Search results for: detecting and classifying tumour
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1087

Search results for: detecting and classifying tumour

1087 Brain Tumor Detection and Classification Using Pre-Trained Deep Learning Models

Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

Abstract:

Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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1086 A Cellular Automaton Model Examining the Effects of Oxygen, Hydrogen Ions, and Lactate on Early Tumour Growth

Authors: Maymona Al-Husari, Craig Murdoch, Steven Webb

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Some tumors are known to exhibit an extracellular pH that is more acidic than the intracellular, creating a 'reversed pH gradient' across the cell membrane and this has been shown to affect their invasive and metastatic potential. Tumour hypoxia also plays an important role in tumour development and has been directly linked to both tumour morphology and aggressiveness. In this paper, we present a hybrid mathematical model of intracellular pH regulation that examines the effect of oxygen and pH on tumour growth and morphology. In particular, we investigate the impact of pH regulatory mechanisms on the cellular pH gradient and tumour morphology. Analysis of the model shows that: low activity of the Na+/H+ exchanger or a high rate of anaerobic glycolysis can give rise to a 'fingering' tumour morphology; and a high activity of the lactate/H+ symporter can result in a reversed transmembrane pH gradient across a large portion of the tumour mass. Also, the reversed pH gradient is spatially heterogenous within the tumour, with a normal pH gradient observed within an intermediate growth layer, that is the layer between the proliferative inner and outermost layer of the tumour.

Keywords: acidic pH, cellular automaton, ebola, tumour growth

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1085 Spontaneous Tumour Lysis in Acute Myeloid Leukemia

Authors: Rojith K. Balakrishnan

Abstract:

Spontaneous tumour lysis syndrome is a constellation of electrolyte abnormalities and an acute renal failure which occurs in the setting of rapid cell turnover prior to the administration of cytotoxic chemotherapy. While spontaneous tumour lysis well-described in patients with Burkitt lymphoma, it is thought to occur less commonly in patients with other hematological malignancies. We present a case of forty-year-old female who presented with features of acute renal failure, on further evaluation turned out to be a newly diagnosed acute myeloid leukemia with spontaneous tumour lysis best of our knowledge only three cases of AML with spontaneous tumour lysis has reported world wide.

Keywords: AML, tumour lysis, renal failure, myeloid leukemia

Procedia PDF Downloads 259
1084 In silico Repopulation Model of Various Tumour Cells during Treatment Breaks in Head and Neck Cancer Radiotherapy

Authors: Loredana G. Marcu, David Marcu, Sanda M. Filip

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Advanced head and neck cancers are aggressive tumours, which require aggressive treatment. Treatment efficiency is often hindered by cancer cell repopulation during radiotherapy, which is due to various mechanisms triggered by the loss of tumour cells and involves both stem and differentiated cells. The aim of the current paper is to present in silico simulations of radiotherapy schedules on a virtual head and neck tumour grown with biologically realistic kinetic parameters. Using the linear quadratic formalism of cell survival after radiotherapy, altered fractionation schedules employing various treatment breaks for normal tissue recovery are simulated and repopulation mechanism implemented in order to evaluate the impact of various cancer cell contribution on tumour behaviour during irradiation. The model has shown that the timing of treatment breaks is an important factor influencing tumour control in rapidly proliferating tissues such as squamous cell carcinomas of the head and neck. Furthermore, not only stem cells but also differentiated cells, via the mechanism of abortive division, can contribute to malignant cell repopulation during treatment.

Keywords: radiation, tumour repopulation, squamous cell carcinoma, stem cell

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1083 Analysis of Radiation-Induced Liver Disease (RILD) and Evaluation of Relationship between Therapeutic Activity and Liver Clearance Rate with Tc-99m-Mebrofenin in Yttrium-90 Microspheres Treatment

Authors: H. Tanyildizi, M. Abuqebitah, I. Cavdar, M. Demir, L. Kabasakal

Abstract:

