Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Occupational respiratory diseases

Shipping list no.: 87-222-P."September 1986."S/N 017-033-00425-1 Item 499-F-2Also available via the World Wide Web.Includes bibliographies and index

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Development of novel nanomedicines for the treatment of non-small cell lung cancer

Lung cancer stands as one of the deadliest diseases, responsible for the most cancer related deaths worldwide. The UK 5-year survival rate of non-small-cell lung cancer (NSCLC), the predominant subtype of lung cancer, stands at 9.5%, highlighting an unmet need for therapeutic intervention. A key issue is the lack of efficacy current chemotherapy regimens have in the clinic. These therapies often suffer from poor tumour targeting, resulting in dissemination throughout the body and inadequate concentrations in the tumour. This causes deleterious side effects contributing to a reduced patient quality of life and ultimately survival. Nanomedicine may serve to overcome the current therapeutic hurdles in treating NSCLC; the use of nanoparticles (NPs) for the delivery of drugs can improve drug targeting to tumours, increasing efficacy and attenuating off-target side effects. NPs can be used to deliver multiple drugs and be made from varying materials such as gold (AuNPs) or polymers. Furthermore, the discovery of oncogenic mutations in genes like EGFR present druggable targets in patients harbouring the appropriate mutations. This can also be taken advantage of using NPs to more directly target tumours and increase therapeutic response. Therefore, the aim of this thesis was to develop novel NP formulations comprised of a chemically modified variant of the tyrosine kinase inhibitor afatinib and gold (Afb-AuNPs) or in combination with vinorelbine as a polymeric dual chemotherapy formulation (Dual-NPs). Drug-bearing NPs were synthesised using a combination of organic chemistry and hydrophobic ion pairing, after which the NPs were extensively characterised to discern their physicochemical properties. We then sought to investigate the in vitro efficacy of NPs. Cell viability studies revealed Afb-AuNPs and Dual-NPs were significantly cytotoxic to various NSCLC cell lines and comparatively nontoxic to noncancerous cells. Moreover, NP formulations were found to significantly inhibit proliferation of A549, H226 and PC-9 cells 3 compared to clinical formulations as determined by electric cell-substrate impedance sensing. The mechanism of uptake in cancer cells was elucidated using fluorescent NPs as a model system and quantified using confocal microscopy. Finally, the in vivo activity of biocompatibility of Dual-NPs was investigated in a physiologically relevant murine model of NSCLC. Taken together, these results highlight the therapeutic potential for NP formulations of chemotherapy.Open Acces

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

Pathological Anatomy. Lecture course

УЧЕБНО-МЕТОДИЧЕСКИЕ ПОСОБИЯАНАТОМИЯПАТОЛОГИЧЕСКАЯ АНАТОМИЯPATHOLOGICAL ANATOMYLECTURE COURSEЧАСТНАЯ ПАТОЛОГИЯПАТОЛОГИЯВ пособии представлены наиболее важные темы, охватывающие полный курс патологической анатомии