TY - JOUR
T1 - A deep-learning-based prediction model for the biodistribution of 90Y microspheres in liver radioembolization
AU - Plachouris, Dimitris
AU - Tzolas, Ioannis
AU - Gatos, Ilias
AU - Papadimitroulas, Panagiotis
AU - Spyridonidis, Trifon
AU - Apostolopoulos, Dimitris
AU - Papathanasiou, Nikolaos
AU - Visvikis, Dimitris
AU - Plachouri, Kerasia Maria
AU - Hazle, John D.
AU - Kagadis, George C.
N1 - Publisher Copyright:
© 2021 American Association of Physicists in Medicine
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - biodistribution prediction
KW - deep learning
KW - radioembolization
KW - treatment planning
KW - yttrium-90
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U2 - 10.1002/mp.15270
DO - 10.1002/mp.15270
M3 - Article
C2 - 34628667
AN - SCOPUS:85117399845
SN - 0094-2405
VL - 48
SP - 7427
EP - 7438
JO - Medical physics
JF - Medical physics
IS - 11
ER -