Aim: Whole liver radiation has the modest benefit in the treatment of unresectable hepatic metastases but the radiation doses must keep in control. Otherwise, RILD complications may arise. In this study, we aimed to calculate amount of maximum permissible activity (MPA) and critical organ absorbed doses with MIRD methodology, to evaluate tumour doses for treatment response and whole liver doses for RILD and to find optimal liver function test additionally. Materials and Methods: This study includes 29 patients who attended our nuclear medicine department suffering from Y-90 microspheres treatment. 10 mCi Tc-99m MAA was applied to the patients for dosimetry via IV. After the injection, whole body SPECT/CT images were taken in one hour. The minimum therapeutic tumour dose is on the point of being 120 Gy1, the amount of activities were calculated with MIRD methodology considering volumetric tumour/liver rate. A sub-working group was created with 11 patients randomly and liver clearance rate with Tc-99m-Mebrofenin was calculated according to Ekman formalism. Results: The volumetric tumour/liver rates were found between 33-66% (Maksimum Tolarable Dose (MTD) 48-52Gy3) for 4 patients, were found less than 33% (MTD 72Gy3) for 25 patients. According to these results the average amount of activity, mean liver dose and mean tumour dose were found 1793.9±1.46 MBq, 32.86±0.19 Gy, and 138.26±0.40 Gy. RILD was not observed in any patient. In sub-working group, the relationship between Bilirubin, Albumin, INR (which show presence of liver disease and its degree), liver clearance with Tc-99m-Mebrofenin and calculated activity amounts were found r=0.49, r=0.27, r=0.43, r=0.57, respectively. Discussions: The minimum tumour dose was found 120 Gy for positive dose-response relation. If volumetric tumour/liver rate was > 66%, dose 30 Gy; if volumetric tumour/liver rate 33-66%, dose escalation 48 Gy; if volumetric tumour/liver rate < 33%, dose 72 Gy. These dose limitations did not create RILD. Clearance measurement with Mebrofenin was concluded that the best method to determine the liver function. Therefore, liver clearance rate with Tc-99m-Mebrofenin should be considered in calculation of yttrium-90 microspheres dosimetry.

Keywords: clearance, dosimetry, liver, RILD

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1082 Role of Mismatch Repair Protein Expression in Colorectal Cancer: A Study from North India

Authors: Alka Yadav, Mayank Jain, Rajan Saxena, Niraj Kumari, Narendra Krishnani, Ashok Kumar

Abstract:

Purpose: To study the mismatch repair (MMR) protein expression and its clinicopathological correlation in colorectal cancer patients in North India. Methods: A prospective study was conducted on histologically proven 52 (38 males and 14 females) patients with adenocarcinoma of colorectum. MMR protein loss was determined by using immunohistochemistry for MLH1, MSH2, PMS2 and MSH6. Results: 52 patients (38 males and 14 females) underwent resection for colorectal cancer with the median age of 52 years (16-81 years). 35% of the patients (n=18) were younger than 50 years of the age. 3 patients had associated history of malignancy in the family. 29 (56%) patients had right colon cancer, 9 (17%) left colon cancer and 14 (27%) rectal cancer. 2 patients each had synchronous and metachronous cancer. Histology revealed well-differentiated tumour in 16, moderately differentiated in 10 and poorly differentiated tumour in 26 patients. MMR protein loss was seen in 15 (29%) patients. Seven (46%) of these patients were less than 50 years of age. Combined loss of MSH2 and MSH6 was seen most commonly and it was found in 6 patients. 12 (80%) patients with MMR protein loss had tumour located proximal to the splenic flexure compared to 3 (20%) located distal to the splenic flexure. There was no difference in MMR protein loss based on patients' age, gender, degree of tumour differentiation, stage of the disease and tumour histological characteristics. Conclusions: This study revealed that there was less than 30% MMR protein loss in colorectal cancer patients. The loss was most commonly seen in right sided colon cancer than left. A larger study is further required to validate these findings.

Keywords: colorectal cancer, mismatch repair protein, immunohitochemistry, clinicopathological correlation

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1081 Comparison of 18F-FDG and 11C-Methionine PET-CT for Assessment of Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Carcinoma

Authors: Sonia Mahajan Dinesh, Anant Dinesh, Madhavi Tripathi, Vinod Kumar Ramteke, Rajnish Sharma, Anupam Mondal

Abstract:

