↓ Skip to main content

Sparse feature selection for classification and prediction of metastasis in endometrial cancer

Overview of attention for article published in BMC Genomics, March 2017
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
24 Mendeley
Title
Sparse feature selection for classification and prediction of metastasis in endometrial cancer
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3604-y
Pubmed ID
Authors

Mehmet Eren Ahsen, Todd P. Boren, Nitin K. Singh, Burook Misganaw, David G. Mutch, Kathleen N. Moore, Floor J. Backes, Carolyn K. McCourt, Jayanthi S. Lea, David S. Miller, Michael A. White, Mathukumalli Vidyasagar

Abstract

Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 29%
Student > Ph. D. Student 5 21%
Student > Postgraduate 3 13%
Unspecified 2 8%
Student > Doctoral Student 2 8%
Other 5 21%
Readers by discipline Count As %
Medicine and Dentistry 9 38%
Computer Science 5 21%
Biochemistry, Genetics and Molecular Biology 3 13%
Unspecified 3 13%
Agricultural and Biological Sciences 1 4%
Other 3 13%

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 01 April 2017.
All research outputs
#7,067,078
of 9,274,545 outputs
Outputs from BMC Genomics
#4,913
of 6,357 outputs
Outputs of similar age
#188,476
of 260,858 outputs
Outputs of similar age from BMC Genomics
#80
of 96 outputs
Altmetric has tracked 9,274,545 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,357 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 260,858 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.