Segmentation of the Upper Torso for Lung Cancer TTFields Treatment Planning

Abstract

<h3>Purpose/Objective(s)</h3> Tumor Treating Fields (TTFields) is an FDA-approved treatment for glioblastoma multiforme (GBM) and malignant pleural mesothelioma (MPM). Moreover, TTFields therapy is currently investigated in a phase III clinical trial for the treatment of advanced Non-Small Cell Lung Cancer (NSCLC). Recent studies have shown that larger TTFields dose was associated with longer patients' survival. Therefore, personalized simulations to estimate the dose are performed as part of the patient treatment planning. For MPM and NSCLC treatment, these simulations require the segmentation of all upper torso tissues. A manual segmentation of the torso requires a few dozens of hours per patient and is impractical. Therefore, we have developed a computational method for semi-automatic segmentation of all upper torso tissues that are relevant to TTFields treatment planning. <h3>Materials/Methods</h3> We have incorporated a dataset of 40 CT images of NSCLC patients that underwent TTFields treatment in the lungs for this study. We have utilized threshold-based methods combined with morphological operations, region growing methods and known anatomical spatial relations to automatically identify and segment the lungs, bones, skin, muscle, fat, spinal cavity, costal cartilage, trachea and bronchi. Other structures such as the heart, blood vessels, liver, stomach, spleen, esophagus, diaphragm and intervertebral discs were semi-automatically segmented by using a few reference points that were provided by a human rater. Since there is no gold standard segmentation of the whole torso, an experienced radiation oncologist that is highly familiar with TTFields treatment inspected the results of the algorithm on top of the original CT images. <h3>Results</h3> The radiation oncologist has confirmed that the semi-automatic segmentation of the torso provides an adequate quality result for TTFields treatment planning for all cases. The segmentation time was reduced to one hour on a typical patient, compared to 20 hours that is the estimated time required for a fully manual segmentation. <h3>Conclusion</h3> We have presented a method for adequate quality segmentation of the upper torso in a reasonable time to facilitate TTFields treatment planning. In addition, this method facilitates the creation of a dataset for the development of state-of-the-art segmentation methods that utilize deep learning methods.