Predictive value of 99mTc-MAA-based dosimetry in personalized 90Y-SIRT planning for liver malignancies

Abstract

Background Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that the pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation of the 90Y microspheres biodistribution, which is not always the case. Due to the growing interest in theragnostic dosimetry for personalized radionuclide therapy, a robust relationship between the delivered and pre-treatment radiation absorbed doses is required. In this work, we aim to investigate the predictive value of absorbed dose metrics calculated from 99mTc-MAA (simulation) compared to those obtained from 90Y post-therapy SPECT/CT. Results A total of 79 patients were analysed. Pre- and post-therapy 3D-voxel dosimetry was calculated on 99mTc-MAA and 90Y SPECT/CT, respectively, based on Local Deposition Method. Mean absorbed dose, tumour-to-normal ratio, and absorbed dose distribution in terms of dose-volume histogram (DVH) metrics were obtained and compared for each volume of interest (VOI). Mann–Whitney U-test and Pearson’s correlation coefficient were used to assess the correlation between both methods. The effect of the tumoral liver volume on the absorbed dose metrics was also investigated. Strong correlation was found between simulation and therapy mean absorbed doses for all VOIs, although simulation tended to overestimate tumour absorbed doses by 26%. DVH metrics showed good correlation too, but significant differences were found for several metrics, mostly on non-tumoral liver. It was observed that the tumoral liver volume does not significantly affect the differences between simulation and therapy absorbed dose metrics. Conclusion This study supports the strong correlation between absorbed dose metrics from simulation and therapy dosimetry based on 90Y SPECT/CT, highlighting the predictive ability of 99mTc-MAA, not only in terms of mean absorbed dose but also of the dose distribution.

Are survival outcomes dependent on the tumour dose threshold of 139 Gy in patients with chemorefractory metastatic colorectal cancer treated with yttrium-90 radioembolization using glass particles? A real-world single-centre study

“…However, recent papers supported the current study’s dosimetry planning method and revealed that pre-treatment MAA is strongly correlated with the post-90Y PET, in terms of the mean absorbed dose and also the dose distribution. 22 , 23 Fourthly, the administered sphere count as dose/sphere was not calculated for each patient. The availability of flexible dose shipping options may have resulted in different dose/sphere values for different administrations, resulting in varying sphere density and dose uniformity/heterogeneity.…”

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning

“…For instance, the management of patients treated with selective internal radiation therapy (SIRT) for the treatment of liver malignancies requires CE-CT for the delineation of the tumoral tissues, perfused liver lobe and organs at risk towards personalized dosimetry. 3 Artificial intelligence (AI) and particularly deep learning has shown very promising performance in multiple tasks, including image segmentation, [4][5][6] image generation, 7,8 dosimetry, [9][10][11] and classification. 12,13 However, the number of clean and reliable data available is still the bottle neck for generalizability and robustness of deep learning models.…”