Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.
Authors of this article are:
Niu J Zhang S Ma S Diao J Zhou W Tian J Zang Y Jia W.
A summary of the article is shown below:
OBJECTIVES: To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging.METHODS: A total of 194 patients with Knosp grade two and three PAs (training set: n = 97; test set: n = 97) were enrolled in this retrospective study. From CE-T1 and T2 MR images, 2553 quantitative imaging features were extracted. To select the most informative features, least absolute shrinkage and selection operator (LASSO) was performed. Subsequently, a linear support vector machine (SVM) was used to fit the predictive model. Furthermore, a nomogram was constructed by incorporating clinico-radiological risk factors and radiomics signature, and the clinical usefulness of the nomogram was validated using decision curve analysis (DCA).RESULTS: Three imaging features were selected in the training set, based on which the radiomics model yielded area under the curve (AUC) values of 0.852 and 0.826 for the training and test sets. The nomogram based on the radiomics signature and the clinico-radiological risk factors yielded an AUC of 0.899 in the training set and 0.871 in the test set.CONCLUSIONS: The nomogram developed in this study might aid neurosurgeons in the pre-operative prediction of CS invasion by Knosp grade two and three PAs, which might contribute to creating surgical strategies.KEY POINTS: • Pre-operative diagnosis of CS invasion by PAs might affect creating surgical strategies • MRI might help for diagnosis of CS invasion by PAs before surgery • Radiomics might improve the CS invasion detection by MR images.
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This article is a good source of information and a good way to become familiar with topics such as: Cavernous sinus;Neoplasm invasion;Nomogram;Pituitary adenomas;Support vector machine.
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