A Method for Tumor Treating Fields Fast Estimation

TL;DR

This paper introduces a rapid estimation method for Tumor Treating Fields intensity that significantly reduces computation time, enabling more efficient treatment optimization without sacrificing accuracy.

Contribution

A novel machine learning-based approach for fast TTFields intensity estimation using a random-forest regressor, reducing computation time from hours to minutes.

Findings

Computation time reduced to 1.5 minutes from 3 hours.

Average estimation error of 0.14 V/cm compared to detailed simulations.

Method feasible for treatment optimization in clinical settings.

Abstract

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.