Abstract It is generally accepted practice that each cancer patient case should be discussed in a clinical expert meeting, the so‐called tumor board. A central role in finding the best therapy options for patients with solid tumors plays the Tumor, lymph Node, and Metastasis staging (TNM staging). Correctness of TNM staging has a significant impact on the therapy choice and hence on the patient's post‐therapeutic quality of life or even survival. If inconsistencies in the TNM staging occur, possible explanations and solutions must be found based on the complex patient records, which takes the costly time of (multiple) physicians. We propose a more efficient visual analysis component, which supports a physician in verifying the given TNM staging before forwarding it to the tumor board. Our component comprises a Bayesian network model of the TNM staging process. Using information from the patient records and Bayesian inference, the models computes a patient‐specific TNM staging, which is then explored and compared to the given staging by means of a graph‐based visualization. Our component is implemented in a research prototype that supports an understanding of the model computations, allows for a fast identification of important influencing factors, and facilitates a quick detection of differences between two TNM stagings. We evaluated our component with five physicians, each studying 20 cases of laryngeal cancer.

In this article, a new Micromanipulator System (MMS-II) for middle ear surgery is presented. The purpose of this work was to develop a simple but effective manipulator that would enable the surgeon to move standard surgical instruments in a precise way even under non-ergonomic conditions. The MMS-II is lightweight, small, and easy to use; it requires no PC, besides a small microcontroller-based joystick console. Such features, together with a practicable sterilization concept and the availability of a multiplicity of surgical instruments, allow the system to be used in standard surgical procedures.