M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/60
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Advancing Breast Cancer Detection: Embedding Nanoparticles into Breast Phantoms for Diagnostic Applications(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) MISBAH ASHIQ; CUI/SP24-RPH-012/LHR; Dr. Naima Amin; LHR TP 10124In this study, a single-layer homogeneous breast tumor phantom was developed to simulate the diagnostic properties of malignant breast tissue, with the aim of enhancing breast cancer detection and diagnostic accuracy. The tumor-mimicking phantom was fabricated using an optimized gelatin-based hydrogel, incorporating additives such as NaCl, PVA, glycerin, agar, ethanol, and PEG to reproduce the physical consistency and imaging characteristics of tumor tissue. Silver oxide (Ag₂O) and iron oxide (Fe₃O₄) nanoparticles were synthesized using controlled chemical methods and characterized using FTIR, XRD, and SEM to confirm their chemical composition, crystalline structure, and surface morphology, ensuring their suitability for diagnostic imaging applications. The nanoparticles were uniformly incorporated into the tumor phantom through a premixing approach, resulting in a homogeneous distribution that simulates nanoparticle-enhanced tumor tissue. The fabricated tumor phantoms were evaluated using computed tomography (CT) to assess their effectiveness in breast cancer detection. CT imaging demonstrated increased Hounsfield Unit values due to the high atomic number of silvers in Ag₂O nanoparticles, leading to enhanced X-ray attenuation, while MRI scans revealed clear signal suppression and contrast variation in Fe₃O₄- loaded phantoms owing to their superparamagnetic properties. The results confirm that the developed single-layer homogeneous tumor phantom provides a reliable and reproducible model for evaluating nanoparticle-assisted breast cancer detection and precise diagnosis, offering a simplified yet effective platform for imaging system calibration, contrast agent assessment, and preclinical diagnostic studies.Item Computerized Medical Image Analysis for Optimization of Cancer(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Zulqarnain Nazir; CIIT/FA23-RPH-041/LHR; Dr. Naima Amin; LHR TP 10116The medical imaging has a significant role in the diagnosis and management of cancer, but the images produced by medical imaging modalities like MRI and CT usually contain noise, low contrast, and visual artifacts. These artifacts may conceal valuable tumor data and complicate the correct interpretation of clinicians. This thesis is aimed at enhancing medical image quality by means of computerized analysis of images so as to aid in enhancing the evaluation of cancers. Images of different patients acquired by MRI and CT were utilized in standard DICOM format in this study. Ten image enhancement procedures have been used in this study including sharpening, noise reduction, edge retention, contrast enhancement and visualization in three dimensions. The tools that were utilized to complete the analysis were RadiAnt DICOM Viewer and 3D Slicer, which provided the visualization options of the images, multiplanar reconstruction, tumor segmentation, and quantitative analysis. The results shows that image clarity and contrast and noise reduction are greatly enhanced by the use of appropriate enhancement methods. These enhancements contribute to the better visualization of tumor borders and the adjacent organs, resulting in more accurate segmentation and analysis. The improved quality of images also means that repeat scans can be reduced hence the cost and patient exposure can be minimized. On the whole, this study indicates that computerized medical image analysis may be used as a quality tool in streamlining the cancer imaging process and assisting in better diagnosis and treatment planning.Item Dosimetry Audit for Precise Radiotherapy Treatment Planning by Using High-Energy Electron Beam (6-22 MeV)(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Sufia Kiran; CIIT/SP22-RPH-030/LHR; Dr. Naima Amin; LHR TP 10000It has been long known that patients treated with ionizing radiation carry a risk of developing a second cancer in their lifetimes. Factors that contribute to the restate of cancer (or may be called second cancer) includes improved cancer survival rate by using high radiations as well as emerging treatment modalities. These modalities such as electron beam therapy or external beam radiation therapy. They can potentially elevate secondary exposures to healthy tissues distant from the target volume. The standard Linear-Quadratic (LQ) survival model for electron beam radiotherapy is reviewed with particular emphasis on studying how different schedules of radiation treatment planning may be affected by different tumour repopulation kinetics. The LQ model is further examined in the context of tumour control probability (TCP) models. A high energy electron (6-22) MeV through linear accelerator is used to strike the target in cancer treatment. In general, radiotherapy has different energy of electron used for cancer treatment which depends on the various factors such as general patient health and history and the stage of the cancer. The crucial problem in the radiotherapy in cancer treatment is that electron beam did not know the difference between healthy and cancerous cell. External beam radiotherapy is used to deliver the radiations to the organ. In this study, beast organ selected as a target volume. The breast organs include surrounding tissues/OARs such as brain, lungs, liver, breast, thyroid and colon. Thus, the purpose of this study is to analyze and calculate the effect of high energy electrons on these surrounding tissues of breast. Thus mathematical & biological model, (LQM) is used as a tool to calculate the Biological Effective Dose (BED) with dose per fraction 1.8 for nearer to the pelvic. Moreover, the aim of this study is to provide an understanding of the principles and methods related to scattered doses (electron beam) in surrounding organs to check and optimize the dose plan in radiation therapy by summarizing a large collection of dosimetry and clinical studiesItem MIP based Electrochemical Identification of Rutin using Lead Pencil as An Electrode Source.