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Attention Score in Context
| Title |
Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence
|
|---|---|
| Published in |
BMC Cancer, March 2023
|
| DOI | 10.1186/s12885-023-10704-w |
| Pubmed ID | |
| Authors |
Chao Zhang, Lisha Qi, Jun Cai, Haixiao Wu, Yao Xu, Yile Lin, Zhijun Li, Vladimir P. Chekhonin, Karl Peltzer, Manqing Cao, Zhuming Yin, Xin Wang, Wenjuan Ma |
| Abstract |
Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis. We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data. Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients. Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine. |
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 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 % |
|---|---|---|
| Unspecified | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Student > Ph. D. Student | 1 | 4% |
| Student > Postgraduate | 1 | 4% |
| Student > Doctoral Student | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 17 | 71% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 13% |
| Computer Science | 2 | 8% |
| Unspecified | 2 | 8% |
| Unknown | 17 | 71% |
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 15 March 2023.
All research outputs
#21,364,089
of 23,861,036 outputs
Outputs from BMC Cancer
#6,730
of 8,567 outputs
Outputs of similar age
#338,978
of 407,511 outputs
Outputs of similar age from BMC Cancer
#121
of 161 outputs
Altmetric has tracked 23,861,036 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,567 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.