Saudi Cultural Missions Theses & Dissertations
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Item Restricted The use and evaluation of integrated diagnostics in haematological malignancy(Manchester, 2020-09-30) Altowairqi, Ahlam; Byers, RichardBackground: Haematologic malignancies are one of the most challenging diagnostic fields. Many discrepancies have been reported in diagnoses of this disease over the last few years that affect patient outcomes. Such discrepancies have promoted the introduction of guidelines and classifications to improve identification and predict an optimal therapy approach. The Specialist Integrated Haematological Malignancy Diagnostic Services (SIHMDS), which was introduced in the UK as a special service for haematologic malignancies, to provide a second review, promote multiple techniques for proper diagnosis and prognosis and to choose the best treatment approach. Objectives: This study aims to identify the effectiveness of diagnosing lymphoma using SIHMDS in Manchester by comparing this service to local in MRI and other hospitals diagnoses. Also, it aims to identify the role of interpretation and diagnostic techniques in accurate lymphoma diagnosis. Methods: A total of 2,014 cases were collected from January 2019 to December 2019 from HODS system in SIHMDS. Among these, 295 cases were lymphoma cases and their details were taken from an integrated report. The primary diagnoses of these cases were taken to compare and identify discrepancies. Results: From 295 cases, the discrepancy was 10% in the total cases diagnosed, while the discrepancy rate was lower in MRI (7.4%), and higher in other hospitals by (29%). The role and need of the second review and multiple techniques were confirmed to improve the diagnoses in the special service. Also, needle core biopsy was shown to be useful in lymphoma diagnosis. Conclusion: Introducing special diagnostic service in haematologic malignancies is crucial due to the great benefits of second review and multiple advance techniques. New advances in this field will further improve the diagnosis sensitivity and further enhance patient outcomes.16 0Item Restricted Design and Development of Lateral Flow Test for Early Screening of Pancreatic Cancer(University College London (UCL), 2022) Sharief, Rimaz; Patra, Hirak; Acedo, Pilar; Chivu, Alex; Sampedro, AndresPancreatic cancer currently showing one of the lowest survival rates in the UK (~5%). Despite the huge efforts in improving the diagnosis using existing screening methods, an early stage detection remains a challenge. Developing a rapid, point-of-care lateral flow test (LFT) for patients is an unmet clinical goal that can accelerate the screening in clinical settings. The focus of my project is to design & develop a LFT based on carbohydrate/cancer antigen (CA 19-9) levels in the blood. My aim is to investigate and increase the sensitivity of the LFT by indicating high-low levels of CA 19-9 for regular screening and monitoring. This project involved 4 distinct stages: Optimizing the appropriate golden nanoparticles (Au NP) for their functionalization with different concentrations of PEG and MSA to get the best immunoassay. Different characterization methods including UV-Vis and DLS will be conducted to MSA and PEG Au NPs to observe the stability of Au NPs at different concentrations. Consequently, freeze-drying procedure will be conducted to ensure that PEG and MSA AuNPs sensitivity and reactivity is retained when in dried state and rehydration/rewetting. All MSA AuNP aggregated after resuspension. While 50 uM, and 100 uM PEG retained stability after resuspension. 50 uM PEG was selected for the functionalization of 22 nm AuNP (AuNP1) for further Anti-CA 19-9 monoclonal antibody covalent conjugation. Characterisation methods including UV-VIS, DLS, gel electrophoresis (agarose and ) indicated successful EDC/NHS conjugation. To assess the binding efficiency of Ab-AuNP on membrane strip; Supernatant was collected from cholangiocarcinoma cell lines and pancreatic cell lines at 80% confluency. The expression of CA 19-9 was evident in PDX-185 only. No test line were evident; during suspension of SFM PD-185 supernatant and Ab-AuNP1 on immobalised mAb. Hence, the Fabrication of the LFT Lastly was not achieved as Ab concentration optmisation during conjugation process is required to achieve the optimum sensitivity and the limit of detection for the LFT.17 0Item Restricted Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review(University of Manchester, 2024) Almohawis, Alhaitham; Yong, SinAbstract Oral and maxillofacial cysts are cavities that can pose significant risks if not detected and treated promptly. Many of these cysts are asymptomatic, often going unnoticed until complications arise. The introduction of artificial intelligence (AI) presents a promising opportunity for early detection and management of these cysts. Aim: To explore current studies on the use of artificial intelligence in diagnosing oral and maxillofacial cysts. Objectives: To examine the existing literature in this field, assess the accuracy, effectiveness, and limitations of AI models, and identify challenges in implementing AI in clinical practice. Methods: This literature review followed a systematic