A novel conformity index for intensity modulated radiation therapy plan evaluation

Intensity modulated radiation therapy (IMRT) has gained popularity in the treatment of cancers. Manual evaluation of IMRT plans for head-and-neck cancers has been especially challenging necessitating efficient and objective assessment tools. In this work, the authors address this issue by developing a personalized conformity index (CI) for comparison of IMRT plans for head-and-neck cancers and evaluating its plan quality discerning power in comparison with other widely used CIs.

A two-dimensional CI with dose and distance incorporated (CI(DD)) was developed using the MATLAB program language, to quantify the planning target volume (PTV) coverage. Valuable information contained in the digital imaging and communication in medicine (DICOM) RT objects were harvested for computation of each of the CI(DD) components. Apart from the dose penalty factor, a distance-based exponential function was employed by varying the penalty weight associated with the location of cold spots within the PTV. With the goal of deriving a customized penalty factor, the distances between individual pixel and its nearest PTV boundary was found. Using the exponential function, the impact of distance penalty was substantially larger for cold spots closer to the PTV centroid but petered out quickly wherever they were situated in the vicinity of PTV border. In order to evaluate the CI(DD) scoring system, three CT image data sets of nasopharyngeal carcinoma (NPC) patients were collected. Ten IMRT plans with degrading qualities were generated from each dataset and were ranked based on CI(DD) and other existing indices. The coefficient of variance was calculated for each dataset to compare the degree of variation.

The CI(DD) scoring system that considered spatial importance of each voxel within the PTV was successfully developed. The results demonstrated that the CI(DD) including four discrete factors could provide accurate rankings of plan quality by examining the relative importance of each cold spot within the PTVs. Apart from the dose penalty factor, a distance-based exponential function was employed taking the specific tumor geometry into account. Compared with other commonly used CIs, the CI(DD) resulted in the largest coefficient of variance among the ten IMRT plans for each dataset, indicating that its discerning power was the best among the CIs being compared.

The CI(DD) scoring system was successfully developed to incorporate patient-specific spatial dose information and provide a geometry-based physical index for comparison of IMRT plans for head-and-neck cancers. By taking individual tumor geometry into account, the superiority of CI(DD) in plan discerning power was demonstrated. The use of CI(DD) could provide an effective means of benchmarking performance, reducing treatment plan variability, and advancing the quality of current IMRT planning.