Published in: Journal of Nuclear Medicine (2024)
Cited by: 1
Breakthrough:
This study evaluates semi-supervised machine learning approaches for predicting lung cancer survival outcomes, integrating PET/CT imaging and clinical features.
Key Findings:
AI-powered PET/CT feature extraction improves prognosis accuracy.
Semi-supervised learning improves survival prediction in smaller datasets.
Model integrates radiomics and clinical biomarkers for precision medicine.
Impact:
The research provides advanced AI solutions for oncology, helping oncologists personalize treatment strategies for better patient outcomes.
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