"A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy ..."

Fatma El-Zahraa A. El-Gamal et al. (2024)

> Home

Details and statistics

DOI: 10.1109/ACCESS.2024.3432459

access: open

type: Journal Article

metadata version: 2024-08-22

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/access/ElGamalSAAASSATEGCE24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/access/ElGamalSAAASSATEGCE24
Fatma El-Zahraa A. El-Gamal, Ahmed Sharafeldeen, Eman Alnaghy, Reham Alghandour, Norah Saleh Alghamdi, Khadiga M. A. Seddik, Sameh Shamaa, Amal Aboueleneen, Ahmed Elsaid Tolba, Samir Elmougy, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz:
A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: Integration of Multimodal Radiomics With Clinical and Molecular Subtype Markers. IEEE Access 12: 104983-105003 (2024)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.