ArticleNPJ digital medicine2025
An MRI radiomics approach using invasion-based weak supervision for identifying and evaluating aggressive PitNETs.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
12 authors.
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Abstract
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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