A Method for Tumor Treating Fields Fast Estimation

Tumor Treating Fields (TTFields) is an FDA approved treatment for specific types of cancer and significantly extends patients life. The intensity of the TTFields within the tumor was associated with the treatment outcomes: the larger the intensity the longer the patients are likely to survive. Therefore, it was suggested to optimize TTFields transducer array location such that their intensity is maximized. Such optimization requires multiple computations of TTFields in a simulation framework. However, these computations are typically performed using finite element methods or similar approaches that are time consuming. Therefore, only a limited number of transducer array locations can be examined in practice. To overcome this issue, we have developed a method for fast estimation of TTFields intensity. We have designed and implemented a method that inputs a segmentation of the patients head, a table of tissues electrical properties and the location of the transducer array. The method outputs a spatial estimation of the TTFields intensity by incorporating a few relevant parameters in a random-forest regressor. The method was evaluated on 10 patients (20 TA layouts) in a leave-one-out framework. The computation time was 1.5 minutes using the suggested method, and 180-240 minutes using the commercial simulation. The average error was 0.14 V/cm (SD = 0.06 V/cm) in comparison to the result of the commercial simulation. These results suggest that a fast estimation of TTFields based on a few parameters is feasible. The presented method may facilitate treatment optimization and further extend patients life.

Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

Objective: Overlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structure size. Addressing these challenges is the objective of this work. Methods: We extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC is less or equal to 1 and cDC = 1 if-and-only-if the structures overlap is complete, and, 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brainstimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus. Results: Partial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively. Conclusion: The cDC is well defined for probabilistic segmentation, less biased to structure size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.

Evaluation of head segmentation quality for treatment planning of tumor treating fields in brain tumors

Tumor treating fields (TTFields) is an FDA approved therapy for the treatment of Gliobastoma Multiform (GBM) and currently being investigated for additional tumor types. TTFields are delivered to the tumor through the placement of transducer arrays (TAs) placed on the patient scalp. The positions of the TAs are associated with treatment outcomes via simulations of the electric fields. Therefore, we are currently developing a method for recommending optimal placement of TAs. A key step to achieve this goal is to correctly segment the head into tissues of similar electrical properties. Visual inspection of segmentation quality is invaluable but time-consuming. Automatic quality assessment can assist in automatic refinement of the segmentation parameters, suggest flaw points to the user and indicate if the segmented method is of sufficient accuracy for TTFields simulation. As a first step in this direction, we identified a set of features that are relevant to atlas-based segmentation and show that these are significantly correlated (p < 0.05) with a similarity measure between validated and automatically computed segmentations. Furthermore, we incorporated these features in a decision tree regressor to predict the similarity of the validated and computed segmentations of 20 TTFields patients using a leave-one-out approach. The predicted similarity measures were highly correlated with the actual ones (average abs. difference 3% (SD = 3%); r = 0.92, p < 0.001). We conclude that quality estimation of segmentations is feasible by incorporating machine learning and segmentation-relevant features.