RBIO-01. DEVELOPING THE FRAMEWORK FOR TUMOR TREATING FIELDS (TTFIELDS) TREATMENT PLANNING FOR A PATIENT WITH ASTROCYTOMA IN THE SPINAL CORD

Abstract The use of Tumor Treating Fields (TTFields) following resection and chemoradiation has increased survival in patients with Glioblastoma. Patient-specific planning for TTFields transducer array placement has been demonstrated to maximize TTFields dose at the tumor: providing higher TTFields intensity (≥ 1.0 V/cm) and power density (≥ 1.1 mW/cm3) which are associated with improved overall survival. Treatment planning was performed for a 48 year old patient following T10-L1 laminectomy, gross total resection, and postoperative chemoradiation for an anaplastic astrocytoma of the spinal cord. An MRI at 3 weeks following chemoradiation showed tumor recurrence. Based on the post-chemoradiation MRI, a patient-specific model was created. The model was created by modifying a realistic computational phantom of a healthy female. To mimic the laminectomy, the lamina in T10-L1 was removed, and the region assigned electric conductivity similar to that of muscle. A virtual mass was introduced into the spinal cord. Virtual transducer arrays were placed on the model at multiple positions, and delivery of TTFields simulated. The dose delivered by different transducer array layouts was calculated, and the layouts that yielded maximal dose to the tumor and spine identified. Transducer array layouts, in which the arrays were placed on the back of the patient with one array above the tumor and one array below the tumor, yielded the highest doses at the tumor site. Such layouts yielded TTFields doses of over 3.4mW/cm3 which is well above the threshold dose of 1.1 mW/cm3 reported previously [Ballo et al. Red Jour 2019]. The framework developed for TTFields dosimetry and treatment planning for this spinal tumor will have the potential to increase dose delivery to the tumor bed while optimizing placement that may enhance comfort and encourage device usage.

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INNV-05. TUMOR TREATING FIELDS (TTFIELDS) TREATMENT PLANNING FOR A PATIENT WITH ASTROCYTOMA IN THE SPINAL CORD

Neuro-Oncology ◽

10.1093/neuonc/noaa215.489 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii117-ii117

Author(s):

Jennifer De Los Santos ◽

Smadar Arvatz ◽

Oshrit Zeevi ◽

Shay levi ◽

Zeev Bomzon ◽

...

Keyword(s):

Spinal Cord ◽

Numerical Simulations ◽

Treatment Planning ◽

Spinal Tumor ◽

Specific Model ◽

Patient Specific ◽

Transducer Array ◽

Tumor Treating Fields ◽

Healthy Female ◽

Specific Manner

Abstract BACKGROUND The use of Tumor Treating Fields (TTFields) following resection and chemoradiation has increased survival in patients with Glioblastoma. Randomized data provide strong rationale for planning TTFields transducer array placement to maximize TTFields dose at the tumor in a patient-specific manner. Here we present a case demonstrating the use of numerical simulations for patient-specific TTFields treatment planning for a spinal tumor. METHODS Treatment planning was performed for a 48 year old patient following T10-L1 laminectomy, gross total resection, and postoperative chemoradiation for an anaplastic astrocytoma of the spinal cord. An MRI at 3 weeks following chemoradiation showed tumor recurrence. Based on the post-chemoradiation MRI, a patient-specific model was created. The model was created by modifying a realistic computational phantom of a healthy female. To mimic the laminectomy, the lamina in T10-L1 was removed, and the region assigned electric conductivity similar to that of muscle. A virtual mass was introduced into the spinal cord. Virtual transducer arrays were placed on the model at multiple positions, and delivery of TTFields simulated. The dose delivered by different transducer array layouts was calculated, and the layouts that yielded maximal dose to the tumor and spine identified. RESULTS Transducer array layouts, in which the arrays were placed on the back of the patient with one array above the tumor and one array below the tumor, yielded the highest doses at the tumor site. Such layouts yielded TTFields doses of over 3.4mW/cm3 which is well above the threshold dose of 1.1 mW/cm3 reported previously [Ballo et al Red Jour 2019]. CONCLUSIONS These data represent the first ever study on utilizing numerical simulations in order to plan treatment for a spinal tumor in a patient-specific manner. This is an important milestone in the developing a framework for TTFields dosimetry and treatment planning.

