A deep‐learning‐based prediction model for the biodistribution of 90Y microspheres in liver radioembolization. Issue 11 (21st October 2021)

• Record Type: Journal Article
• Authors: Plachouris, Dimitris; Tzolas, Ioannis; Gatos, Ilias; Papadimitroulas, Panagiotis; Spyridonidis, Trifon; Apostolopoulos, Dimitris; Papathanasiou, Nikolaos; Visvikis, Dimitris; Plachouri, Kerasia‐Maria; Hazle, John D.; Kagadis, George C.
• Is Part Of: Medical physics. Volume 48:Issue 11(2021)
• Page Start: 7427
• Page End: 7438
• Publication Date: 2021-10-21
• Subjects: biodistribution prediction -- deep learning -- radioembolization -- treatment planning -- yttrium‐90; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15270
• Languages: English
• ISSNs: 0094-2405

Abstract

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.