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Mendeley readers
Attention Score in Context
| Title |
Can automated CT body composition analysis predict high-grade Clavien–Dindo complications in patients with RCC undergoing partial and radical nephrectomy?
|
|---|---|
| Published in |
Scottish Medical Journal, March 2023
|
| DOI | 10.1177/00369330231166122 |
| Pubmed ID | |
| Authors |
Emin Demirel, Okan Dilek |
| Abstract |
This study investigated the relationship between body tissue composition analysis and complications according to the Clavien-Dindo classification in patients with renal cell carcinoma (RCC) who underwent partial (PN) or radical nephrectomies (RN). We obtained all data of 210 patients with RCC from the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) dataset and obtained radiological images from the cancer image archive. Body composition was assessed with automated artificial intelligence software using the convolutional network segmentation technique from abdominal computed tomography images. We included 125 PN and 63 RN in the study. The relationship between body fat and muscle tissue distribution and complications according to the Clavien-Dindo classification was evaluated between these two groups. Clavien-Dindo 3A and higher (high grade) complications were developed in 9 of 125 patients who underwent PN and 7 of 63 patients who underwent RN. There was no significant difference between all body composition values between patients with and without high-grade complications. This study showed that body muscle-fat tissue distribution did not affect patients with 3A and above complications according to the Clavien-Dindo classification in patients who underwent nephrectomy due to RCC. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 2 | 20% |
| Student > Bachelor | 1 | 10% |
| Lecturer > Senior Lecturer | 1 | 10% |
| Unknown | 6 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 2 | 20% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Social Sciences | 1 | 10% |
| Unknown | 6 | 60% |
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 22 March 2023.
All research outputs
#19,444,782
of 23,915,168 outputs
Outputs from Scottish Medical Journal
#382
of 451 outputs
Outputs of similar age
#294,166
of 401,724 outputs
Outputs of similar age from Scottish Medical Journal
#3
of 3 outputs
Altmetric has tracked 23,915,168 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 451 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.