Name
Title | ||
|---|---|---|
Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. |
Abstract | ||
|---|---|---|
•Shape features presented greatest potential on nodal metastasis pattern recognition.•Gray-level cooccurrence matrix texture features presented greatest potential on distant metastasis and histopathological pattern recognition.•Our radiomics model may provide additional information for therapy decision support based on metastases prediction and aid the histopathological subtype diagnosis. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cmpb.2018.02.015 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Lung cancer,Metastasis prediction,Pattern recognition,Quantitative image analysis,Radiomics | Lung cancer,Metastasis,Receiver operating characteristic,Pattern recognition,Computer science,Histopathology,Biopsy,Computed tomography,Artificial intelligence,Stage (cooking),Radiomics | Journal |
Volume | ISSN | Citations |
159 | 0169-2607 | 1 |
PageRank | References | Authors |
0.36 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Raniery Ferreira | 1 | 1 | 0.36 |
| Marcel Koenigkam-Santos | 2 | 3 | 1.07 |
| Federico Enrique Garcia Cipriano | 3 | 1 | 0.70 |
| Alexandre Todorovic Fabro | 4 | 1 | 0.70 |
| Paulo M. Azevedo-Marques | 5 | 195 | 19.04 |