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Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods

Overview of attention for article published in EJNMMI Research, April 2018
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Title
Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods
Published in
EJNMMI Research, April 2018
DOI 10.1186/s13550-018-0379-3
Pubmed ID
Authors

Craig Parkinson, Kieran Foley, Philip Whybra, Robert Hills, Ashley Roberts, Chris Marshall, John Staffurth, Emiliano Spezi

Abstract

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 16%
Other 3 12%
Researcher 3 12%
Student > Doctoral Student 2 8%
Student > Master 2 8%
Other 3 12%
Unknown 8 32%
Readers by discipline Count As %
Medicine and Dentistry 7 28%
Engineering 3 12%
Computer Science 3 12%
Nursing and Health Professions 1 4%
Social Sciences 1 4%
Other 1 4%
Unknown 9 36%
Attention Score in Context

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 13 May 2018.
All research outputs
#13,905,391
of 23,051,185 outputs
Outputs from EJNMMI Research
#202
of 564 outputs
Outputs of similar age
#176,427
of 329,170 outputs
Outputs of similar age from EJNMMI Research
#8
of 12 outputs
Altmetric has tracked 23,051,185 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 63% 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 329,170 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.