AI Article Synopsis

  • - The study investigates how different Dose Voxel Kernels (DVKs) affect absorbed dose (AD) maps in radiopharmaceutical therapy (RPT), aiming to standardize treatment and improve cancer outcomes.
  • - Researchers analyzed nine DVKs each for Lutetium (Lu) and Yttrium (Y), calculating variations in absorbed doses using the same time-integrated activity (TIA) maps and software for different patients' treatment cases.
  • - The results showed notable differences in the coefficient of variation and maximum percentage differences in absorbed doses, indicating that the choice of DVK significantly impacts dosimetry calculations in clinical settings.

Article Abstract

Background: Radiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time-integrated activity (TIA) distribution has not been systematically investigated.

Purpose: This study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs.

Methods: DVKs of 3 × 3 × 3 mm sampling-nine for Lu, nine for Y-were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with Lu-DOTATATE and PET images for two patients treated with Y-microspheres were used, including organs at risk ( Lu: kidneys; Y: liver and healthy liver) and tumors' segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed.

Results: The CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the Lu DVKs. For the case of Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for Lu, and 4%(15%) for Y. The variability of the mean tumor and organ AD was up to 19% and 15% in Lu-DOTATATE and Y-microspheres patients, respectively.

Conclusions: This study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10843484PMC
http://dx.doi.org/10.1002/mp.16729DOI Listing

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