Background: Neo-adjuvant chemotherapy plays an important role in treatment of breast cancer by decreasing the tumour load and it offers an opportunity to evaluate response of primary tumour to chemotherapy. Standard anatomical imaging modalities are unable to accurately reflect the response to chemotherapy until several cycles of drug treatment have been completed. Metabolic imaging using tracers like 18F-fluorodeoxyglucose (FDG) as a marker of glucose metabolism or amino acid tracers like L-methyl-11C methionine (MET) have potential role for the measurement of treatment response. In this study, our objective was to compare these two PET tracers for assessment of response to neoadjuvant chemotherapy, in locally advanced breast carcinoma. Methods: In our prospective study, 20 female patients with histology proven locally advanced breast carcinoma underwent PET-CT imaging using FDG and MET before and after three cycles of neoadjuvant chemotherapy (CAF regimen). Thereafter, all patients were taken for MRM and the resected specimen was sent for histo-pathological analysis. Tumour response to the neoadjuvant chemotherapy was evaluated by PET-CT imaging using PERCIST criteria and correlated with histological results. Responses calculated were compared for statistical significance using paired t- test. Results: Mean SUVmax for primary lesion in FDG PET and MET PET was 15.88±11.12 and 5.01±2.14 respectively (p<0.001) and for axillary lymph nodes was 7.61±7.31 and 2.75±2.27 respectively (p=0.001). Statistically significant response in primary tumour and axilla was noted on both FDG and MET PET after three cycles of NAC. Complete response in primary tumour was seen in only 1 patient in FDG and 7 patients in MET PET (p=0.001) whereas there was no histological complete resolution of tumor in any patient. Response to therapy in axillary nodes noted on both PET scans were similar (p=0.45) and correlated well with histological findings. Conclusions: For the primary breast tumour, FDG PET has a higher sensitivity and accuracy than MET PET and for axilla both have comparable sensitivity and specificity. FDG PET shows higher target to background ratios so response is better predicted for primary breast tumour and axilla. Also, FDG-PET is widely available and has the advantage of a whole body evaluation in one study.

Keywords: 11C-methionine, 18F-FDG, breast carcinoma, neoadjuvant chemotherapy

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1080 Effects of Gamma-Tocotrienol Supplementation on T-Regulatory Cells in Syngeneic Mouse Model of Breast Cancer

Authors: S. Subramaniam, J. S. A. Rao, P. Ramdas, K. R. Selvaduray, N. M. Han, M. K. Kutty, A. K. Radhakrishnan

Abstract:

Immune system is a complex system where the immune cells have the capability to respond against a wide range of immune challenges including cancer progression. However, in the event of cancer development, tumour cells trigger immunosuppressive environment via activation of myeloid-derived suppressor cells and T regulatory (Treg) cells. The Treg cells are subset of CD4+ T lymphocytes, known to have crucial roles in regulating immune homeostasis and promoting the establishment and maintenance of peripheral tolerance. Dysregulation of these mechanisms could lead to cancer progression and immune suppression. Recently, there are many studies reporting on the effects of natural bioactive compounds on immune responses against cancer. It was known that tocotrienol-rich-fraction consisting 70% tocotrienols and 30% α-tocopherol is able to exhibit immunomodulatory as well as anti-cancer properties. Hence, this study was designed to evaluate the effects of gamma-tocotrienol (G-T3) supplementation on T-reg cells in a syngeneic mouse model of breast cancer. In this study, female BALB/c mice were divided into two groups and fed with either soy oil (vehicle) or gamma-tocotrienol (G-T3) for two weeks followed by inoculation with tumour cells. All the mice continued to receive the same supplementation until day 49. The results showed a significant reduction in tumour volume and weight in G-T3 fed mice compared to vehicle-fed mice. Lung and liver histology showed reduced evidence of metastasis in tumour-bearing G-T3 fed mice. Besides that, flow cytometry analysis revealed T-helper cell population was increased, and T-regulatory cell population was suppressed following G-T3 supplementation. Moreover, immunohistochemistry analysis showed that there was a marked decrease in the expression of FOXP3 in the G-T3 fed tumour bearing mice. In conclusion, the G-T3 supplementation showed good prognosis towards breast cancer by enhancing the immune response in tumour-bearing mice. Therefore, gamma-T3 can be used as immunotherapy agent for the treatment of breast cancer.

Keywords: breast cancer, gamma tocotrienol, immune suppression, supplement

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1079 Non-AIDS Related Multiple Brain and Orbital Lymphoma Mimicking Meningioma: A Case Report

Authors: Eghosa Morgan, Bourtarbouch Mahjouba, Heida El Ouahabi, Poluyi Edward, Diawarra Seylan

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Non-AIDS lymphoma, a type of primary central nervous system (CNS) lymphoma is an uncommon aggressive infiltrative malignant tumour involving several sites in the central nervous system, such as the periventricular region and leptomeninges. In this article, the authors presented a 26-year old man with painless progressive right exophthalmos and scalp swelling with no symptoms and signs of intracranial hypertension and hyperthyroidism. Magnetic resonance imaging (MRI) done revealed isointense masses with brilliant homogenous enhancement on contrast administration resembling a meningioma, with a dura tail – like attachment as seen in meningioma. He had surgery for the right orbital tumour and histopathological diagnosis confirmed our suspicion of lymphoma (B type). Steroid was given in the post-operative period which led to significant regression of the tumours, hence its description as ‘vanishing tumour’. He is presently receiving methotrexate-based chemotherapy and subsequently planned for radiotherapy.