(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Zahra Munir; CIIT/SP22-RPH-013/LHR; Dr. Naima Amin; LHR TP 9999In response to the increasing demand for rapid and user-friendly procedures in research laboratories and hospitals, this study introduces a novel and direct electropolymerization method for monitoring Rutin in biological samples and pharmaceutical formulations. A molecularly imprinted pencil graphite electrode was employed, and electropolymerized in situ to create a Molecularly Imprinted Polymer (MIP) network. The electropolymerization process involved Rutin as the template, pyrrole as the monomer, and SnS2/rGO. Experimental parameters were fine-tuned using Differential Pulse Voltammetry (DPV) to optimize MIP efficacy. DPV investigations demonstrated a proportional increase in the peak oxidation signal with decreasing Rutin concentrations, showcasing the sensitivity of the developed method. Rutin concentrations ranging from 0.05nM to 100 µM could be accurately measured, boasting an impressive low detection limit of 0.01 nM. The proposed sensor exhibited exceptional detection capabilities in biological samples spiked with Rutin, highlighting acceptable recovery rates. This innovative electrochemical approach, combining molecular imprinting with electropolymerization, not only offers a sensitive and selective method for Rutin detection but also presents a versatile platform for monitoring other bioactive compounds. The simplicity, efficiency, and low detection limit make it a promising tool for routine analysis in diverse healthcare and pharmaceutical applications.Item Radiation Analysis for Cancer Treatment Indicates Biological Alterations in the Surrounding Tissues(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) AMINA SAEED; CUI/SP23-RPH-017/LHR; Dr. Naima Amin; LHR TP 9637It has been long known that patients treated with ionizing radiation carry a risk of developing a second cancer in their lifetimes. A high-energy electron beam 6 MeV through linear accelerator is used to strike the target in cancer treatment. External beam radiotherapy is used to deliver the radiations to the organ. In this study, breast organ selected as a target volume. The breast organs include surrounding tissues/OARs such as brain, lungs, liver, breast, heart. Thus, the purpose of this study is to analyze and calculate the effect of high-energy electrons on these surrounding tissues of breast. This damage is very important to analyze to the functionality of organs. Surrounding tissues affected by the direct and indirect radiation. Both radiation produce chemical changes in DNA. In this study, we investigate the chemical changes at DNA level to analyze the functionality of organ and the effect of radiation in surrounding tissues. More precise dosimetry can be develop by analysis the effect of radiation in target and surrounding tissues for further treatment.Item Fabrication of Breast Cancer Phantom for the Estimation of Radiation Dose in Breast Cancer Radiotherapy(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Sahar Khalid; CIIT/FA20-RPH-008/LHR; Dr. Naima Amin; LHR TP 7975A Gel-based breast tumor phantom was fabricated to estimate radiation dose in breast cancer radiotherapy. The materials used have similar properties to real tumor tissues. Vegetable oil was used as a source of fats and lipids in the Phantom, while Gelatinwas used as a source of protein to make equivalent breast tumor tissues. NaCl was added to increase the Phantom's conductivity. Different characterization techniques were used to analyze the equivalency of tumor phantom with real breast tumor tissues. The bonding characteristic in FTIR spectra shows different organic compositions in the Phantom. X- ray diffraction was used for structural properties to find crystalline structure in the Phantom. It shows the highest crystalline structure peak at [200] planes due to the presence of NaCl. The swelling test helped to swell the tumor phantom with the right water content equivalent to real tumor tissues. The swelling ratiowas increased until 35 minutes later, and the sample started to degrade. Degradation was used to check the life span of a phantom. 40 Gy of Radiation dose was given to the tumor phantom, which led to the breakage of ester bonds in the Phantom, similar to the breakage of phospholipids in the cell membrane of breast tumor cells after the radiation was given.Item Pattern Recognition and Deep Learning Models for Cancer Detection(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Mishal Tahir; CIIT\FA21-RPH-020/LHR; Dr. Naima Amin; LHR TP 8591Cancer, a leading cause of death worldwide, poses challenges due to late-stage detection and inaccurate imaging techniques. Precise and effective screening is crucial for early detection and treatment. Various low-cost and accurate imaging techniques are used, but difficulties arise when experts struggle to interpret certain image areas, leading to missed cancer diagnoses. To address this, computer-based software utilizing deep learning models and algorithms has been developed. Traditional approaches have evolved into computerized