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

Dimitris Plachouris, Ioannis Tzolas, Ilias Gatos, Panagiotis Papadimitroulas, Trifon Spyridonidis, Dimitris Apostolopoulos, Nikolaos Papathanasiou, Dimitris Visvikis, Kerasia Maria Plachouri, John D. Hazle, George C. Kagadis

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background: Radioembolization with 90Y 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 99mTc-macroaggregated albumin on SPECT/CT and post-treatment distribution of 90Y microspheres on PET/CT and to accurately predict how the 90Y-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 90Y 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 90Y microsphere biodistribution. Its combination with a rapid and accurate 3D dosimetry method will render it clinically suitable and could improve patient-specific pretreatment planning.

Original languageEnglish (US)
Pages (from-to)7427-7438
Number of pages12
JournalMedical physics
Volume48
Issue number11
DOIs
StatePublished - Nov 2021

Keywords

  • biodistribution prediction
  • deep learning
  • radioembolization
  • treatment planning
  • yttrium-90

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'A deep-learning-based prediction model for the biodistribution of 90Y microspheres in liver radioembolization'. Together they form a unique fingerprint.

Cite this