Radiotherapy class-solution to correct an energy-dependent optically stimulated luminescence film dosimeter

Background

Patient-Specific Quality Assurance in Radiotherapy (PSQA) demands high-resolution dosimetry to verify accurate dose delivery in personalized intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) treatments. A novel optically stimulated luminescence (OSL) film dosimeter made with BaFBr:Eu²⁺ phosphor, offers submm spatial resolution. However, its energy-dependent response, requires corrections. Previously, a correction was proposed for a class of prostate cancer treatments assuming similar OSL energy response within the class.

Purpose

This study explored other class-specific corrections using a comprehensive radiotherapy treatment dataset. New classes were formed based on the similarity of treatment parameters without the need for user-based classifications.

Methods

The dataset comprised 101 IMRT and VMAT treatment plans for three different Varian linac types (2 × Halcyon, 2 × TrueBeam, and 1 × TrueBeam STx). The treatment classes are based on a K-means clustering algorithm, that utilizes twelve quantitative treatment parameters expressed in principal components. Within cluster sum square (WCSS) was used to find the optimal number of classes and prevent data-overfitting. This objective assignment to classes was compared with three independent manual classifications by experienced medical physicists and dosimetrist. Additionally, a random class assignment was conducted for comparison. The adjusted-random-index (ARI) measured the similarity between classification methods. The OSL film, produced by Agfa N.V., was calibrated using a 6 MV TrueBeam linac. It was then used to measure treatments in an MULTICube phantom (IBA). Readout was performed in a CR-15 scanner. The local dose difference distribution between the measurement and treatment was characterized using a rational function. Class-specific corrections were developed by averaging the parameters of the rational function for each class as determined by the clustering, manual, and random classification methods. Dosimetric performances were evaluated within 20% and 50% isodose lines (D20% and D50%) before and after correction.

Results

The clustering method identified eight clusters (WCSS = 119 silhouette = 0.6) when representing data in three principal components, that is, 75% of the data variance. No significant similarity was found between clustering results and manual classification methods (ARI < 0.01). Manual classifications are subject to interoperator variability. In fact, we found moderate similarity between classes and variations in the number of classes, ranging from 9 to 16. Uncorrected global dose difference (%) had mean value 0.9% ± 4.1% within D20%, with 47 and 34 treatments resulting in dose difference below 3% within D20% and D50%, respectively. After class-specific correction, the clustering method had mean dose differences (%) −0.2% ± 2.0%. The removal of the skewness in the corrected pixel-to-pixel dose difference distribution indicated an effective reduction of the OSL over-response. 88 and 74 treatments had corrected mean dose difference below 3% within D20% and D50%, respectively. Similar average dosimetric improvements were found only for the 16 manual class-solution, which however still showed a moderate skewness (0.1) after correction. Both, automated and manual class assignment preform better than the random assignment.

Conclusions

Eight treatment class-solutions corrected the energy-dependent response of an OSL film used for PSQA measurements. Clustering classification methods, based on quantitative treatment information, yielded better dosimetric results compared to qualitative classification techniques.