Web-Based MICT Intervention Planning and Validation
There are several common methods of image guided minimally invasive cancer treatment (MICT) as for instance radiofrequency ablation, microwave ablation, cryoablation, brachytherapy or irreversible electroporation. However, yet there is no common software environment to plan and to predict the results from different types of MICT. The GoSmart project develops a web-based intervention planning system for assisting radiologists to choose the best patient specific approach in MICT by simulating the personalized result of the different treatments and medical protocols in patient specific conditions.
Different MICT types in liver, lung and kidney generate different clinical requirements regarding pre-interventional imaging, planning, treatment protocols and post-interventional analysis of the results. To identify the clinical requirements first of all specific procedures for each provided MICT types were analyzed. Therefore every medical partner described the individual workflows of his procedures. According to the Institutional Review Board (IRB) of every partner patient data were analyzed and collected retrospectively to prevent influence on the existing clinical workflows. Simulations are run for each included patient including comparison of the simulated with the real ablation zone in terms of validation.
Although workflows may differ between the several types of MICT certain similarities in the imaging and treatment protocols are obvious. These commonalities between the different procedures allow for the development of a generic, reusable, robust simulation environment with the relevant physics and physiology needed to correctly predict the result of each MICT in terms of lesion size and shape.
The web-based software environment will be open-ended with extendable interfaces within the framework to allow clinicians to add further patient data collected before, during and after different types of MICT. These data will be used to train and to refine the existing physiological models thus transforming the environment into a user-driven growing MICT info-structure.
For clinicians the environment will provide the optimal tool selection for patient specific treatment and will allow cross-validation of different types of MICT. The research community will use the environment to add novel and to refine existing tissue models with a broad range of patient data and MICT protocols. Furthermore, producers of novel MICT instruments may register their new products in the framework environment to validate them against existing benchmarks. Unifying and cross validation of different types of MICT into a common simulation environment will promote their systematic comparison and establish common standards and protocols for MICT.
The sharing of data and the use of a common simulation software environment will both increase the effectiveness of the MICTs and at the same time introduce cost savings through the streamlining of the information flow needed by interventionalists to provide patient specific treatments. In this way Go-Smart directly contributes to delivering more predictive, more individualized, more effective and safer healthcare. Moreover, industrial producers of MICT instruments will be provided with the right benchmark environment for comparative tests of new products and physical parameters.