A deep-learning-based prediction model for the biodistribution of 90 Y microspheres in liver radioembolization

Abstract

Background: Radioembolization with ⁹⁰ Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics.

Purpose: The purpose of the present study was to employ deep learning (DL) algorithms to differentiate patterns of pretreatment distribution of ^99m Tc-macroaggregated albumin on SPECT/CT and post-treatment distribution of ⁹⁰ Y microspheres on PET/CT and to accurately predict how the ⁹⁰ Y-microspheres will be distributed in the liver tissue by radioembolization therapy.

Methods: Data for 19 patients with liver cancer (10 with hepatocellular carcinoma, 5 with intrahepatic cholangiocarcinoma, 4 with liver metastases) who underwent radioembolization with ⁹⁰ Y microspheres were used for the DL training. We developed a 3D voxel-based variation of the Pix2Pix model, which is a special type of conditional GANs designed to perform image-to-image translation. SPECT and CT scans along with the clinical target volume for each patient were used as inputs, as were their corresponding post-treatment PET scans. The real and predicted absorbed PET doses for the tumor and the whole liver area were compared. Our model was evaluated using the leave-one-out method, and the dose calculations were measured using a tissue-specific dose voxel kernel.

Results: The comparison of the real and predicted PET/CT scans showed an average absorbed dose difference of 5.42% ± 19.31% and 0.44% ± 1.64% for the tumor and the liver area, respectively. The average absorbed dose differences were 7.98 ± 31.39 Gy and 0.03 ± 0.25 Gy for the tumor and the non-tumor liver parenchyma, respectively. Our model had a general tendency to underpredict the dosimetric results; the largest differences were noticed in one case, where the model underestimated the dose to the tumor area by 56.75% or 72.82 Gy.

Conclusions: The proposed deep-learning-based pretreatment planning method for liver radioembolization accurately predicted ⁹⁰ Y microsphere biodistribution. Its combination with a rapid and accurate 3D dosimetry method will render it clinically suitable and could improve patient-specific pretreatment planning.

Keywords: biodistribution prediction; deep learning; radioembolization; treatment planning; yttrium-90.