Keywords: central nervous system (CNS), meningioma, non-aids lymphoma, orbital

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1078 A Comparative Study of Medical Image Segmentation Methods for Tumor Detection

Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

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Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: features extraction, image segmentation, medical images, tumor detection

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1077 A Comparison of Clinical and Pathological TNM Staging in a COVID-19 Era

Authors: Sophie Mills, Leila L. Touil, Richard Sisson

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Introduction: The TNM classification is the global standard for the staging of head and neck cancers. Accurate clinical-radiological staging of tumours (cTNM) is essential to predict prognosis, facilitate surgical planning and determine the need for other therapeutic modalities. This study aims to determine the accuracy of pre-operative cTNM staging using pathological TNM (pTNM) and consider possible causes of TNM stage migration, noting any variation throughout the COVID-19 pandemic. Materials and Methods: A retrospective cohort study examined records of patients with surgical management of head and neck cancer at a tertiary head and neck centre from November 2019 to November 2020. Data was extracted from Somerset Cancer Registry and histopathology reports. cTNM and pTNM were compared before and during the first wave of COVID-19, as well as with other potential prognostic factors such as tumour site and tumour stage. Results: 119 cases were identified, of which 52.1% (n=62) were male, and 47.9% (n=57) were female with a mean age of 67 years. Clinical and pathological staging differed in 54.6% (n=65) of cases. Of the patients with stage migration, 40.4% (n=23) were up-staged and 59.6% (n=34) were down-staged compared with pTNM. There was no significant difference in the accuracy of cTNM staging compared with age, sex, or tumour site. There was a statistically highly significant (p < 0.001) correlation between cTNM accuracy and tumour stage, with the accuracy of cTNM staging decreasing with the advancement of pTNM staging. No statistically significant variation was noted between patients staged prior to and during COVID-19. Conclusions: Discrepancies in staging can impact management and outcomes for patients. This study found that the higher the pTNM, the more likely stage migration will occur. These findings are concordant with the oncology literature, which highlights the need to improve the accuracy of cTNM staging for more advanced tumours.

Keywords: COVID-19, head and neck cancer, stage migration, TNM staging

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1076 Management of Gap Non-Union Following Tumour Resection of the Distal Femur

Authors: Rajendra Kumar Kanojia

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Correction of the gap created by the resection of large juxtra-articular tumours of the femur would be difficult to manage, several bone substitutes, bone grafts, and artificial bone granules were tried but the results were not as good as with the distraction osteogensis, by the help of either Ilizarov ring fixator or the mono-rail fixators. We are presenting a small study of five cases of malignant tumours of the distal femur, removed, custom made mega prosthesis was applied and that failed twice in a span of five years. We had no better option left then to apply mono-rail fixator, and start the process of distraction osteogeneis, we got the union, gap was filled with new bone and patient has been made walking in few months.

Keywords: distal femur tumour, resection, defect non-union, mono-rail fixator

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1075 Low-Density Lipoproteins Mediated Delivery of Paclitaxel and MRI Imaging Probes for Personalized Medicine Applications

Authors: Sahar Rakhshan, Simonetta Geninatti Crich, Diego Alberti, Rachele Stefania

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The combination of imaging and therapeutic agents in the same smart nanoparticle is a promising option to perform a minimally invasive imaging guided therapy. In this study, Low density lipoproteins (LDL), one of the most attractive biodegradable and biocompatible nanoparticles, were used for the simultaneous delivery of Paclitaxel (PTX), a hydrophobic antitumour drug and an amphiphilic contrast agent, Gd-AAZTA-C17, in B16-F10 melanoma cell line. These cells overexpress LDL receptors, as assessed by Flow cytometry analysis. PTX and Gd-AAZTA-C17 loaded LDLs (LDL-PTX-Gd) have been prepared, characterized and their stability was assessed under 72 h incubation at 37 ◦C and compared to LDL loaded with Gd-AAZTA-C17 (LDL-Gd) and LDL-PTX. The cytotoxic effect of LDL-PTX-Gd was evaluated by MTT assay. The anti-tumour drug loaded into LDLs showed a significantly higher toxicity on B16-F10 cells with respect to the commercially available formulation Paclitaxel Kabi (PTX Kabi) used in clinical applications. It was possible to demonstrate a high uptake of LDL-Gd in B16-F10 cells. As a consequence of the high cell uptake, melanoma cells showed significantly high cytotoxic effect when incubated in the presence of PTX (LDL-PTX-Gd). Furthermore, B16-F10 have been used to perform Magnetic Resonance Imaging. By the analysis of the image signal intensity, it was possible to extrapolate the amount of internalized PTX indirectly by the decrease of relaxation times caused by Gd, proportional to its concentration. Finally, the treatment with PTX loaded LDL on B16-F10 tumour bearing mice resulted in a marked reduction of tumour growth compared to the administration of PTX Kabi alone. In conclusion, LDLs are selectively taken-up by tumour cells and can be successfully exploited for the selective delivery of Paclitaxel and imaging agents.

