An MRI radiomics approach using invasion-based weak supervision for identifying and evaluating aggressive PitNETs

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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Publication Details

Journal
npj Digital Medicine
Published
2025-12-02
DOI
https://doi.org/10.1038/s41746-025-02189-7
Primary Topic
Pituitary Gland Disorders and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

An MRI radiomics approach using invasion-based weak supervision for identifying and evaluating aggressive PitNETs

Yueqian Sun, Xiudong Guan, Shunchang Ma, Deling Li et al.
npj Digital Medicine
Pituitary Gland Disorders and Treatments
article

An MRI radiomics approach using invasion-based weak supervision for identifying and evaluating aggressive PitNETs

Yueqian Sun, Xiudong Guan, Shunchang Ma, Deling Li, Yangyang Wang, Yan Liu, Li Ma, Yuyan Zhang, Chuanbao Zhang, Jun Yang, Jia Wang, Yongqiang Tang
article en

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.

npj Digital MedicineVol. 9(1)
Capital Medical University (CN), Chinese Academy of Sciences (CN), Beijing Tian Tan Hospital (CN), National Clinical Research Center for Digestive Diseases (CN), Shandong Institute of Automation (CN), Institute of Automation (CN), Beijing Luhe Hospital Affiliated to Capital Medical University (CN), First Affiliated Hospital of Zhengzhou University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Beijing Postdoctoral Science Foundation
Openalex Percentile: Top 50%
Pituitary Gland Disorders and Treatments
DOIOpen Access PDFOpenAlex
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