tools that analyze, diagnose, and predict symptoms. This study conducted a comparative analysis of three detection models of different frameworks for binary and multi-class image classification using image processing techniques. Despite their distinct inputs, model network architecture Convolutional Neural Networks (CNN). The data were split into sets: training, validation, and testing, and the evaluation involved learning curves of training and validation loss and accuracy as well as a comparison of training, validation, and test accuracies to check the model performances. The results showed the accuracy of all models of predicting unknown images of cancerous or non-cancerous. The Difference in input data and model learning rate could be the reason for variation in test results. Computer language, python, and the platform, Pycharm IDE have been used for tasks performed.Item Fabrication Of Body Equivalent Tissue Phantom For The Evaluation Of Breast Cancer By Radiation Therapy(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) ABDUL MUGHNI; CUI/FA18-RPH-048/LHR; LHR TP 7245; Dr. Naima AminPreparing a phantom is a highly specialized testing object utilized in medical physics for dosimetry and evaluation of the effect of the dose distribution on cancer cells. The phantom is the best cancer investigational tool for the Medical Physicist. We will be preparing these phantoms to use as our training model. The phantom will be prepared like a real part of the human body tissues. The body or particular organ tissue-equivalent phantom was designed to minimize uncertainties in dosage measurements and treatment plan and also find the track followed by the high energy photon beam. After the preparation of our phantom, we investigate the center and surface doses by the radio-chromic film, it will be helpful in future research. The gel phantom will be irritated with the help of a photon beam and an electron beam of different energies. In this research, we will explore the diffusion of radiation in the irradiated part of the phantom and its effect on normal healthy cells of the phantom. These radiations aim to destroy a cancer cell's DNA. These radiation treatments usually include risks for adjacent healthy tissues within a f ew millimeters. Estimating ionizing radiation is important before cancer therapy. Breast cancer is reinduced by inappropriate radiation usage. Estimation of the radiation dosage may be made more precise for future treatment of any malignancy when the patient is repeatedly irradiated. The main difficulty in radiation treatment is providing a specified dosage to normal tissues. A severe lesion in healthy tissues may impede genome replication and transcription an d, if not corrected, lead to mutations that endanger the survival of cells or organs. Another aspect of this project is to revise the estimation and study of the dose calculation for further cancer treatment to keep safe the human body cellsItem Deep Learning Model for Identifying Organ at Risk Toxicity in Thorax Carcinomas Caused by Radiation Therapy(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Mutayyab Ilyas; SP19-RPH-012; Dr. Naima Amin; LHR TP 7253To assist doctors in making individualized treatment decisions, a machine learning algorithm that can accurately predict toxicity could be used. Patients undergoing thoracic radiation treatment often have organ at risk (OAR) toxicity, which may have a major effect on their health. The use of radiation therapy to treat thorax cancer has been shown to be beneficial. Toxins associated with treatment, on the other hand, are a different story. Treatment-related toxicity is a significant issue because cancer spreads to healthy cells and organs in close proximity. The use of radionics in therapy control strategies has been studied in a number of research features extracted from computed tomography (CT), magnetic resonance imaging (MRI), or dosimetric features collected by 3D dose distributions (DD). Convolutional Neural Networks (CNN) are being used to investigate the toxicity of organs in danger. Image processing, treatment planning, treatment delivery, and post-therapy follow-up are all feasible with a deep learning model. As well as by optimising Using convolution filters during the training stage, a deep learning-based prediction model may discover and fine-tune variables for particular categorization issues. The potential of a deep learning-based estimate technique to reduce the radiation treatment hazard rate is a significant benefit. Furthermore, using convolution methods, the attributes may be obtained automatically (CNN). The objective of this study is to create and test a 3D CNN-based toxicity prediction model. CT images, radiation treatment DD, and contours will be used to extract low and large temporal spatial features utilising (3D) filters, especially in low and large temporal spatial characteristics. The comparative test will demonstrate that the planned model is capable of forecasting OAR toxicity properly. Research into more specific criteria for places that are closely connected to OAR Toxicity might improve the model even further.Item Significance of Noise Reduction in Magnetic Resonance Imaging for Precise Diagnosis of Normal and Abnormal Tissues(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Muhammad Awais Sardar; FA18-RPH-037; Dr. Naima Amin; LHR TP 6361Over the past few decades with the invention and use of different medical imaging techniques like CT, MRI, Digital X-Ray and Angiography, its importance has already been proven by the ability of these techniques to diagnose and treat diseases. Medical images are usually low in quality, especially when it comes to image contrast