An MRI radiomics approach using invasion-based weak supervision for identifying and evaluating aggressive PitNETs Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.1038/s41746-025-02189-7
Pituitary neuroendocrine tumor (PitNET) aggressiveness critically affects treatment and prognosis, yet reliable noninvasive preoperative tools remain lacking. We developed a deep learning radiomics (DLR) model integrating automatic segmentation, feature extraction, selection, and DLR score computation, trained on the training cohort and validated on the remaining cohorts (total n = 1089 from three medical centers). Using nnUnet and a fine-tuned Swin Transformer, 13 key features were identified to construct the model. The DLR score demonstrated strong correlation with Knosp and Hardy-Wilson invasion classifications, while outperforming them in predicting recurrence and indicating aggressive pathological markers (Ki-67, p53, macrophages) and revealing biological pathways (MAPK, TGF-β). The model was further implemented into an online platform, enabling clinical deployment. This noninvasive preoperative approach provides a robust imaging biomarker for identifying and evaluating PitNET aggressiveness and may support individualized treatment strategies.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41746-025-02189-7
- https://www.nature.com/articles/s41746-025-02189-7_reference.pdf
- OA Status
- gold
- References
- 33
- OpenAlex ID
- https://openalex.org/W4416893420