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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

Sadik, May; Lind, Erica; Polymeri, Eirini; Enqvist, Olof; Ulén, Johannes LU and Trägårdh, Elin LU (2018) In Clinical Physiology and Functional Imaging
Abstract

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: The AI-based method was trained to segment the liver and the mediastinal blood pool in CT images from 80 lymphoma patients, who had... (More)

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: 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. Results: The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0·02 and 0·02, respectively, and 0·02 and 0·02 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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author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
artificial intelligence, convolutional neural network, objective, segmentation
in
Clinical Physiology and Functional Imaging
publisher
Wiley Online Library
external identifiers
  • scopus:85054407151
ISSN
1475-0961
DOI
10.1111/cpf.12546
language
English
LU publication?
yes
id
0df0e693-523e-4686-86c1-b93e0a16307f
date added to LUP
2018-11-08 12:46:04
date last changed
2018-11-09 03:00:05
@article{0df0e693-523e-4686-86c1-b93e0a16307f,
  abstract     = {<p>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: 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. Results: The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0·02 and 0·02, respectively, and 0·02 and 0·02 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.</p>},
  author       = {Sadik, May and Lind, Erica and Polymeri, Eirini and Enqvist, Olof and Ulén, Johannes and Trägårdh, Elin},
  issn         = {1475-0961},
  keyword      = {artificial intelligence,convolutional neural network,objective,segmentation},
  language     = {eng},
  month        = {10},
  publisher    = {Wiley Online Library},
  series       = {Clinical Physiology and Functional Imaging},
  title        = {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},
  url          = {http://dx.doi.org/10.1111/cpf.12546},
  year         = {2018},
}