Clinical relevance of model based computer-assisted diagnosis and therapy

Abstract

The ability to acquire and store radiological images digitally has made this data available to mathematical and scientific methods. With the step from subjective interpretation to reproducible measurements and knowledge, it is also possible to develop and apply models that give additional information which is not directly visible in the data. In this context, it is important to know the characteristics and limitations of each model. Four characteristics assure the clinical relevance of models for computer-assisted diagnosis and therapy: ability of patient individual adaptation, treatment of errors and uncertainty, dynamic behavior, and in-depth evaluation. We demonstrate the development and clinical application of a model in the context of liver surgery. Here, a model for intrahepatic vascular structures is combined with individual, but in the degree of vascular details limited anatomical information from radiological images. As a result, the model allows for a dedicated risk analysis and preoperative planning of oncologic resections as well as for living donor liver transplantations. The clinical relevance of the method was approved in several evaluation studies of our medical partners and more than 2900 complex surgical cases have been analyzed since 2002

Interactive determination of robust safety margins for oncologic liver surgery

“…The planning tool is available as an add-on for the planning software MeVis LiverAnalyzer [4] and integrates smoothly into a preoperative planning workflow. Preliminary evaluations confirm that the width of safety margins can be determined more precisely due to the interactive in-depth analysis, which may affect the proposed resection strategy.…”

“…The reconstruction of 3D planning models and associated risk analyses are an integral part of the software assistant MeVisLiverAnalyzer [4], which comprises all steps for analyzing contrast-enhanced CT and MRI data for preoperative planning in liver surgery. We briefly describe the important steps for vascular analysis and visualization as introduced in [5]: vascular structures are segmented semiautomatically using a threshold-based region-growing technique.…”

Impact of model-based risk analysis for liver surgery planning

“…This would require a clinical study with a randomized decision regarding the utilization of the results of the risk analysis and the subsequent evaluation of clinical criteria, such as complication rate, tumor recurrence, and blood loss [7]. The participants could be divided in different groups depending on their level of training (surgical experience, experience with 3D planning software and experience with model-based risk analysis) to evaluate how this affects the outcome of surgical decisions.…”

Evaluation of a portable image overlay projector for the visualisation of surgical navigation data: phantom studies

“…Surface models of a liver and its internal vessels and tumours were pre-segmented from CT by Mevis Distant Services (MeVis Medical Solutions AG, Bremen, Germany) via the process described in [18], see . The liver model construction process was completed as per non-AR navigated liver surgery, and the models were added to the surgical navigation systems virtual 3D scene as normal.…”