Keywords: low density lipoprotein, melanoma cell lines, MRI, paclitaxel, personalized medicine application, theragnostic System

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1074 Automatic Differential Diagnosis of Melanocytic Skin Tumours Using Ultrasound and Spectrophotometric Data

Authors: Kristina Sakalauskiene, Renaldas Raisutis, Gintare Linkeviciute, Skaidra Valiukeviciene

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Cutaneous melanoma is a melanocytic skin tumour, which has a very poor prognosis while is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the automatic analysis of spectrophotometric images and high-frequency ultrasonic 2D data can improve differential diagnosis of cutaneous melanoma and provide additional information about tumour penetration depth. This paper presents the novel complex automatic system for non-invasive melanocytic skin tumour differential diagnosis and penetration depth evaluation. The system is composed of region of interest segmentation in spectrophotometric images and high-frequency ultrasound data, quantitative parameter evaluation, informative feature extraction and classification with linear regression classifier. The segmentation of melanocytic skin tumour region in ultrasound image is based on parametric integrated backscattering coefficient calculation. The segmentation of optical image is based on Otsu thresholding. In total 29 quantitative tissue characterization parameters were evaluated by using ultrasound data (11 acoustical, 4 shape and 15 textural parameters) and 55 quantitative features of dermatoscopic and spectrophotometric images (using total melanin, dermal melanin, blood and collagen SIAgraphs acquired using spectrophotometric imaging device SIAscope). In total 102 melanocytic skin lesions (including 43 cutaneous melanomas) were examined by using SIAscope and ultrasound system with 22 MHz center frequency single element transducer. The diagnosis and Breslow thickness (pT) of each MST were evaluated during routine histological examination after excision and used as a reference. The results of this study have shown that automatic analysis of spectrophotometric and high frequency ultrasound data can improve non-invasive classification accuracy of early-stage cutaneous melanoma and provide supplementary information about tumour penetration depth.

Keywords: cutaneous melanoma, differential diagnosis, high-frequency ultrasound, melanocytic skin tumours, spectrophotometric imaging

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1073 Value of FOXP3 Expression in Prediction of Neoadjuvant Chemotherapy Effect in Triple Negative Breast Cancer

Authors: Badawia Ibrahim, Iman Hussein, Samar El Sheikh, Fatma Abou Elkasem, Hazem Abo Ismael

Abstract:

Background: Response of breast carcinoma to neoadjuvant chemotherapy (NAC) varies regarding many factors including hormonal receptor status. Breast cancer is a heterogenous disease with different outcomes, hence a need arises for new markers predicting the outcome of NAC especially for the triple negative group when estrogen, progesterone receptors and Her2/neu are negative. FOXP3 is a promising target with unclear role. Aim: To examine the value of FOXP3 expression in locally advanced triple negative breast cancer tumoral cells as well as tumor infiltrating lymphocytes (TILs) and to elucidate its relation to the extent of NAC response. Material and Methods: Forty five cases of immunohistochemically confirmed to be triple negative breast carcinoma were evaluated for NAC (Doxorubicin, Cyclophosphamide AC x 4 cycles + Paclitaxel x 12 weeks, patients with ejection fraction less than 60% received Taxotere or Cyclophosphamide, Methotrexate, Fluorouracil CMF) response in both tumour and lymph nodes status according to Miller & Payne's and Sataloff's systems. FOXP3 expression in tumor as well as TILs evaluated in the pretherapy biopsies was correlated with NAC response in breast tumor and lymph nodes as well as other clinicopathological factors. Results: Breast tumour cells showed FOXP3 positive cytoplasmic expression in (42%) of cases. High FOXP3 expression percentage was detected in (47%) of cases. High infiltration by FOXP3+TILs was detected in (49%) of cases. Positive FOXP3 expression was associated with negative lymph node metastasis. High FOXP3 expression percentage and high infiltration by FOXP3+TILs were significantly associated with complete therapy response in axillary lymph nodes. High FOXP3 expression in tumour cells was associated with high infiltration by FOXP3+TILs. Conclusion: This result may provide evidence that FOXP3 marker is a good prognostic and predictive marker for triple negative breast cancer (TNBC) indicated for neoadjuvant chemotherapy and can be used for stratifications of TNBC cases indicated for NAC. As well, this study confirmed the fact that the tumour cells and the surrounding microenvironment interact with each other and the tumour microenvironment can influence the treatment outcomes of TNBC.