approach, identifying 223 studies from PUBMED and SCOPUS databases between 1975 and 2024. After applying inclusion and exclusion criteria, 26 retrospective cohort studies were included in the final analysis. A risk of bias assessment was conducted using the ROBINS I tool. Results: The investigation revealed that AI models consistently demonstrate high accuracy in detecting oral cysts in both radiographs and digital histopathology. The ROBINS I tool indicated a moderate risk of bias in most of the included studies. Notable limitations include limited datasets, variable data quality, and a lack of explainability in AI models results. Conclusion: AI models have shown considerable effectiveness and speed in detecting both simple and complex cysts. However, to fully leverage AI's potential in clinical settings, further rigorous studies are needed to evaluate its risks, benefits, and feasibility, ensuring compliance with governmental health regulations on AI.13 0Item Restricted Comprehensive Patient-Specific Prediction Models for Diagnosis and Prognosis of Temporoman-dibular Joint Osteoarthritis(Saudi Digital Library, 2023) Alturkestani, Najla; Cevidanes, LuciaOsteoarthritis is the most common degenerative joint disease, affecting 15% of the global popula-tion. Osteoarthritis in temporomandibular joint (TMJ OA) can cause chronic pain, facial deformi-ty, joint dysfunction, impacting the quality of life. Unlike weight-bearing joints, TMJ OA primar-ily affects individuals between the ages of 20 and 40 and can also appear in adolescents. Current standards for diagnosing TMJ OA rely on clinical and imaging criteria. However, these criteria have limited efficacy in detecting early-stage TMJ OA, posing challenges to timely inter-vention and mitigation of irreversible tissue damage. Hence, it becomes imperative to identify additional objective diagnostic criteria. In addition, determining which patients are at increased risk of disease progression is critical for making informed clinical decisions and designing more effective and individualized treatments. Radiomics is a newly established field propelled by advancements in computational power. It extracts quantitative imaging features from radiological images, aiming to identify subtle tissue variations and reduce subjectivity in image interpretation. Beyond radiomics, metabolic abnor-malities in joint tissues serve as early indicators of osteoarthritis. Although there has been pro-gress in studying osteoarthritis biomarkers, they have not yet been clinically established. Evaluat-ing multiple markers may reveal their intricate interrelations and fully harness their potential. With the advent of powerful machine learning (ML) methods, analysis of complex multisource data became feasible. Nevertheless, applying feature selection methods is crucial to eliminate re-dundant and irrelevant data, improving the output accuracy. Unlike knee osteoarthritis, which has been extensively studied using ML models, TMJ OA remains an underexplored area. Therefore, we aimed to 1) Develop a reliable prediction tool for TMJ OA progression and identify the con-tributing factors during a 2–3-year follow-up period, 2) Develop a comprehensive prediction tool tailored for TMJ OA diagnosis and use explainable methods to identify key factors driving diag-nosis, and 3) Investigate the feasibility of privileged learning in addressing missing data when diagnosing TMJ OA. We successfully developed an open-source tool which combined 18 feature selection and ML methods. This allowed for the prediction of disease progression with an accuracy=0.87, area un-der the ROC curve (AUC)=0.72, and an F1 score=0.82. Using the interpretable SHAP analysis method, we identified the strongest predictors for TMJ OA progression. These included: clinical (headache, lower back pain, restless sleep), quantitative imaging (condyle high-grey-level-run-emphasis (HGLRE), articular fossa GL-non-uniformity, and long-run-low-GLRE, joint space), and biological markers in saliva (Osteoprotegerin, Angiogenin, VEGF, and MMP-7) and serum samples (ENA-78). Utilizing clinical, CBCT imaging, and biological data from 162 prospectively recruited subjects, we evaluated 77 ML methods. Random forest demonstrated the best diagnostic performance, achieving AUC=0.90, accuracy=0.79, precision=0.80, and F1=0.80. The integration of clinical, imaging, and biological markers enhanced TMJ OA diagnosis. The top contributing features were clinical (headache, restless sleep, mouth opening, muscle soreness), objective quantitative imag-ing (condyle Cluster-Prominence, HGLRE, SRHGLRE, Trabecular Thickness), and biological markers in saliva (TGFB-1, TRANCE, TIMP-1, PAI-1, VECadherin, CXCL-16) and serum (An-giogenin, PAI-1, VEGF, TRANCE, TIMP-1, BDNF, VECadherin). Lastly, we developed the KRVFL+ diagnostic tool, which can be used when only clinical and imaging data are available. It achieved an AUC, specificity, and precision of 0.81, 0.79, and 0.77, respectively. Collectively, these efforts emphasize the immense potential of multi-source data and ML applica-tions in presenting solutions for predicting TMJ OA progression and diagnosis, with potential implications for timely interventions and a transformative impact on TMJ OA healthcare deliv-ery.33 0