Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI.
Machine learning
Magnetic resonance imaging
Optic chiasm
Pituitary adenoma
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
26
12
2022
accepted:
23
05
2023
revised:
18
05
2023
medline:
27
10
2023
pubmed:
25
7
2023
entrez:
24
7
2023
Statut:
ppublish
Résumé
To investigate whether morphological changes after surgery and delta-radiomics of the optic chiasm obtained from routine MRI could help predict postoperative visual recovery of pituitary adenoma patients. A total of 130 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (n = 87) and non-recovery group (n = 43) according to visual outcome 1 year after endoscopic endonasal transsphenoidal surgery. Morphological parameters of the optic chiasm were measured preoperatively and postoperatively, including chiasmal thickness, deformed angle, and suprasellar extension. Delta-radiomics of the optic chiasm were calculated based on features extracted from preoperative and postoperative coronal T2-weighted images, followed by machine learning modeling using least absolute shrinkage and selection operator wrapped with support vector machine through fivefold cross-validation in the development set. The delta-radiomic model was independently evaluated in the test set, and compared with the combined model that incorporated delta-radiomics, significant clinical and morphological parameters. Postoperative morphological changes of the optic chiasm could not significantly be used as predictors for the visual outcome. In contrast, the delta-radiomics model represented good performances in predicting visual recovery, with an AUC of 0.821 in the development set and 0.811 in the independent test set. Moreover, the combined model that incorporated age and delta-radiomics features of the optic chiasm achieved the highest AUC of 0.841 and 0.840 in the development set and independent test set, respectively. Our proposed machine learning models based on delta-radiomics of the optic chiasm can be used to predict postoperative visual recovery of pituitary adenoma patients. Our delta-radiomics-based models from MRI enable accurate visual recovery predictions in pituitary adenoma patients who underwent endoscopic endonasal transsphenoidal surgery, facilitating better clinical decision-making and ultimately improving patient outcomes. • Prediction of the postoperative visual outcome for pituitary adenoma patients is important but challenging. • Delta-radiomics of the optic chiasm after surgical decompression represented better prognostic performances compared with its morphological changes. • The proposed machine learning models can serve as novel approaches to predict visual recovery for pituitary adenoma patients in clinical practice.
Identifiants
pubmed: 37488296
doi: 10.1007/s00330-023-09963-9
pii: 10.1007/s00330-023-09963-9
pmc: PMC10598191
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7482-7493Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
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