A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients
Abstract In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated pe...
Main Authors: | Samantha Bove, Maria Colomba Comes, Vito Lorusso, Cristian Cristofaro, Vittorio Didonna, Gianluca Gatta, Francesco Giotta, Daniele La Forgia, Agnese Latorre, Maria Irene Pastena, Nicole Petruzzellis, Domenico Pomarico, Lucia Rinaldi, Pasquale Tamborra, Alfredo Zito, Annarita Fanizzi, Raffaella Massafra |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-05-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-11876-4 |
Similar Items
-
Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer
by: Annarita Fanizzi, et al.
Published: (2023-05-01) -
A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
by: Raffaella Massafra, et al.
Published: (2022-01-01) -
A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification.
by: Raffaella Massafra, et al.
Published: (2022-01-01) -
A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence
by: Nicola Amoroso, et al.
Published: (2021-05-01) -
Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
by: Annarita Fanizzi, et al.
Published: (2021-11-01)