Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas.

May SadikErica LindEirini PolymeriOlof EnqvistJohannes UlénElin Trägårdh

Published in: Clinical physiology and functional imaging (2018)

An AI-based method for the automated quantification of reference levels in the liver and mediastinal blood pool shows good agreement with results obtained by experienced radiologists who had manually segmented the CT images. This is a first, promising step towards objective treatment response evaluation in patients with lymphoma based on 18F-FDG-PET/CT.

Keyphrases

deep learning
artificial intelligence
machine learning
lymph node
convolutional neural network
hodgkin lymphoma
high throughput
computed tomography
ultrasound guided
diffuse large b cell lymphoma
image quality
stem cells
positron emission tomography
replacement therapy