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Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

A figure showing the process of generating and using PDX models.

The focus of this chapter is the provision of decision support modeled, at least in part, on human expertise. This chapter is divided into sections, each concerned with some aspect of the relationship between human expertise, statistical modeling, and knowledge-based systems, in particular expert systems. The first section provides a comparison of human expert judgment with statistical models, particularly linear regression models, concluding that statistical techniques have been shown to be more accurate than human experts given knowledge of which variables to include in the analysis and the relevant data. The second section provides a brief introduction to expert systems and a comparison of the accuracy of expert systems with human experts. Like statistical models, some expert systems have been shown to be more accurate than human experts. The third section discusses the circumstances under which symbolic representation of knowledge (as rules or frames) or purely statistical approaches are likely to be more useful in building decision aids. Since the utility of a decision support system is a combined function of both the cost of system development and the change in performance of the decision maker(s) using the system, this analysis necessarily raises considerations relating to knowledge engineering and utility of system explanations as well as that of accuracy. The final section summarizes the relative merits and demerits of statistical and symbolic styles of reasoning, emphasizing the complementarity of the two approaches, and provides a brief illustration of how statistical and symbolic reasoning may be combined within a knowledge-based decision support system.