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EXTH-37. A NOVEL TRANSDUCER ARRAY LAYOUT FOR DELIVERING TUMOR TREATING FIELDS TO THE SPINE

Neuro-Oncology ◽

10.1093/neuonc/noz175.369 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi90-vi90

Author(s):

Ariel Naveh ◽

Ofir Yesharim ◽

Ze’ev Bomzon

Keyword(s):

Spinal Cord ◽

Electric Fields ◽

Computational Models ◽

Leptomeningeal Disease ◽

Computational Simulations ◽

Transducer Array ◽

Tumor Treating Fields ◽

Transducer Arrays ◽

Perpendicular Field ◽

Computational Platform

Abstract Tumor Treating Fields (TTFields) are an antimitotic technology utilising electric fields to disrupt mitosis in cancer cells. TTFields are currently approved by the FDA for the treatment of Glioblastoma Multiforme (GBM) and Malignant Pleural Mesothelioma (MPM). TTFields are delivered through 2 pairs of transducer arrays placed on the patient’s skin. Each pair delivers TTFields in a single direction, and the pairs are placed to provide perpendicular field. Preclinical studies show that 1V/cm is the clinical threshold for the treatment to be effective. Some types of cancers send metastases to the spinal cord and CSF, i.e. leptomeningeal disease. The purpose of this study was to find transducer array layouts that deliver TTFields to the spine at therapeutic intensities of above 1 V/cm. Computational simulations testing the delivery of TTFields to the spine were performed using the Sim4Life 4.0 (ZMT Zurich) computational platform, and the Duke 3.1 and Ella 3.0 (ITI’S, Zurich) realistic computational models of a male and female respectively. “Standard” layouts in which a pair of arrays are placed on the front and back of the patient and second pair on the lateral aspects of the patient failed to deliver TTFields at therapeutic intensities to the spinal cord. This is probably because the spinal cord is surrounded by the CSF and spine, which shunt the electric fields from reaching the spinal cord. However, field intensities above 1 V/cm were observed when delivering TTFields when both arrays were placed on the patients back, with a first array placed close to the neck, and second array placed towards the thighs. In this case, the spinal cord and surrounding CSF act as a conductive cable, directing the electric field along the spine. This novel layout opens the possibility for treating cancerous disease along the spine.

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NIMG-65. STUDY OF LOCAL PERTURBATION IN COMPUTATIONAL MODELLING ON TUMOR TREATING FIELDS (TTFIELDS) THERAPY

Neuro-Oncology ◽

10.1093/neuonc/noaa215.678 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii162-ii162

Author(s):

Oshrit Zeevi ◽

Zeev Bomzon ◽

Tal Marciano

Keyword(s):

