The self-supervised model predicts lung cancer outcomes in large chest CT scans, indicating improved diagnosis effectiveness.
The self-supervised Unified CT-Based Lung Cancer Imaging Foundation model, trained on 33 901 chest scans, surpassed state-of-the-art algorithms in predicting lung cancer histologic subtype, stage, survival, and recurrence while reducing annotation demands.
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Li et al. (2026) studied this question.
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