The use of novel tumour markers and statistical models in the preoperative diagnosis of ovarian cancer

Malignant ovarian tumours are diagnosed at an advanced stage in 75% of cases and they have the highest mortality figures of all gynaecological cancers. As the treatment of benign and malignant adnexal masses is significantly different it is important to be able to reliably distinguish between them preoperatively. Thus, women with malignancies could be referred to cancer centres, whilst those with benign conditions could be offered more conservative management.The aims of this thesis are (1) To investigate the use of new tumour markers in the preoperative diagnosis of ovarian cancer. (2) To validate previously published models and compare their performance to subjective assessment and to the models developed in this thesis. (3) To investigate the differences between small asymptomatic masses and large masses and to investigate the accuracy of published models on the diagnosis of malignancy in small masses.CA 125, CA 15-3 and CA 72-4 were significantly raised in the presence of ovarian cancer. CA 72-4 was higher in mucinous cancers and CA 125 and CA 15-3 were higher in serous and endometrioid cancers. Her-2/neu and CA 19-9 were not significantly different in benign or malignant disease. Logistic regression analysis showed age, CA125 and CA 15-3 to be the most valuable discriminators. A neural network was designed and trained which gave a sensitivity of 100% and a specificity of 90.9% on the test set. None of the six published models tested prospectively performed as well as in their original publication. The IOTA logistic regression model performed best and gave a sensitivity of 81.8% and a specificity of 72.3%. Subjective assessment of the mass gave a sensitivity of 72.7% with a specificity of 81.8%. Small masses were more commonly unilocular and large masses multilocular. Ascites, papillary proliferations, detectable flow and the smoothness of the internal wall discriminated well between benign and malignant small cysts. Age, menopausal status and CA125 were not discriminatory. None of the published models were as accurate as subjective assessment at diagnosing malignancy. These data suggest that statistical models may be of less value than tumour markers and subjective assessment in the diagnosis of ovarian malignancy.This work improves our ability to predict malignancy in a pelvic mass. As a result of this work, further research might aim to combine the use of tumour markers and subjective assessment to improve the preoperative diagnosis of malignancy. It may thus be possible to provide care in a cancer centre for those women that need it and to allow conservative management or minimally invasive surgery for women with benign disease

Prediction model and prognostic index to estimate clinically relevant pelvic organ prolapse in a general female population

Introduction and hypothesis: Estimation on prevalence and distribution of pelvic organ prolapse (POP) signs in a general female population is difficult. We therefore developed and validated

A Review

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given

Bioelectrical measurement for sugar recovery of sugarcane prediction using artificial neural network

One of the problems in the sugar industry is lack of low cost, simple and accurate measurement techniques for sugar recovery of sugarcane in the field or laboratory. This study investigated the potential using of bioelectrical properties as a non-destructive technique for this purpose. A parallel plate capacitor was developed to measure the bioelectric properties of sugarcane in a lateral and longitudinal position of the samples. Eighteen internode samples from 3 sugarcane varieties were measured within 0.1-10 kHz frequency range of LCR meter and then was analyzed sugar recovery in the laboratory. The result showed that in the lateral position are more capacitive and resistive than the longitudinal position. Artificial neural network (ANN) was developed for prediction of sugar recovery as a function of bioelectrical properties. The best ANN model produces a high accuracy in the lateral bioelectrical measurement position with a correlation coefficient (R) > 0.90 and mean square error (MSE) <; 0.05. It showed that the ANN model based on bioelectrical properties had the potential to be developed as a simple technique to predict the sugar recovery of sugarcane

Clinical, ultrasound parameters and tumor marker-based mathematical models and scoring systems in pre-surgical diagnosis of adnexal tumors

The choice of management for patients with adnexal tumors requires careful pre-surgical assessment. In case of adnexal masses, the diagnostic difficulties arise from the heterogenic nature of the adnexal diseases, presence of multiple functional changes, and lack of early symptoms of malignancy. A reliable pre-surgical differentiation cannot be performed using clinical features, ultrasound examination, or tumor markers alone. New diagnostic techniques and novel markers are under investigations, however no single test can be used to conclusively differentiate between malignant and non-malignant adnexal masses. Mathematical models and scoring systems based on different clinical, ultrasonographic and laboratory parameters alone or together may facilitate the diagnosis. Selected mathematical models and scoring systems are presented in this article. Models using only ultrasound features include simple rules, regression models, Gynecologic Imaging Report and Data System, and various morphologic scores. Some logistic regression models are based on multiple clinical and ultrasound data. The OVA1 test is based on five tumor markers without using other data. The Risk of Malignancy Algorithm uses two tumor markers with one clinical parameter. i.e. the menopausal status. Some models used clinical, ultrasound and tumor marker data together. This group of models includes risk of malignancy indices, artificial neural networks, and the ADNEX model. Although some of these models have been compared in the literature, more prospective studies are needed to select the most effective model, to develop the existing models, or to create new more effective models of oncological assessment of the adnexal tumors

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

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThere are several clinical prognostic models in the medical field. Prior to clinical use, the outcome models of longitudinal cohort data need to undergo a multi-centre evaluation of their predictive accuracy. This thesis evaluates the possible gain in predictive accuracy in multicentre evaluation of a flexible model with Bayesian regularisation, the (PLANN-ARD), using a reference data set for breast cancer, which comprises 4016 records from patients diagnosed during 1989-93 and reported by the BCCA, Canada, with follow-up of 10 years. The method is compared with the widely used Cox regression model. Both methods were fitted to routinely acquired data from 743 patients diagnosed during 1990-94 at the Christie Hospital, UK, with follow-up of 5 years following surgery. Methodological advances developed to support the external validation of this neural network with clinical data include: imputation of missing data in both the training and validation data sets; and a prognostic index for stratification of patients into risk groups that can be extended to non-linear models. Predictive accuracy was measured empirically with a standard discrimination index, Ctd, and with a calibration measure, using the Hosmer-Lemeshow test statistic. Both Cox regression and the PLANN-ARD model are found to have similar discrimination but the neural network showed marginally better predictive accuracy over the 5-year followup period. In addition, the regularised neural network has the substantial advantage of being suited for making predictions of hazard rates and survival for individual patients. Four different approaches to stratify patients into risk groups are also proposed, each with a different foundation. While it was found that the four methodologies broadly agree, there are important differences between them. Rules sets were extracted and compared for the two stratification methods, the log-rank bootstrap and by direct application of regression trees, and with two rule extraction methodologies, OSRE and CART, respectively. In addition, widely used clinical breast cancer prognostic indexes such as the NPI, TNM and St. Gallen consensus rules, were compared with the proposed prognostic models expressed as regression trees, concluding that the suggested approaches may enhance current practice. Finally, a Web clinical decision support system is proposed for clinical oncologists and for breast cancer patients making prognostic assessments, which is tailored to the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the NPI, Cox regression modelling and PLANN-ARD. For a given patient, all three models yield a generally consistent but not identical set of prognostic indices that can be analysed together in order to obtain a consensus and so achieve a more robust prognostic assessment of the expected patient outcome