| Title |
Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features
|
|---|---|
| Published in |
European Radiology Experimental, August 2019
|
| DOI | 10.1186/s41747-019-0117-2 |
| Pubmed ID | |
| Authors |
Alberto Stefano Tagliafico, Bianca Bignotti, Federica Rossi, Joao Matos, Massimo Calabrese, Francesca Valdora, Nehmat Houssami |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 18% |
| Student > Postgraduate | 4 | 9% |
| Student > Master | 4 | 9% |
| Student > Doctoral Student | 3 | 7% |
| Researcher | 3 | 7% |
| Other | 8 | 18% |
| Unknown | 15 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 15 | 33% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Computer Science | 2 | 4% |
| Engineering | 2 | 4% |
| Nursing and Health Professions | 1 | 2% |
| Other | 5 | 11% |
| Unknown | 18 | 40% |