Electric Field ◽

Treatment Planning ◽

Computational Models ◽

Computational Modelling ◽

Clinical Trial Data ◽

Patient Specific ◽

Local Perturbation ◽

Tumor Bed ◽

Tumor Treating Fields ◽

Local Perturbations

Abstract INTRODUCTION Tumor Treating Fields (TTFields) are an approved therapy for glioblastoma (GBM). A recent study combining post-hoc analysis of clinical trial data and extensive computational modelling demonstrated that TTFields dose at the tumor has a direct impact on patient survival (Ballo MT, et al. Int J Radiat Oncol Biol Phys, 2019). Hence, there is rationale for developing TTFields treatment planning tools that rely on numerical simulations and patient-specific computational models. To assist in the development of such tools is it important to understand how inaccuracies in the computational models influence the estimation of the TTFields dose delivered to the tumor bed. Here we analyze the effect of local perturbations in patient-specific head models on TTFields dose at the tumor bed. METHODS Finite element models of human heads with tumor were created. To create defects in the models, conductive spheres with varying conductivities and radii were placed into the model’s brains at different distances from the tumor. Virtual transducer arrays were placed on the models, and delivery of TTFields numerically simulated. The error in the electric field induced by the defects as a function of defect conductivity, radius, and distance to tumor was investigated. RESULTS Simulations showed that when a defect of radius R is placed at a distance, d >7R, the error is below 1% regardless of the defect conductivity. Further the defects induced errors in the electric field that were below 1% when σrR/d < 0.16, where σrR/d < 0.16, where σr = (σsphere – σsurrounding)/(σsphere + σsurrounding).σsurroundings is the average conductivity around the sphere and σsphere is the conductivity of the sphere. CONCLUSIONS This study demonstrates the limited impact of local perturbations in the model on the calculated field distribution. These results could be used as guidelines on required model accuracy for TTFields treatment planning.

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P11.21 Treating numerous secondary tumors in the brain with tumor treating fields: Simulation study to identify optimal transducer array layout

Neuro-Oncology ◽

10.1093/neuonc/noz126.167 ◽

2019 ◽

Vol 21 (Supplement_3) ◽

pp. iii47-iii47

Author(s):

A Kinzel ◽

O Yesharim ◽

A Naveh ◽

Z Bomzon

Keyword(s):

Brain Metastases ◽

Electric Fields ◽

Phase Iii ◽

Homogeneous Distribution ◽

Head Model ◽

Transducer Array ◽

Tumor Treating Fields ◽

Entire Brain ◽

Transducer Arrays ◽

The Brain

Abstract BACKGROUND Tumor Treating Fields (TTFields) use alternating electric fields for the treatment of solid tumors. The therapy is approved for glioblastoma multiforme (GBM), and a phase III trial in 1–10 brain metastases from non-small cell lung cancer (METIS) is currently enrolling patients. In GBM, the layout of the transducer arrays delivering the TTFields to the tumor is optimized for high field intensity in the tumor, while the dose in other regions is decreased. In the setting of secondary brain tumors, as they manifest as brain metastases in 10–30 % of adult cancer patients - especially in melanoma, lung, breast, colon, and kidney cancer - a high TTFields dose in the entire brain would be beneficial. Thus, numerous tumors instead of only one lesion should receive therapeutic TTFields doses. In this study, transducer array layouts aiming for a homogeneous TTFields distribution in the whole brain were investigated. MATERIAL AND METHODS We used computer simulations in a realistic computational head model of a 40+ years old man, constructed in-house from a T1 MRI series, to compute the field distributions obtained with various transducer array layouts. The distribution of TTFields delivered by pairs of transducer arrays at different positions on the head and neck was simulated using Sim4Life v3.0 (ZMT Zürich). For each layout, we determined and compared the mean and median field intensities in five pre-determined sections of the brain: (1) the cerebellum and brain stem together with other infra-tentorial anatomical regions; and (2–5) the four cerebral quadrants. RESULTS One array layout could be identified yielding median intensities between 1.5 V/cm to 1.7 V/cm in all areas and a homogeneous distribution within the brain. This layout is composed of one pair of arrays positioned on the right temple and left scapula, and the other pair positioned on the left temple and right scapula. CONCLUSION This study was able to determine a novel TTFields transducer array layout that might be used for treatment of the entire brain with therapeutic intensities, as would be beneficial in patients with brain metastases.