Keywords: breast cancer, FOXP3 expression, prediction of neoadjuvant chemotherapy effect, triple negative

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1072 Asymptomatic Intercostal Schwannoma in a Patient with COVID-19: The First of Its Kind

Authors: Gabriel Hunduma

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Asymptomatic intra-thoracic neurogenic tumours are rare. Tumours arising from the intercostal nerves of the chest wall are exceedingly rare. This paper reports an incidental discovery of a neurogenic intercostal tumour while being investigated for Coronavirus Disease 2019 (COVID-19). A 54-year-old female underwent a thoracotomy and resection for an intercostal tumour. Pre-operative images showed an intrathoracic mass, and the biopsy revealed a schwannoma. The most common presenting symptom recorded in literature is chest pain; however, our case remained asymptomatic despite the size of the mass and thoracic area it occupied. After an extensive search of the literature, COVID-19 was found to have an influence on the development of certain cells in breast cancer. Hence there is a possibility that COVID-19 played a role in progressing the development of the schwannoma cells.

Keywords: thoracic surgery, intercostal schwannoma, chest wall oncology, COVID-19

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1071 Tumour-Associated Tissue Eosinophilia as a Prognosticator in Oral Squamous Cell Carcinoma

Authors: Karen Boaz, C. R. Charan

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Background: The infiltration of tumour stroma by eosinophils, Tumor-Associated Tissue Eosinophilia (TATE), is known to modulate the progression of Oral Squamous Cell Carcinoma (OSCC). Eosinophils have direct tumoricidal activity by release of cytotoxic proteins and indirectly they enhance permeability into tumor cells enabling penetration of tumoricidal cytokines. Also, eosinophils may promote tumor angiogenesis by production of several angiogenic factors. Identification of eosinophils in the inflammatory stroma has been proven to be an important prognosticator in cancers of mouth, oesophagus, larynx, pharynx, breast, lung, and intestine. Therefore, the study aimed to correlate TATE with clinical and histopathological variables, and blood eosinophil count to assess the role of TATE as a prognosticator in Oral Squamous Cell Carcinoma (OSCC). Methods: Seventy two biopsy-proven cases of OSCC formed the study cohort. Blood eosinophil counts and TNM stage were obtained from the medical records. Tissue sections (5µm thick) were stained with Haematoxylin and Eosin. The eosinophils were quantified at invasive tumour front (ITF) in 10HPF (40x magnification) with an ocular grid. Bryne’s grading of ITF was also performed. A subset of thirty cases was also assessed for association of TATE with recurrence, involvement of lymph nodes and surgical margins. Results: 1) No statistically significant correlation was found between TATE and TNM stage, blood eosinophil counts and most parameters of Bryne’s grading system. 2) Statistically significant relation of intense degree of TATE was associated with the absence of distant metastasis, increased lympho-plasmacytic response and increased survival (diseasefree and overall) of OSCC patients. 3) In the subset of 30 cases, tissue eosinophil counts were higher in cases with lymph node involvement, decreased survival, without margin involvement and in cases that did not recur. Conclusion: While the role of eosinophils in mediating immune responses seems ambiguous as eosinophils support cell-mediated tumour immunity in early stages while inhibiting the same in advanced stages, TATE may be used as a surrogate marker for determination of prognosis in oral squamous cell carcinoma.

Keywords: tumour-associated tissue eosinophilia, oral squamous cell carcinoma, prognosticator, tumoral immunity

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1070 Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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1069 Effect of Post and Pre Induced Treatment with Hesperidin in N-Methyl N-Nitrosourea Induced Mammary Gland Cancer in Female Sprague-Dawley Rats

