.png){kind=icon}
Return
Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization
.png){kind=icon}
OA
AI
DOI：10.1002/mp.18070.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The increased use of CT imaging has elevated the incidental detection of renal masses, necessitating accurate differentiation between benign and malignant nodules. Radiomics offers potential for improved diagnostics; however, it is limited by variability in imaging parameters such as slice thickness, highlighting the need for effective harmonization techniques.
Keywords:
kidney cancer
machine learning
radiomics
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.2
Papers:
3.7W
Citations:
3.2W
Organization
Cited Papers
ShareSaveGeneralized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSave
ShareSaveShareSaveEarly-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of 18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant MetastasisRADIOLOGYOAAI
RADIOLOGYIF15.2
OAAIShareSave
View more