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Tumor-Treating Fields at EMBC 2019: A Roadmap to Developing a Framework for TTFields Dosimetry and Treatment Planning

Brain and Human Body Modeling 2020 ◽

10.1007/978-3-030-45623-8_1 ◽

2020 ◽

pp. 3-17

Author(s):

Ze’ev Bomzon ◽

Cornelia Wenger ◽

Martin Proescholdt ◽

Suyash Mohan

Keyword(s):

Patient Outcome ◽

Treatment Planning ◽

Electric Fields ◽

Dose Distribution ◽

Pleural Mesothelioma ◽

Adaptive Planning ◽

Tumor Bed ◽

Short Overview ◽

Tumor Treating Fields ◽

Rigorous Treatment

AbstractTumor Treating Fields (TTFields) are electric fields known to exert an anti-mitotic effect on cancerous tumors. TTFields have been approved for the treatment of glioblastoma and malignant pleural mesothelioma. Recent studies have shown a correlation between TTFields doses delivered to the tumor bed and patient survival. These findings suggest that patient outcome could be significantly improved with rigorous treatment planning, in which numerical simulations are used to plan treatment in order to optimize delivery of TTFields to the tumor bed.Performing such adaptive planning in a practical and meaningful manner requires a rigorous and scientifically proven framework defining TTFields dose and showing how dose distribution influences disease progression in different malignancies (TTFields dosimetry). At EMBC 2019, several talks discussing key components related to TTFields dosimetry and treatment planning were presented. Here we provide a short overview of this work and discuss how it sets the foundations for the emerging field of TTFields dosimetry and treatment planning.

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Impact of Number of Segmented Tissues on SAR Prediction Accuracy in Deep Pelvic Hyperthermia Treatment Planning

Cancers ◽

10.3390/cancers12092646 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2646

Author(s):

Iva VilasBoas-Ribeiro ◽

Gerard C. van Rhoon ◽

Tomas Drizdal ◽

Martine Franckena ◽

Margarethus M. Paulides

Keyword(s):

Treatment Planning ◽

High Water ◽

Specific Model ◽

Treatment Quality ◽

Patient Specific ◽

High Water Content ◽

Treatment Optimization ◽

Hyperthermia Treatment ◽

Pre Treatment ◽

The Impact

In hyperthermia, the general opinion is that pre-treatment optimization of treatment settings requires a patient-specific model. For deep pelvic hyperthermia treatment planning (HTP), tissue models comprising four tissue categories are currently discriminated. For head and neck HTP, we found that more tissues are required for increasing accuracy. In this work, we evaluated the impact of the number of segmented tissues on the predicted specific absorption rate (SAR) for the pelvic region. Highly detailed anatomical models of five healthy volunteers were selected from a virtual database. For each model, seven lists with varying levels of segmentation detail were defined and used as an input for a modeling study. SAR changes were quantified using the change in target-to-hotspot-quotient and maximum SAR relative differences, with respect to the most detailed patient model. The main finding of this study was that the inclusion of high water content tissues in the segmentation may result in a clinically relevant impact on the SAR distribution and on the predicted hyperthermia treatment quality when considering our pre-established thresholds. In general, our results underline the current clinical segmentation protocol and help to prioritize any improvements.

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Optimizing Transducer Array Layout for the Treatment of Pancreatic Cancer Using Tumor Treating Fields (TTFields) in the Phase 3 Panova-3 Trial

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2019.06.2481 ◽

2019 ◽

Vol 105 (1) ◽

pp. E796

Author(s):

A. Naveh ◽

H.S. Hershkovich ◽

Z. Bomzon ◽

U. Weinberg ◽

E. Kirson

Keyword(s):

Pancreatic Cancer ◽

Phase 3 ◽

Transducer Array ◽

Tumor Treating Fields

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Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

Communications Biology ◽

10.1038/s42003-020-01638-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Gaoyang Li ◽

Haoran Wang ◽

Mingzi Zhang ◽

Simon Tupin ◽

Aike Qiao ◽

...

Keyword(s):

Deep Learning ◽

Artery Bypass ◽

Computational Cost ◽

Specific Model ◽

Arterial System ◽

Patient Specific ◽

Cardiovascular Hemodynamics ◽

Before And After ◽

Deep Learning Network ◽

Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

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