Authors: Vinay Kumar Theendra

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The main objective of the study is to evaluate the effectiveness of hesperidin in the treatment of breast cancer and causing less (or) no bone marrow depression which is the major side effect of the present anticancer drugs treating breast cancer, also to evaluate the mechanisms through which these compounds are exerting their effect. Breast cancer is induced by administering N-methyl N-Nitrosourea (MNU) at a dose of 50mg/kg body weight. Upon the termination of the experiment, the animals were sacrificed by the method of cervical dislocation. The animals were dissected along the ventral midline and were grossly examined for the presence of tumors. Then the tumours were removed along with the stroma. Vascular endothelial growth factor (VEGF) levels were estimated by using ELISA method. The first occurrence of palpable tumors was eight weeks after carcinogen treatment and the final tumour incidence was 100% in the MNU alone and topical treated rats. Whereas in rats of other treatment groups there is decreased tumour incidence which might be due to their antitumour activity. Hesperidin therapy inhibited angiogenesis which can be evident from the significant reduction in serum as well as tumour VEGF concentrations in comparison to the untreated mammary carcinoma bearing rats. Hesperidin is promising agents that exert direct antitumor and also antiangiogenic, antiproliferative and anti-inflammatory activities. Even though the potency is little lesser than standard drug vincristine, it has been proved to be safe without effecting haematological count.

Keywords: hesperidin, VEGF, COX 2, N-methyl N-nitrosourea

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1068 Using Autoencoder as Feature Extractor for Malware Detection

Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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1067 Integration of Magnetoresistance Sensor in Microfluidic Chip for Magnetic Particles Detection

Authors: Chao-Ming Su, Pei-Sheng Wu, Yu-Chi Kuo, Yin-Chou Huang, Tan-Yueh Chen, Jefunnie Matahum, Tzong-Rong Ger

Abstract:

Application of magnetic particles (MPs) has been applied in biomedical field for many years. There are lots of advantages through this mediator including high biocompatibility and multi-diversified bio-applications. However, current techniques for evaluating the quantity of the magnetic-labeled sample assays are rare. In this paper, a Wheatstone bridge giant magnetoresistance (GMR) sensor integrated with a homemade detecting system was fabricated and used to quantify the concentration of MPs. The homemade detecting system has shown high detecting sensitivity of 10 μg/μl of MPs with optimized parameter vertical magnetic field 100 G, horizontal magnetic field 2 G and flow rate 0.4 ml/min.

Keywords: magnetic particles, magnetoresistive sensors, microfluidics, biosensor

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1066 Machine Learning Methods for Network Intrusion Detection

Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

Abstract:

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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1065 Early Diagnosis and Treatment of Cancer Using Synthetic Cationic Peptide

Authors: D. J. Kalita

Abstract:

Cancer is one of the prime causes of early death worldwide. Mutation of the gene involve in DNA repair and damage, like BRCA2 (Breast cancer gene two) genes, can be detected efficiently by PCR-RFLP to early breast cancer diagnosis and adopt the suitable method of treatment. Host Defense Peptide can be used as blueprint for the design and synthesis of novel anticancer drugs to avoid the side effect of conventional chemotherapy and chemo resistance. The change at nucleotide position 392 of a -› c in the cancer sample of dog mammary tumour at BRCA2 (exon 7) gene lead the creation of a new restriction site for SsiI restriction enzyme. This SNP may be a marker for detection of canine mammary tumour. Support vector machine (SVM) algorithm was used to design and predict the anticancer peptide from the mature functional peptide. MTT assay of MCF-7 cell line after 48 hours of post treatment showed an increase in the number of rounded cells when compared with untreated control cells. The ability of the synthesized peptide to induce apoptosis in MCF-7 cells was further investigated by staining the cells with the fluorescent dye Hoechst stain solution, which allows the evaluation of the nuclear morphology. Numerous cells with dense, pyknotic nuclei (the brighter fluorescence) were observed in treated but not in control MCF-7 cells when viewed using an inverted phase-contrast microscope. Thus, PCR-RFLP is one of the attractive approach for early diagnosis, and synthetic cationic peptide can be used for the treatment of canine mammary tumour.

Keywords: cancer, cationic peptide, host defense peptides, Breast cancer genes

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1064 An Efficient Machine Learning Model to Detect Metastatic Cancer in Pathology Scans Using Principal Component Analysis Algorithm, Genetic Algorithm, and Classification Algorithms

Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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1063 A Study of Serum Beta 2-Microglobulin (β2M) and Lipid Bound Sialic Acid (LSA) Levels in Oral Carcinoma Patients

Authors: Kapoor Anurag, Sharma Pradeep, Mittal K Kailash, Kumar Ajai, Jawad Kalbe, Amit Kumar Singh

Abstract:

