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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

Artikel i vetenskaplig tidskrift
Författare May Sadik
Erica Lind
O. Enqvist
J. Ulen
E. Tragardh
Publicerad i Clinical Physiology and Functional Imaging
Volym 39
Nummer/häfte 1
Sidor 78-84
ISSN 1475-0961
Publiceringsår 2019
Publicerad vid Institutionen för medicin, avdelningen för molekylär och klinisk medicin
Sidor 78-84
Språk en
Länkar dx.doi.org/10.1111/cpf.12546
Ämnesord artificial intelligence, convolutional neural network, objective, segmentation, criteria
Ämneskategorier Radiologi och bildbehandling, Medicinsk bildbehandling

Sammanfattning

Background 18F-FDG-PET/CT has become a standard for assessing treatment response in patients with lymphoma. A subjective interpretation of the scan based on the Deauville 5-point scale has been widely adopted. However, inter-observer variability due to the subjectivity of the interpretation is a limitation. Our main goal is to develop an objective and automated method for evaluating response. The first step is to develop and validate an artificial intelligence (AI)-based method, for the automated quantification of reference levels in the liver and mediastinal blood pool in patients with lymphoma. Methods Results The AI-based method was trained to segment the liver and the mediastinal blood pool in CT images from 80 lymphoma patients, who had undergone 18F-FDG-PET/CT, and apply this to a validation group of six lymphoma patients. CT segmentations were transferred to the PET images to obtain automatic standardized uptake values (SUV). The AI-based analysis was compared to corresponding manual segmentations performed by two radiologists. The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0 center dot 02 and 0 center dot 02, respectively, and 0 center dot 02 and 0 center dot 02 for mediastinal blood pool respectively. Conclusions 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.

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