PURPOSE
To develop and externally validate a multi-phase CT deep learning framework for preoperative risk stratification of 2-5 cm gastric gastrointestinal stromal tumors (GISTs).
METHODS
This retrospective multicenter study included 427 patients with pathologically confirmed gastric GISTs. At Center A, patients trea...
Si-Hang Guo, Cheng-Ze Yu, Jian-Ying Xu et al.· European Journal of Radiolog...· 0 citations
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.
Yechao Huang, Joe Sarah Luciny, Yumin Hu et al.· Chinese Journal of Academic...· 0 citations
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.
Feng-Juan Tian, Jing Ding, Zhen-Yu Cao et al.· Quantitative Imaging in Medi...· 0 citations
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.
L. Lai, Yanji Wang, Jiangle Jiang et al.· Frontiers in Immunology· 0 citations
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