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Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer

Overview of attention for article published in EJNMMI Physics, February 2022
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Title
Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer
Published in
EJNMMI Physics, February 2022
DOI 10.1186/s40658-022-00437-3
Pubmed ID
Authors

Pablo Borrelli, José Luis Loaiza Góngora, Reza Kaboteh, Johannes Ulén, Olof Enqvist, Elin Trägårdh, Lars Edenbrandt

Abstract

Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data. We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [18F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer. A secondary aim was to make the method available to other researchers. A total of 320 consecutive patients referred for FDG PET/CT due to suspected lung cancer were retrospectively selected for this study. Two nuclear medicine specialists manually segmented abnormal FDG uptake in all of the PET/CT studies. One-third of the patients were assigned to a test group. Survival data were collected for this group. The CNN was trained to segment lung tumours and thoracic lymph nodes. Total lesion glycolysis (TLG) was calculated from the CNN-based and manual segmentations. Associations between TLG and OS were investigated using a univariate Cox proportional hazards regression model. The test group comprised 106 patients (median age, 76 years (IQR 61-79); n = 59 female). Both CNN-based TLG (hazard ratio 1.64, 95% confidence interval 1.21-2.21; p = 0.001) and manual TLG (hazard ratio 1.54, 95% confidence interval 1.14-2.07; p = 0.004) estimations were significantly associated with OS. Fully automated CNN-based TLG measurements of PET/CT data showed were significantly associated with OS in patients with lung cancer. This type of measurement may be of value for the management of future patients with lung cancer. The CNN is publicly available for research purposes.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Lecturer > Senior Lecturer 1 8%
Other 1 8%
Student > Master 1 8%
Researcher 1 8%
Other 1 8%
Unknown 5 42%
Readers by discipline Count As %
Computer Science 2 17%
Medicine and Dentistry 2 17%
Economics, Econometrics and Finance 1 8%
Chemistry 1 8%
Engineering 1 8%
Other 0 0%
Unknown 5 42%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2022.
All research outputs
#14,109,655
of 21,724,962 outputs
Outputs from EJNMMI Physics
#57
of 160 outputs
Outputs of similar age
#212,347
of 401,159 outputs
Outputs of similar age from EJNMMI Physics
#1
of 1 outputs
Altmetric has tracked 21,724,962 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 160 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 401,159 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them