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The segmentation and volumetry results of the proposed CBGU-Net model on the dataset of T2-weighted images demonstrate promising placental segmentation performance and indicate its potential for clinical application.
The radiomics model developed in this study facilitates the accurate and interpretable preoperative classification of lung adenocarcinoma subtypes and enhances diagnostic performance and supports clinical decision-making.
The multimodal model consistently predicted the high-risk recurrence phenotype across multiple centers and may serve as a pragmatic indicator of poor DFS to guide earlier individualized treatment decisions, including adjuvant therapy and intensified surveillance, in stage IA NSCLC.
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