Creating Computational Models for Planning TTFields Treatment for Tumors in the Infratentorial Brain.

Abstract

PURPOSE/OBJECTIVE(S)\nTumor Treating Fields (TTFields) is an FDA-approved treatment for glioblastoma multiforme (GBM) and malignant pleural mesothelioma (MPM), and is currently being investigated in a phase III trial for the treatment of brain metastases from non-small cell lung cancer (NSCLC). Increased TTFields dose density at the tumor is associated with longer patient survival. Therefore, estimating dose at the tumor utilizing computational simulations with patient-specific computational models are integral for treatment planning. NSCLC brain metastases are often observed in infratentorial brain regions. In these cases, TTFields arrays may be placed on the head, neck, and upper back. Manual segmentation of this large area is impractical and time consuming. Therefore, we developed a computational method for semi-automatic segmentation of infratentorial tissues relevant to TTFields treatment planning.\n\n\nMATERIALS/METHODS\nWe incorporated a dataset of 20 head T1w Gad MRIs of patients treated with TTFields with segmentation of the infratentorial area, and 20 CT images that incorporate head, neck and upper torso from a public database for segmentation of extracranial structures. For the segmentation of the infratentorial zone we developed an atlas-based method that deforms a predefined statistical atlas to best fit the patient s MRI. Our method then revises the initial fitting to ensure the segmented structures adhere to known anatomical relations. For supratentorial brain areas, we incorporate a custom atlas-based method. Extra-cranial zones (head, neck and upper torso) were segmented with threshold-based methods for the skin, muscle, fat, bones, and bronchi. User-defined landmarks were used to segment the esophagus and the artery. The spine and CSF were segmented automatically by identifying the relevant area and defining a constant ratio between their diameters. To evaluate the segmentation of the infratentorial regions, a trained annotator manually segmented the infratentorial area of the 20 head MRIs. We compared manual and automatic segmentations and measured the Dice overlap score.\n\n\nRESULTS\nThe average Dice coefficient between the human annotator and the algorithms segmentation was 0.84 (SD\u202f=\u202f0.05) for cerebellum and 0.67 (SD\u202f=\u202f0.05) for brainstem. These results are comparable to those observed in supratentorial regions with a method that was verified previously. The radiation oncologist confirmed that the semi-automatic segmentation of the infratentorial, and extracranial head, neck and upper torso provides a quality result for TTFields treatment planning in a reasonable amount of time for all cases. The infratentorial segmentation is fully automatic and typically lasts a few minutes. Segmentation of extracranial structures is semi-automatic and typically lasts less than an hour.\n\n\nCONCLUSION\nWe have developed a fast method for the segmentation of infratentorial structures for TTFields planning.