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- W4281486044 abstract "To achieve a result of a large tumor ablation volume with minimal thermal damage to the surrounding blood vessels by designing a few clinically-adjustable operating parameters in radiofrequency ablation (RFA) for liver tumors abutting complex vascular structures.Response surface method (RSM) was employed to correlate the ablated tumor volume (Ra) and thermal damage to blood vessels (Dt) based on RFA operating parameters: ablation time, electrode position, and insertion angle. A coupled electric-thermal-fluid RFA computer model was created as the testbed for RSM to simulate RFA process. Then, an optimal RFA protocol for the two conflicting goals, namely (1) large tumor ablation and (2) small thermal damage to the surrounding blood vessels, has been achieved under a specific ablation environment.Linear regression analysis confirmed that the RFA protocol significantly affected Ra and Dt (the adjusted coefficient of determination Radj2 = 93.61% and 95.03%, respectively). For a proposed liver tumor scenario (liver tumor with a dimension of 4×3×2.9 cm3 abutting a complex vascular structure), an optimized RFA protocol was found based on the regression results in RSM. Compared with a reference RFA protocol, in which the electrode was centered in the tumor with a 12-min ablation time, the optimized RFA protocol has increased Ra from 98.1% to 99.6% and decreased Dt from 4.1% to 0.4%, achieving nearly the complete ablation of proposed liver tumor and ignorable thermal damages to vessels.This work showed that it is possible to design a few clinically-adjustable operating parameters of RFA for achieving a large tumor ablation volume while minimizing thermal damage to the surrounding blood vessels." @default.
- W4281486044 created "2022-05-26" @default.
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- W4281486044 date "2022-05-24" @default.
- W4281486044 modified "2023-10-11" @default.
- W4281486044 title "Radiofrequency ablation for liver tumors abutting complex blood vessel structures: treatment protocol optimization using response surface method and computer modeling" @default.
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- W4281486044 doi "https://doi.org/10.1080/02656736.2022.2075567" @default.
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