Response-probability volume histograms and iso-probability of response charts in treatment plan evaluation.

PURPOSE: This study aims at demonstrating a new method for treatment plan evaluation and comparison based on the radiobiological response of individual voxels. This is performed by applying them on three different cancer types and treatment plans of different conformalities. Furthermore, their usefulness is examined in conjunction with traditionally applied radiobiological and dosimetric treatment plan evaluation criteria.

METHODS: Three different cancer types (head and neck, breast and prostate) were selected to quantify the benefits of the proposed treatment plan evaluation method. In each case, conventional conformal radiotherapy (CRT) and intensity modulated radiotherapy (IMRT) treatment configurations were planned. Iso-probability of response charts was produced by calculating the response probability in every voxel using the linear-quadratic-Poisson model and the dose-response parameters of the corresponding structure to which this voxel belongs. The overall probabilities of target and normal tissue responses were calculated using the Poisson and the relative seriality models, respectively. The 3D dose distribution converted to a 2 Gy fractionation, D2(GY) and iso-BED distributions are also shown and compared with the proposed methodology. Response-probability volume histograms (RVH) were derived and compared with common dose volume histograms (DVH). The different dose distributions were also compared using the complication-free tumor control probability, P+, the biologically effective uniform dose, D, and common dosimetric criteria.

RESULTS: 3D Iso-probability of response distributions is very useful for plan evaluation since their visual information focuses on the doses that are likely to have a larger clinical effect in that particular organ. The graphical display becomes independent of the prescription dose highlighting the local radiation therapy effect in each voxel without the loss of important spatial information. For example, due to the exponential nature of the Poisson distribution, cold spots in the target volumes or hot spots in the normal tissues are much easier to be identified. Response-volume histograms, as DVH, can also be derived and used for plan comparison. RVH are advantageous since by incorporating the radiobiological properties of each voxel they summarize the 3D distribution into 2D without the loss of relevant information. Thus, more clinically relevant radiobiological objectives and constraints could be defined and used in treatment planning optimization. These measures become increasingly important when dose distributions need to be designed according to the microscopic biological properties of tumor and normal tissues.

CONCLUSIONS: The proposed methods do not aim to replace quantifiers like the probabilities of total tissue response, which ultimately are the quantities of interest to evaluate treatment success. However, iso-probability of response charts and response-probability volume histograms illustrates more clearly the difference in effectiveness between different treatment plans than the information provided by alternative dosimetric data. The use of 3D iso-probability of response distributions could serve as a good descriptor of the effectiveness of a dose distribution indicating primarily the regions in a tissue that dominate its response.