Inverse planning for radiofrequency ablation in cancer therapy using multiple damage models

Radiofrequency ablation (RFA) offers localized and minimally invasive treatment of small-to-medium sized inoperable tumors. In RFA, tissue is ablated with high temperatures obtained from electrodes (needles) inserted percutaneously or via an open surgery into the target. RFA treatments are generally not planned in a systematic way, and do not account for nearby organs-at-risk (OARs), potentially leading to sub-optimal treatments and inconsistent treatment quality. We therefore develop a mathematical framework to design RFA treatment plans that provide complete ablation while minimizing healthy tissue damage. Borrowing techniques from radiosurgery inverse planning, we design a two-stage approach where we first identify needle positions and orientations, called needle orientation optimization, and then compute the treatment time for optimal thermal dose delivery, called thermal dose optimization. Several different damage models are used to determine both target and OAR damage. We present numerical results on three clinical case studies. Our findings indicate a need for high source voltage for short tip length (conducting portion of the needle) or fewer needles, and low source voltage for long tip length or more needles to achieve full coverage. Further, more needles yields a larger ablation volume and consequently more OAR damage. Finally, the choice of damage model impacts the source voltage, tip length, and needle quantity.

A Novel Fuzzy Logic Guided Method for Automatic gEUD-based Inverse Treatment Planning

Generalized equivalent uniform dose (gEUD) -based hybrid objective functions are widely used in intensity modulated radiotherapy (IMRT). To improve its efficiency, a novel fuzzy logic guided inverse planning method was developed for the automatic parameters optimization of the gEUD-based radiotherapy optimization. Simple inference rules were formulated according to the knowledge of the treatment planner. Then they automatically and iteratively guide the parameters modification according to the percentage of deviation between the current dose and the prescribed dose. weighting factors and prescribed dose were automatically adjusted by developed fuzzy inference system (FIS). The performance of the FIS was tested on ten prostate cancer cases. Experimental results indicate that proposed automatic method can yield comparable or better plans than manual method. The fuzzy logic guided automatic inverse planning method of parameters optimization can significantly improve the efficiency of the method of manually adjusting parameters, and contributes to the development of fully automated planning.