| Title |
Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer
|
|---|---|
| Published in |
Annals of Nuclear Medicine, September 2017
|
| DOI | 10.1007/s12149-017-1203-2 |
| Pubmed ID | |
| Authors |
Ana María Garcia-Vicente, David Molina, Julián Pérez-Beteta, Mariano Amo-Salas, Alicia Martínez-González, Gloria Bueno, María Jesús Tello-Galán, Ángel Soriano-Castrejón |
| Abstract |
To study the influence of dual time point 18F-FDG PET/CT in textural features and SUV-based variables and their relation among them. Fifty-six patients with locally advanced breast cancer (LABC) were prospectively included. All of them underwent a standard 18F-FDG PET/CT (PET-1) and a delayed acquisition (PET-2). After segmentation, SUV variables (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Eighteen three-dimensional (3D) textural measures were computed including: run-length matrices (RLM) features, co-occurrence matrices (CM) features, and energies. Differences between all PET-derived variables obtained in PET-1 and PET-2 were studied. Significant differences were found between the SUV-based parameters and MTV obtained in the dual time point PET/CT, with higher values of SUV-based variables and lower MTV in the PET-2 with respect to the PET-1. In relation with the textural parameters obtained in dual time point acquisition, significant differences were found for the short run emphasis, low gray-level run emphasis, short run high gray-level emphasis, run percentage, long run emphasis, gray-level non-uniformity, homogeneity, and dissimilarity. Textural variables showed relations with MTV and TLG. Significant differences of textural features were found in dual time point 18F-FDG PET/CT. Thus, a dynamic behavior of metabolic characteristics should be expected, with higher heterogeneity in delayed PET acquisition compared with the standard PET. A greater heterogeneity was found in bigger tumors. |
X Demographics
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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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 5 | 16% |
| Researcher | 4 | 13% |
| Other | 3 | 10% |
| Student > Doctoral Student | 3 | 10% |
| Lecturer > Senior Lecturer | 3 | 10% |
| Other | 7 | 23% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 42% |
| Mathematics | 2 | 6% |
| Physics and Astronomy | 2 | 6% |
| Nursing and Health Professions | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 9 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 11 September 2017.
All research outputs
#18,571,001
of 23,001,641 outputs
Outputs from Annals of Nuclear Medicine
#396
of 635 outputs
Outputs of similar age
#242,396
of 316,058 outputs
Outputs of similar age from Annals of Nuclear Medicine
#3
of 7 outputs
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