Author:
Theysohn Jens M.,Demircioglu Aydin,Kleditzsch Malte,Ludwig Johannes M.,Weber Manuel,Umutlu Lale,Li Yan,Kircher Malte,Lapa Constantin,Buck Andreas,Koehler Michael,Wildgruber Moritz,Lange Christian M.,Palard Xavier,Garin Etienne,Herrmann Ken,Forsting Michael,Nensa Felix
Abstract
AbstractIn cirrhotic patients with hepatocellular carcinoma (HCC), right-sided radioembolization (RE) with Yttrium-90-loaded microspheres is an established palliative therapy and can be considered a “curative intention” treatment when aiming for sequential tumor resection. To become surgical candidate, hypertrophy of the left liver lobe to > 40% (future liver remnant, FLR) is mandatory, which can develop after RE. The amount of radiation-induced shrinkage of the right lobe and compensatory hypertrophy of the left lobe is difficult for clinicians to predict. This study aimed to utilize machine learning to predict left lobe liver hypertrophy in patients with HCC and cirrhosis scheduled for right lobe RE, with external validation. The results revealed that machine learning can accurately predict relative and absolute volume changes of the left liver lobe after right lobe RE. This prediction algorithm could help to estimate the chances of conversion from palliative RE to curative major hepatectomy following significant FLR hypertrophy.
Funder
Universitätsklinikum Essen
Publisher
Springer Science and Business Media LLC
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