MRI based radiomics nomogram for predict recurrence of non functioning pituitary macroadenomas post surgery
Abstract Objective: To establish and validate a comprehensive predictive model combining clinical data and radiomics features to improve the accuracy of predicting recurrence within five years after surgery in patients with non-functioning pituitary macroadenomas (NFMA). Methods: This retrospective...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-89907-z |
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| Summary: | Abstract Objective: To establish and validate a comprehensive predictive model combining clinical data and radiomics features to improve the accuracy of predicting recurrence within five years after surgery in patients with non-functioning pituitary macroadenomas (NFMA). Methods: This retrospective study included 292 NFMA patients who underwent surgery between January 2012 and January 2018, with an additional 123 patients as an external test set. Clinical, pathological, and conventional imaging features were collected and analyzed using univariate and multivariate logistic regression to identify independent risk factors for postoperative recurrence. Radiomic features were extracted from preoperative T1-weighted (T1WI), T2-weighted (T2WI), and T1-enhanced images using 3D Slicer software. A radiomics prediction model was developed, and a combined model integrating clinical and radiomics features was established. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The clinical model (Cli-score), radiomics model (Rad-score) and combined model were developed. The diagnostic performance of the clinical model in the external test set, showed an AUC of 0.757 (95%CI: 0.671–0.830), with SEN, SPE, and ACC of 82.5%, 59.04%, and 71.54%, respectively. The diagnostic performance of the radiomics model in the external test set showed an AUC of 0.835 (95% CI: 0.757–0.896), with 80%, 79.52% and 63.41% for SEN, SPE and ACC%, respectively. The diagnostic performance of the combined model in the external test set showed an AUC of 0.863 (95% CI: 0.790–0.919), with SEN, SPE, and ACC of 80%, 81.93%, and 68.30%, respectively. The calibration curve indicated good predictive performance, and DCA confirmed the high clinical utility of the combined model. Conclusion: The combined model provides a more accurate prediction of NFMA recurrence. This model can guide postoperative follow-up strategies and aid in early initiation of adjuvant therapy for high-risk patients. |
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| ISSN: | 2045-2322 |