"Interpretable machine learning model for predicting clinically significant ..."

Wenjun Zhao et al. (2024)

> Home

Details and statistics

DOI: 10.1186/S12880-024-01548-2

access: open

type: Journal Article

metadata version: 2025-01-07

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/bmcmi/ZhaoHWSN24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/bmcmi/ZhaoHWSN24
Wenjun Zhao, Mengyan Hou, Juan Wang, Dan Song, Yongchao Niu:
Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features. BMC Medical Imaging 24: 353 (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.