Cargando…
Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients
Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized...
Autores principales: | Alves, Allan F. F., Souza, Sérgio A., Ruiz, Raul L., Reis, Tarcísio A., Ximenes, Agláia M. G., Hasimoto, Erica N., Lima, Rodrigo P. S., Miranda, José Ricardo A., Pina, Diana R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7967117/ https://www.ncbi.nlm.nih.gov/pubmed/33730292 http://dx.doi.org/10.1007/s13246-021-00988-2 |
Ejemplares similares
-
The relationship between cardiac radiation dose and mediastinal lymph node involvement in stage III non-small cell lung cancer patients
por: McNew, Laura K., et al.
Publicado: (2017) -
CT texture analysis and node-RADS CT score of mediastinal lymph nodes - diagnostic performance in lung cancer patients
por: Meyer, Hans-Jonas, et al.
Publicado: (2022) -
Texture Analysis and Synthesis of Malignant and Benign Mediastinal Lymph Nodes in Patients with Lung Cancer on Computed Tomography
por: Pham, Tuan D., et al.
Publicado: (2017) -
Echoendoscopy with elastography in mediastinal lymph nodes
por: Colaiacovo, Rogerio, et al.
Publicado: (2019) -
Mediastinal lymph node staging for lung cancer
por: Sawabata, Noriyoshi
Publicado: (2019)