Fully or semi-automatic contouring tools are increasingly being used in the tumor contouring task for radiotherapy. While the fully automatic contouring tools have not reached sufficient efficiency, the semi-automatic contouring tools balance more effectively between the human interaction and automation. This study evaluates the influences of a semi-automation contouring tool, called between-slice interpolation, on the resulting contours and the contouring process. The tumor contouring study was conducted on three patient cases with five physicians in a naturalistic setting. The contouring task consisted of initiating the 2D contour manually or with the interpolation tool and correcting that initial contour. The similarity of the resulting contours was pairwise measured within the manual or the interpolated category. Interactions with the software were recorded, and variations in the contouring workflows steps were compared. Results indicated that using the between-slice interpolation tool for creating the initial contour, instead of initiating it manually, influenced both the contouring process and outcomes. First, it was identified that contours initiated by the interpolation tool showed an increased similarity among themselves compared to the manually initiated contours. At the same time, influences to the resulting contours were below clinical relevance, and toward the desired direction-improved consistency of contours. Second, when interpolation was used, in two cases out of three, the average contouring time also decreased significantly. Therefore, the use of such an automation tool can be encouraged.

a b s t r a c tSensemaking theories help designers understand the cognitive processes of a user when he/she performs a complicated task. This paper introduces a two-step approach of incorporating sensemaking support within the design of health information systems by: (1) modeling the sensemaking process of physicians while performing a task, and (2) identifying software interaction design requirements that support sensemaking based on this model. The two-step approach is presented based on a case study of the tumor contouring clinical task for radiotherapy planning. In the first step of the approach, a contextualized sensemaking model was developed to describe the sensemaking process based on the goal, the workflow and the context of the task. In the second step, based on a research software prototype, an experiment was conducted where three contouring tasks were performed by eight physicians respectively. Four types of navigation interactions and five types of interaction sequence patterns were identified by analyzing the gathered interaction log data from those twenty-four cases. Further in-depth study on each of the navigation interactions and interaction sequence patterns in relation to the contextualized sensemaking model revealed five main areas for design improvements to increase sensemaking support. Outcomes of the case study indicate that the proposed two-step approach was beneficial for gaining a deeper understanding of the sensemaking process during the task, as well as for identifying design requirements for better sensemaking support.

In radiotherapy, the delineations of the volume of the macroscopic spread of the tumor as the Gross Tumor Volume (GTV), and the surrounding volume of the microscopic spread of the disease as the Clinical Target Volume (CTV), are key tasks for a high quality treatment plan. In order to design a software that also supports cognition, software designers need a deeper understanding of the physicians’ cognitive processes and medical context. This paper presents a research about identifying main medical factors relevant for the delineation in the radiotherapy context as a first step in deepening the understanding for software design. Using two discussion formats with six radiation oncologists, we identified 29 medical factors regarding the delineations of tumorous volumes, categorized into: treatment context, tumor context and tumorous areas. In addition, the role of multimodal images, dose planning, and future wishes have been elaborated. These findings could support the software designers in using Evidence Based Software Engineering approach. It is expected that software designed based on the results presented here, is tailored towards the medical context and cognitive needs of radiation oncologists in the delineations of brain tumor, and therefore will improve the effectiveness and efficiency of their work.

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