AutoSimTTF: A Fully Automatic Pipeline for Electric Field Simulation and Treatment Planning of Tumor Treating Fields
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909351616184320 |
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| author | Wang, Minmin Xie, Xu Fan, Zhengbo Lan, Yue Pan, Yun Chen, Guangdi Zhang, Shaomin Wang, Yuxing |
| author_facet | Wang, Minmin Xie, Xu Fan, Zhengbo Lan, Yue Pan, Yun Chen, Guangdi Zhang, Shaomin Wang, Yuxing |
| contents | Objective: Tumor Treating Fields (TTFields) is an emerging approach for cancer therapy that inhibits tumor cell proliferation by applying alternating electric fields (EF) of intermediate frequency and low intensity. The TTFields-induced electric field intensity at the tumor site is closely related to the therapeutic efficacy. Therefore, the EF simulation based on realistic head models have been utilized for the dosage analysis and treatment optimization of TTFields. However, current modeling methods require manual segmentation of tumors and rely on commercial software, which is time-consuming and labor-intensive. Approach: We introduce AutoSimTTF, a fully automatic pipeline for simulating and optimizing the EF distribution for TTFields. The main steps of AutoSimTTF utilize open-source toolkits, enabling fully automated processing of individual MRI data for TTFields. Additionally, AutoSimTTF allows for parameter optimization based on individual anatomical information, thereby achieving a more focused and higher EF distribution at the tumor site. Main results: Compared to conventional EF calculation processes, deviations in AutoSimTTF are below 20%. The optimal treatment parameters generated by AutoSimTTF produces a higher EF intensity at the tumor site (111.9%) and better focality (19.4%) compared to traditional TTFields settings. Significance: AutoSimTTF provides significant reference value and guidance for the clinical application and treatment planning of TTFields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoSimTTF: A Fully Automatic Pipeline for Electric Field Simulation and Treatment Planning of Tumor Treating Fields Wang, Minmin Xie, Xu Fan, Zhengbo Lan, Yue Pan, Yun Chen, Guangdi Zhang, Shaomin Wang, Yuxing Medical Physics Objective: Tumor Treating Fields (TTFields) is an emerging approach for cancer therapy that inhibits tumor cell proliferation by applying alternating electric fields (EF) of intermediate frequency and low intensity. The TTFields-induced electric field intensity at the tumor site is closely related to the therapeutic efficacy. Therefore, the EF simulation based on realistic head models have been utilized for the dosage analysis and treatment optimization of TTFields. However, current modeling methods require manual segmentation of tumors and rely on commercial software, which is time-consuming and labor-intensive. Approach: We introduce AutoSimTTF, a fully automatic pipeline for simulating and optimizing the EF distribution for TTFields. The main steps of AutoSimTTF utilize open-source toolkits, enabling fully automated processing of individual MRI data for TTFields. Additionally, AutoSimTTF allows for parameter optimization based on individual anatomical information, thereby achieving a more focused and higher EF distribution at the tumor site. Main results: Compared to conventional EF calculation processes, deviations in AutoSimTTF are below 20%. The optimal treatment parameters generated by AutoSimTTF produces a higher EF intensity at the tumor site (111.9%) and better focality (19.4%) compared to traditional TTFields settings. Significance: AutoSimTTF provides significant reference value and guidance for the clinical application and treatment planning of TTFields. |
| title | AutoSimTTF: A Fully Automatic Pipeline for Electric Field Simulation and Treatment Planning of Tumor Treating Fields |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2410.12196 |