A novel method for planning of image-guided radiofrequency ablation by means of interactive access path determination based on optimization is presented. A first retrospective study indicates that the method is suited to improve the classical planning of RFA.

Background: Features characterizing the immune contexture (IC) in the tumor microenvironment can be prognostic and predictive biomarkers. Identifying novel biomarkers can be challenging due to complex interactions between immune and tumor cells and the abundance of possible features.Methods: We describe an approach for the data-driven identification of IC biomarkers. For this purpose, we provide mathematical definitions of different feature classes, based on cell densities, cell-to-cell distances, and spatial heterogeneity thereof. Candidate biomarkers are ranked according to their potential for the predictive stratification of patients.Results: We evaluated the approach on a dataset of colorectal cancer patients with variable amounts of microsatellite instability. The most promising features that can be explored as biomarkers were based on cell-to-cell distances and spatial heterogeneity. Both the tumor and non-tumor compartments yielded features that were potentially predictive for therapy response and point in direction of further exploration.Conclusion: The data-driven approach simplifies the identification of promising IC biomarker candidates. Researchers can take guidance from the described approach to accelerate their biomarker research.

Magnetic resonance guided focused ultrasound surgery (MRgFUS) has become an attractive, non-invasive treatment for benign and malignant tumours, and offers specific benefits for poorly accessible locations in the liver. However, the presence of the ribcage and the occurrence of liver motion due to respiration limit the applicability MRgFUS. Several techniques are being developed to address these issues or to decrease treatment times in other ways. However, the potential benefit of such improvements has not been quantified. In this research, the detailed workflow of current MRgFUS procedures was determined qualitatively and quantitatively by using observation studies on uterine MRgFUS interventions, and the bottlenecks in MRgFUS were identified. A validated simulation model based on discrete events simulation was developed to quantitatively predict the effect of new technological developments on the intervention duration of MRgFUS on the liver. During the observation studies, the duration and occurrence frequencies of all actions and decisions in the MRgFUS workflow were registered, as were the occurrence frequencies of motion detections and intervention halts. The observation results show that current MRgFUS uterine interventions take on average 213min. Organ motion was detected on average 2.9 times per intervention, of which on average 1.0 actually caused a need for rework. Nevertheless, these motion occurrences and the actions required to continue after their detection consumed on average 11% and up to 29% of the total intervention duration. The simulation results suggest that, depending on the motion occurrence frequency, the addition of new technology to automate currently manual MRgFUS tasks and motion compensation could potentially reduce the intervention durations by 98.4% (from 256h 5min to 4h 4min) in the case of 90% motion occurrence, and with 24% (from 5h 19min to 4h 2min) in the case of no motion. In conclusion, new tools were developed to predict how intervention durations will be affected by future workflow changes and by the introduction of new technology.

Focused ultrasound surgery (FUS) is a non-invasive method for tissue ablation that has the potential for complete and controlled local tumour destruction with minimal side effects. The treatment of abdominal organs such as the liver, however, requires particular technological support in order to enable a safe, efficient and effective treatment. As FUS is applied from outside the patient's body, suitable imaging methods, such as magnetic resonance imaging or diagnostic ultrasound, are needed to guide and track the procedure. To facilitate an efficient FUS procedure in the liver, the organ motion during breathing and the partial occlusion by the rib cage need to be taken into account in real time, demanding a continuous patient-specific adaptation of the treatment configuration. Modelling the patient's respiratory motion and combining this with tracking data improves the accuracy of motion predictions. Modelling and simulation of the FUS effects within the body allows the use of treatment planning and has the potential to be used within therapy to increase knowledge about the patient status. This article describes integrated model-based software for patient-specific modelling and prediction for FUS treatments of moving abdominal organs.

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