Regression modelling of cervical cancer and Chlamydia incidence in the context of national screening programmes

Prevention of cervical cancer development or reduction in undetected Chlamydia incidence and further onward Chlamydia transmission can be achieved through regular screening. Early detection through a regular screening programme is essential to achieve this goal. A well established screening policy is needed to improve screening efficiency.This PhD study demonstrated the use of mathematical and spatial modelling to explore the risk factors through various regression models, to explore the relation between socio-economic conditions and disease incidence, and also other techniques including classification analysis, decision models, and simulation to evaluate screening options. Based on the risk factors and risk grouping, different groups may have different screening policies. Alternatively, geographical differences can be taken into account by dividing areas into a few parts; the population living in each part may be considered to have different risks of developing cervical cancer or Chlamydia in their life time. Therefore, different screening programmes and services could be provided to those populations according their location or the risk groups which they belong to

Positron emission tomography/computerised tomography imaging in detecting and managing recurrent cervical cancer: systematic review of evidence, elicitation of subjective probabilities and economic modelling.

© Queen’s Printer and Controller of HMSO 2013. This work was produced by Meads et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising.Cancer of the uterine cervix is a common cause of mortality in women. After initial treatment women may be symptom free, but the cancer may recur within a few years. It is uncertain whether it is more clinically effective to survey asymptomatic women for signs of recurrence or to await symptoms or signs before using imaging.National Institute for Health Research Health Technology Assessment programm

Ultrasound-based assessment and management of postmenopausal bleeding and endometrial polyps

This thesis has evaluated aspects of ultrasound-based assessment and management of women with postmenopausal bleeding and endometrial polyps. The efficacy of transrectal ultrasound scan (TRS) was assessed in 103 consecutive postmenopausal women with an axial uterus. TRS was accepted by two-thirds of the women and the proportion of satisfactory endometrial assessments was significantly higher on TRS compared to transvaginal scan (TVS), 91% (95% CI 84-98) vs 62% (95% CI 50-74), respectively. In the subgroup of 50 women with postmenopausal bleeding and an axial uterus, the endometrial thickness measured significantly thinner on TRS by a median of 1.2mm (IQR 0.4-3) compared to TVS. Furthermore, subjective pattern recognition for endometrial cancer was less accurate on TVS compared to TRS when the uterus is in an axial position. The interrater reliability of ultrasound subjective pattern recognition for endometrial cancer was prospectively assessed in 40 women with postmenopausal bleeding and a thickened endometrium (≥4.5mm); a good level of agreement (κ = 0.78, 95% CI 0.61-0.95) was found between an expert and an average operator. The diagnostic accuracy of ultrasound subjective pattern recognition for endometrial cancer was assessed in 240 consecutive women with postmenopausal bleeding and a thickened endometrium (≥4.5mm) and available histology. It performed well with a sensitivity and specificity of 88% (95% CI 77-95) and 97% (95% CI 94-99), respectively. The presence of focal malignancy within endometrial polyps was the most common cause of a false-negative diagnosis of endometrial cancer. Endometrial cancer was diagnosed on ultrasound by subjective pattern recognition and simultaneously assessed for the presence of deep myometrial invasion and cervical stromal invasion in 51 women. We found that the accuracy of ultrasound in the preoperative staging of endometrial cancer was comparable to MRI (sensitivity and specificity, 86% vs 77% and 66% vs 76%, respectively). A clinical model was presented to estimate the risk (low, intermediate, or high) of pre-malignancy or malignancy in postmenopausal endometrial polyps. The model included polyp size, the presence or absence of intralesional cystic spaces and the patient’s BMI as clinical variables. Accordingly, approximately one-third of postmenopausal polyps would be categorised as high- or intermediate-risk and they would account for over 90% of all premalignant/malignant polyps, while the remaining polyps would be categorised as low-risk with a 1/18 risk of pre-malignancy or malignancy. The overall accuracy of the model in predicting premalignant or malignant postmenopausal polyps was 92% (95% CI 86.0-97.4). The natural history of expectantly managed endometrial polyps was assessed retrospectively in 112 polyps over a median follow-up of 22.5 months (range 6-136). We found that polyps’ growth rates varied, and it was not possible to predict an individual polyp’s growth based on the patient’s clinical characteristics or polyp’s morphological features. Polyp’s growth rate was not associated with the risk of developing abnormal uterine bleeding (AUB). Some polyps underwent spontaneous regression (7/112, 6%) and this occurred more frequently among premenopausal women and those who were symptomatic of AUB

