample of the cells with a biopsy procedure. Then the biopsy is analyzed under a microscope by a pathologist (a doctor specializing in laboratory science).