AIMC Topic: Cavernous Sinus

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Automatic pituitary adenoma segmentation and identification of cavernous sinus invasion via multitask learning.

Clinical radiology Nov 21, 2024
AIM: This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) segmentation and identification of cavernous sinus (CS) invasion.
Humans Magnetic Resonance Imaging Neoplasm Invasiveness Deep Learning Image Interpretation, Computer-Assisted Cavernous Sinus Adult Adenoma Middle Aged Pituitary Neoplasms Male Female Aged Retrospective Studies
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Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.

European journal of radiology Dec 12, 2022
PURPOSE: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma.
Neoplasm Invasiveness Pituitary Neoplasms Magnetic Resonance Imaging Cavernous Sinus Pituitary Diseases Adenoma Female Image Processing, Computer-Assisted Deep Learning Humans Retrospective Studies
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Thin-Slice Pituitary MRI with Deep Learning-Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study.

AJNR. American journal of neuroradiology Jan 6, 2022
BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning-based reconstruction can better predict cavernous sinu...
Cavernous Sinus Humans Magnetic Resonance Imaging Pituitary Neoplasms Deep Learning Prospective Studies Female Middle Aged Neoplasm Invasiveness
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Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.

European radiology Sep 25, 2018
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.
Area Under Curve Cavernous Sinus Middle Aged Pituitary Neoplasms Adenoma Nomograms Risk Factors Neoplasm Invasiveness Retrospective Studies Male Adult Contrast Media Aged Humans Magnetic Resonance Imaging Female Support Vector Machine
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