A Method for Tumor Treating Fields Fast Estimation

• Published in: Simulation and Synthesis in Medical Imaging Vol. 12417; pp. 60 - 67
• Main Authors: Shamir, Reuben R., Bomzon, Zeev
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Simulation; Treatment planning; Tumor Treating Fields
• ISBN: 3030595196; 9783030595197
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-59520-3_7

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 patient’s 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 min using the suggested method, and 180–240 min 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.