Background: Oral squamous cell carcinoma (OSCC) is the most prevalent malignant tumour on a global scale. Limited research has been conducted on tumour markers in oral cancer, and additional evaluation is required for several tumour producers that show clinical promise. The present study aimed to find out the co-relation of β-2 Microglobulin and Lipid Bound Sialic Acid in oral carcinoma patients. Methodology: The present case-control study was carried out on 35 patients with histopathologically confirmed OSCC and 35 age-matched controls. Serum concentrations of 2-Microglobulin and Total Sialic Acid (TSA) in the participants were determined via ELISA and spectrophotometric technique, respectively. Results: The OSCC group consisted of 20 males and 15 females, with an average age of 58 years, while the control group comprised 18 males and 17 females, with an average age of 55 years. Elevated levels of β2-microglobulin (3.87±0.12) and LSA (73.57±2.42) were observed in OSCC patients compared to controls (2.25±0.18; 65.21±2.06, respectively). Further examination based on smoking status revealed a significant increase in both β2-microglobulin and LSA levels among smokers compared to non-smokers (p < 0.05). Conclusion: The study suggests a notable association between higher levels of β2-microglobulin and LSA in oral squamous cell carcinoma (OSCC) patients who smoke compared to non-smokers. This observation leads to a hypothesis that this disparity could potentially serve as a significant contributing factor to the advancement of oral cancer.

Keywords: biochemistry human cancer, human, oral carcinoma, marker

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1062 Radiological Hazard Assessments and Control of Radionuclides Emitted from Building Materials in Kuwait Using Expert Systems

Authors: Abdulla Almulla, Wafaa Mahdi

Abstract:

Building materials can make a significant contribution to the level of natural radioactivity in closed dwelling areas. Therefore, developing an expert system for monitoring the activity concentrations (ACs) of naturally occurring radioactive materials (NORMs) existing in building materials is useful for limiting the population’s exposure to gamma radiation emitted from those materials. The present work not only is aimed at examining the indoor radon concentration emitted by the building materials that are originated from various countries but are commercially available in Kuwait, but also is aimed at developing an expert system for monitoring the radiation emitted from these materials and classifying it as normal (acceptable) or dangerous (unacceptable). This system makes it possible to always monitor any radiological risks to human health. When detecting high doses of radiation, the system gives warning messages.

Keywords: building materials, NORMs, HNBRA, radionuclides, activity concentrations, expert systems

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1061 Optimal Classifying and Extracting Fuzzy Relationship from Query Using Text Mining Techniques

Authors: Faisal Alshuwaier, Ali Areshey

Abstract:

Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

Procedia PDF Downloads 546
1060 Surveillance of Super-Extended Objects: Bimodal Approach

Authors: Andrey V. Timofeev, Dmitry Egorov

Abstract:

This paper describes an effective solution to the task of a remote monitoring of super-extended objects (oil and gas pipeline, railways, national frontier). The suggested solution is based on the principle of simultaneously monitoring of seismoacoustic and optical/infrared physical fields. The principle of simultaneous monitoring of those fields is not new but in contrast to the known solutions the suggested approach allows to control super-extended objects with very limited operational costs. So-called C-OTDR (Coherent Optical Time Domain Reflectometer) systems are used to monitor the seismoacoustic field. Far-CCTV systems are used to monitor the optical/infrared field. A simultaneous data processing provided by both systems allows effectively detecting and classifying target activities, which appear in the monitored objects vicinity. The results of practical usage had shown high effectiveness of the suggested approach.

Keywords: C-OTDR monitoring system, bimodal processing, LPboost, SVM

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1059 Sub-Pixel Level Classification Using Remote Sensing For Arecanut Crop

Authors: S. Athiralakshmi, B.E. Bhojaraja, U. Pruthviraj

Abstract:

In agriculture, remote sensing is applied for monitoring of plant development, evaluating of physiological processes and growth conditions. Especially valuable are the spatio-temporal aspects of the remotely sensed data in detecting crop state differences and stress situations. In this study, hyperion imagery is used for classifying arecanut crops based on their age so that these maps can be used in yield estimation of crops, irrigation purposes, applying fertilizers etc. Traditional hard classifiers assigns the mixed pixels to the dominant classes. The proposed method uses a sub pixel level classifier called linear spectral unmixing available in ENVI software. It provides the relative abundance of surface materials and the context within a pixel that may be a potential solution to effectively identifying the land-cover distribution. Validation is done referring to field spectra collected using spectroradiometer and the ground control points obtained from GPS.

Keywords: FLAASH, Hyperspectral remote sensing, Linear Spectral Unmixing, Spectral Angle Mapper Classifier.

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1058 A Machine Learning Approach to Detecting Evasive PDF Malware

Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

Abstract:

The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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