Breast Cancer Forecasting Using Machine Learning Algorithms

Some of the most prevalent and significant causes for malignancies in women is breast cancer. It is presently a widespread health problem, and it has recently become more frequent. The greatest method for managing breast cancer symptoms is early identification. The only kind of cancer that primarily affects women globally is breast cancer, which has the potential to be a major cause of mortality. Early detection of breast cancer is crucial in order to properly treat it and save many lives. This paper covers the results and analyses of several machine learning algorithms for identifying breast cancer. Several machine learning models used the information once it was analyzed. In this paper the Random forests and SVCalgorithms were applied and compare the performance of these algorithms. The dataset was taken from UCI repository. Analyze and compare the classifiers' performance in terms of accuracy, precision, and f1-Score in addition. For implementing the ML algorithms, the dataset was split among training and testing phases. The notebook application Jupyter was used to implement these models. When compared to the other two models, it was successfully proven that the SVC model offers the best results. SVC's accuracy of 93% is greater than the method described earlier in that regard. The method used by this model, which will classify cancer into benign and malignant categories, yields the best results

The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients

Evaluation of aspects of screening for oral cancer

Evidence suggests that early detection of oral cancer or precancer decreases both mortality and morbidity. Screening for oral cancer may be an effective health care intervention in view of the annual increase in new registrations of oral cancer and rising mortality rates. Oral cancer meets many of the criteria for a disease suitable for screening, however a need exists for research into the design of a screening programme and the validity of a screening test. Visual examination of the oral mucosa would appear to be a valid instrument for detecting oral cancer and precancer; in this study a selection of qualified dentists achieved this with a sensitivity and specificity of 0.74 and 0.99 respectively. Compliance from an invitational screening programme was disappointingly low (25.7%) compared to other similar programmes suggesting that targeting of high risk individuals may be more effective in detecting lesions. Data from the screened population was modified and used to train a computerised neural network, this was shown to be a useful tool for the identification of people at high risk from oral cancer and could detect lesions with sensitivity (0.80) and specificity (0.77), values comparable to dentists. Health care interventions such as screening programmes are assessed in terms of costs and benefits to the patient and public. Quality of life was compared in terms of utility values between patients treated for small oral cancers and those treated for more major cancers. Utility values for various stages of oral cancer were also obtained from a sample of the public since it is argued that they should play a part in health care decision making. Finally, the potential value of screening was determined using a decision model based on the results obtained from this study

Proceedings From the First Asia-Oceania Research Organisation on Genital Infections and Neoplasia (AOGIN) Meeting

The First Asia-Oceania Research Organisation on Genital Infections and Neoplasia (AOGIN) Meeting was held in Kota Kinabalu, Malaysia, in July 2005. The conference covered regional issues relating to infection with the human papillomavirus—epidemiology, virology, and immunology, testing, screening, and prevention strategies—as well as cervical cancer screening and its management

Cost-effectiveness of early detection of breast cancer in Catalonia (Spain)

<p>Abstract</p> <p>Background</p> <p>Breast cancer (BC) causes more deaths than any other cancer among women in Catalonia. Early detection has contributed to the observed decline in BC mortality. However, there is debate on the optimal screening strategy. We performed an economic evaluation of 20 screening strategies taking into account the cost over time of screening and subsequent medical costs, including diagnostic confirmation, initial treatment, follow-up and advanced care.</p> <p>Methods</p> <p>We used a probabilistic model to estimate the effect and costs over time of each scenario. The effect was measured as years of life (YL), quality-adjusted life years (QALY), and lives extended (LE). Costs of screening and treatment were obtained from the Early Detection Program and hospital databases of the IMAS-Hospital del Mar in Barcelona. The incremental cost-effectiveness ratio (ICER) was used to compare the relative costs and outcomes of different scenarios.</p> <p>Results</p> <p>Strategies that start at ages 40 or 45 and end at 69 predominate when the effect is measured as YL or QALYs. Biennial strategies 50-69, 45-69 or annual 45-69, 40-69 and 40-74 were selected as cost-effective for both effect measures (YL or QALYs). The ICER increases considerably when moving from biennial to annual scenarios. Moving from no screening to biennial 50-69 years represented an ICER of 4,469€ per QALY.</p> <p>Conclusions</p> <p>A reduced number of screening strategies have been selected for consideration by researchers, decision makers and policy planners. Mathematical models are useful to assess the impact and costs of BC screening in a specific geographical area.</p

2020 - The First Annual Fall Symposium of Student Scholars

The full program book from the Fall 2020 Symposium of Student Scholars, held on December 3, 2020. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1022/thumbnail.jp