The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

This paper addresses the political nature of requirements for large systems, and argues that requirements engineering theory and practice must become more engaged with these issues. It argues that large-scale system requirements is constructed through a political decision process, whereby requirements emerge as a set of mappings between consecutive solution spaces justified by a problem space of concern to a set of principals. These solution spaces are complex sociotechnical ensembles that often exhibit non-linear behaviour in expansion due to domain complexity and political ambiguity. Stabilisation of solutions into agreed-on specifications occurs only through the exercise of organisational power. Effective requirements engineering in such cases is most effectively seen as a form of heterogeneous engineering in which technical, social, economic and institutional factors are brought together in a current solution space that provides the baseline for construction of proposed new solution spaces.

The unpredictability of business processes requires that workflow systems support exception handling with the ability to dynamically adapt to the changing environment. Traditional approaches to handling this problem have fallen short, providing little support for change, particularly once the process has begun execution. Further, exceptions vary widely in their character and significance, challenging the application of any single approach to handling them. We briefly discuss the classification of exceptions, highlighting differing impacts on the workflow model. Based on this discussion we suggest principal goals to address in the development of adaptive workflow support, including strategies for avoiding exceptions, detecting them when they occur, and handling them at various levels of impact. We then identify a number of specific approaches to supporting these goals within the design of a workflow system infrastructure. Finally, we describe the implementation of many of these approaches in the Endeavors workflow support system. / z 7 4> n L' rs ru).

Background. Src, EphA2, and platelet-derived growth factor receptors ␣ and ␤ are dysregulated in pancreatic ductal adenocarcinoma (PDAC). Dasatinib is an oral multitarget tyrosine kinase inhibitor that targets BCR-ABL, c-Src, c-KIT, plateletderived growth factor receptor ␤, and EphA2. We conducted a phase II, single-arm study of dasatinib as first-line therapy in patients with metastatic PDAC. Methods. Dasatinib (100 mg twice a day, later reduced to 70 mg twice a day because of toxicities) was orally administered continuously on a 28-day cycle. The primary endpoint was overall survival (OS). Response was measured using the Response Evaluation Criteria in Solid Tumors. Circulating tumor cells (CTCs) were also collected. Results. Fifty-one patients enrolled in this study. The median OS was 4.7 months (95% confidence interval [CI]: 2.8-6.9 months). Median progression-free survival was 2.1 months (95% CI: 1.6-3.2months).In34evaluablepatients,thebestresponseachieved was stable disease in 10 patients (29.4%). One patient had stable disease while on treatment for 20 months. The most common nonhematologic toxicities were fatigue and nausea. Edema and pleural effusions occurred in 29% and 6% of patients, respectively. The number of CTCs did not correlate with survival. Conclusion. Single-agent dasatinib does not have clinical activity in metastatic PDAC.

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