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Mendeley readers
Attention Score in Context
| Title |
An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images
|
|---|---|
| Published in |
BMC Medical Imaging, February 2017
|
| DOI | 10.1186/s12880-017-0181-0 |
| Pubmed ID | |
| Authors |
Mariana A. Nogueira, Pedro H. Abreu, Pedro Martins, Penousal Machado, Hugo Duarte, João Santos |
| Abstract |
Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 18% |
| Student > Master | 6 | 14% |
| Other | 5 | 11% |
| Researcher | 5 | 11% |
| Student > Bachelor | 3 | 7% |
| Other | 3 | 7% |
| Unknown | 14 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 32% |
| Computer Science | 6 | 14% |
| Engineering | 2 | 5% |
| Neuroscience | 2 | 5% |
| Psychology | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 17 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 June 2019.
All research outputs
#7,288,129
of 22,990,068 outputs
Outputs from BMC Medical Imaging
#99
of 605 outputs
Outputs of similar age
#141,669
of 426,917 outputs
Outputs of similar age from BMC Medical Imaging
#2
of 10 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 605 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done well, scoring higher than 82% 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 426,917 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.