Investigation of intra-tumour heterogeneity to identify texture features to characterise and quantify neoplastic lesions on imaging

The aim of this work was to further our knowledge of using imaging data to discover image derived biomarkers and other information about the imaged tumour. Using scans obtained from multiple centres to discover and validate the models has advanced earlier research and provided a platform for further larger centre prospective studies. This work consists of two major studies which are describe separately: STUDY 1: NSCLC Purpose The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). Patients and methods Pre-therapy PET scans from 358 Stage I–III NSCLC patients scheduled for radical radiotherapy/chemoradiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. Using a semiautomatic threshold method to segment the primary tumors, radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. Results Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. Conclusion PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy. STUDY 2: Ovarian Cancer Purpose The 5-year survival of epithelial ovarian cancer is approximately 35-40%, prompting the need to develop additional methods such as biomarkers for personalised treatment. Patient and Methods 657 texture features were extracted from the CT scans of 364 untreated EOC patients. A 4-texture feature ‘Radiomic Prognostic Vector (RPV)’ was developed using machine learning methods on the training set. Results The RPV was able to identify the 5% of patients with the worst prognosis, significantly improving established prognostic methods and was further validated in two independent, multi-centre cohorts. In addition, the genetic, transcriptomic and proteomic analysis from two independent datasets demonstrated that stromal and DNA damage response pathways are activated in RPV-stratified tumours. Conclusion RPV could be used to guide personalised therapy of EOC. Overall, the two large datasets of different imaging modalities have increased our knowledge of texture analysis, improving the models currently available and provided us with more areas with which to implement these tools in the clinical setting.Open Acces

Improvement and implementation of analog based method for software project cost estimation

Ph.DDOCTOR OF PHILOSOPH

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified

A total quality management (TQM) strategic measurement perspective with specific reference to the software industry

The dissertation aims to obtain an integrated and comprehensive perspective on measurement issues that play a strategic role in organisations that aim at continuous quality improvement through TQM. The multidimensional definition of quality is proposed to view quality holistically. The definition is dynamic, thus dimensions are subject to evolution. Measurement of the quality dimensions is investigated. The relationship between quality and cost, productivity and profitability respectively is examined. The product quality dimensions are redefined for processes. Measurement is a strategic component ofTQM. Integration of financial measures with supplier-; customer-; performance- and internal process measurement is essential for synergism. Measurement of quality management is an additional strategic quality dimension. Applicable research was integrated. Quantitative structures used successfully in industry to achieve quality improvement is important, thus the quality management maturity grid, cleanroom software engineering, software factories, quality function deployment, benchmarking and the ISO 9000 standards are briefly described. Software Metrics Programs are considered to be an application of a holistic measurement approach to quality. Two practical approaches are identified. A framework for initiating implementation is proposed. Two strategic software measurement issues are reliability and cost estimation. Software reliability measurement and modelling are introduced. A strategic approach to software cost estimation is suggested. The critical role of data collection is emphasized. Different approaches to implement software cost estimation in organisations are proposed. A total installed cost template as the ultimate goal is envisaged. An overview of selected software cost estimation models is provided. Potential research areas are identified. The linearity/nonlinearity nature of the software production function is analysed. The synergy between software cost estimation models and project management techniques is investigated. The quantification aspects of uncertainty in activity durations, pertaining to project scheduling, are discussed. Statistical distributions for activity durations are reviewed and compared. A structural view of criteria determining activity duration distribution selection is provided. Estimation issues are reviewed. The integration of knowledge from dispersed fields leads to new dimensions of interaction. Research and practical experience regarding software metrics and software metrics programs can be successfully applied to address the measurement of strategic indicators in other industries.Business ManagementD. Phil. (Operations Research

Bayesian statistical effort prediction models for data-centred 4GL software development

Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper presents three Bayesian statistical software effort prediction models for database-oriented software systems, which are developed using a specific 4GL tool suite. The models consist of specification-based software size metrics and development team’s productivity metric. The models are constructed based on the subjective knowledge of human expert and calibrated using empirical data collected from 17 software systems developed in the target environment. The models’ predictive accuracy is evaluated using subsets of the same data, which were not used for the models’ calibration. The results show that the models have achieved very good predictive accuracy in terms of MMRE and pred measures. Hence it is confirmed that the Bayesian statistical models can predict effort successfully in the target environment. In comparison with commonly used multiple linear regression models, the Bayesian statistical models’ predictive accuracy is equivalent in general. However, when the number of software systems used for the models’ calibration becomes smaller than five, the predictive accuracy of the best Bayesian statistical models are significantly better than the multiple linear regression model. This result suggests that the Bayesian statistical models would be a better choice when software organizations/practitioners do not posses sufficient empirical data for the models’ calibration. The authors expect those findings encourage more researchers to investigate the use of Bayesian statistical models for predicting software effort.UnpublishedA.J. Albrecht and J.E. Gaffney. Software function, source lines of code, and development effort prediction: a software science validation. IEEE Transactions on Software Engineering, SE–9(6):639–648, 1983. J. Baik, B. Boehm, and B.M. Steece. Disaggregating and calibrating the CASE tool variable in COCOMO II. IEEE Transactions on Software Engineering, 28(11):1009–1022, 2002. B.W. Boehm. Software Engineering Economics. Prentice–Hall, 1981. S. Chulani, B. Boehm, and B.M. Steece. Bayesian analysis of empirical software engineering cost models. IEEE Transactions on Software Engineering, 25(4):513–583, 1999. P. Congdon. Bayesian Statistical Modelling. John Wiley & Sons., 2001. S.D. Conte, H.E. Dunsmore, and V.Y. Shen. Software Engineering Metrics and Models. Benjamin/Cummings Publishing Company, 1986. R.G. Cowell, A.P. Dawid, S.L. Lauritzen, and D.J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer–Verlag New York, 1999. J.J. Dolado. A study of the relationships among albrecht and mark ii function points, lines of code, 4gl and effort. Journal of Systems Software, 37:161–173, 1997. J.J. Dolado. A validation of the component-based method for software size estimation. IEEE Transactions on Software Engineering, 26(10):1006–1021, 2000. N. Fenton and M. Neil. A critique of software defect prediction models. IEEE Transactions on Software Engineering, 25(5):675–689, 1999. N.E. Fenton and S.L. Pfleeger. Software Metrics:A Rigorous & Practical Approach. PWS Publishing Company, second edition, 1997. T. Foss, E. Stensrud, B. Kitchenham, and I. Myrtveit. A simulation study of the model evaluation criterion mmre. IEEE Transactions on Software Engineering, 29(11):985–995, 2003. R.L. Glass. Frequently forgotten fundamental facts about software engineering. IEEE Software, May/June:110–112, 2001. P.J. Green. A primer on markov chain monte carlo. In O.E. Barndorff-Nielsen, D.R. Cox, and C. Klüppelberg, editors, Complex Stochastic Systems, chapter 1, pages 1–62. Chapman & Hall/CRC, 2001. F.V. Jensen. Bayesian Networks and Decision Graphs. Springer–Verlag New York, 2001. C.F. Kemerer. An empirical validation of software cost estimation models. Communications of the ACM, 30(5):416–429, 1987. B.A. Kitchenham, L.M. Pickard, S.G. MacDonell, and M.J. Shepperd. What accuracy statistics really measure. IEE Proceedings–Software, 148(3):81–85, 2001. S.G. MacDonell. Establishing relationships between specification size and software process effort in case environment. Information and Software Technology, 39:35–45, 1997. M. Neil, N. Fenton, and L. Nielsen. Building large-scale bayesian networks. The Knowledge Engineering Review, 15(3):257–284, 2000. M.J. Shepperd, M. Cartwright, and G. Kadoda. On building prediction systems for software engineers. Empirical Software Engineering, 5:175–182, 2000. I. Stamelos, L. Angelis, P. Dimou, and E. Sakellaris. On the use of Bayesian belief networks for the prediction of software productivity. Information and Software Technology, 45:51–60, 2003. E. Stensrud, T. Foss, B.A. Kitchenham, and I. Myrtveit. An empirical validation of the relationship between the magnitude of relative error and project size. In Proceedings of the 8th IEEE Symposium on Software Metrics (METRICS’02), pages 3–12, 2002. B. Stewart. Predicting project delivery rates using the Naive–Bayes classifier. Journal of Software Maintenance and Evolution: Research and Practice, 14:161–179, 2002. G. Tate and J.M. Verner. Approaches to measuring size of application products with case tools. Information and Software Technology, 33(9):622–628, 1991. C. van Koten and A.R. Gray. An application of bayesian network for predicting object-oriented software maintainability. Information and Software Technology, in press, 2005. J.M. Verner and G. Tate. A software size model. IEEE Transactions on Software Engineering, 18(4):265–278, 1992

A classroom-based investigation into the potential of a computer-mediated criterion-referenced test as an evaluation instrument for the assessment of primary end user spreadsheet skills

The demand for innovative end users of information technology is increasing along with the proliferation of computer equipment within the workplace. This has resulted in increasing demands being made upon educational institutions responsible for the education of computer end users. The demands placed upon the teachers are particularly high. Large class groups and limited physical resources make the task especially difficult. One of the most time consuming, yet important, tasks is that of student evaluation. To effectively assess the practical work of information technology students requires intensive study of the storage media upon which the students'efforts have been saved. The purpose of this study was to assess the suitability of criterion-referenced testing techniques applied to the evaluation of end user computing students. Objective questions were administered to the students using Question Mark, a computer-managed test delivery system which enabled quick and efficient management of scoring and data manipulation for empirical analysis. The study was limited to the classroom situation and the assessment of primary spreadsheet skills. In order to operate within these boundaries, empirical techniques were used which enabled the timeous analysis of the students' test results. The findings of this study proved to be encouraging. Computer-mediated criterion-referenced testing techniques were found to be sufficiently reliable for classroom practice when used to assess primary spreadsheet skills. The validation of the assessment technique proved to be problematic because of the constraints imposed by normal classroom practice as well as the lack of an established methodology for evaluating spreadsheet skills. However, sufficient evidence was obtained to warrant further research aimed at assessing the use of computer-mediated criterion-referenced tests to evaluate information technology end user learning in situations beyond the boundaries of the classroom, such as a national certification examination