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When solving clinical decision-making problems with modern graphical decision-theoretic models such as influence diagrams, we obtain decision tables with optimal decision alternatives describing the best course of action for a given patient or group of patients. For real-life clinical problems, these tables are often extremely large. This is an obstacle to understand their content. KBM2L lists are structures that minimize memory storage requirements for these tables, and, at the same time, improve their knowledge organization. The resulting improved knowledge organization can be interpreted as explanations of the decision-table content. In this paper, we explore the use of KBM2L lists in analyzing and explaining optimal treatment selection in patients with non-Hodgkin lymphoma of the stomach using an expert-designed influence diagram as an experimental vehicle. The selection of the appropriate treatment for …