AIMC Topic: Pituitary Neoplasms

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Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.

Scientific reports May 31, 2024
To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor resection based on machine learning algorithms. A cross-sectional study was conducted. From January to December 2022, 158 patients underwent transnasal s...
Adult Cross-Sectional Studies Male Female Risk Factors Aged ROC Curve Pituitary Neoplasms Olfaction Disorders Middle Aged Risk Assessment Postoperative Complications Humans Machine Learning Algorithms
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Diagnostic support in pediatric craniopharyngioma using deep learning.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery Apr 22, 2024
PURPOSE: We studied a pediatric group of patients with sellar-suprasellar tumors, aiming to develop a convolutional deep learning algorithm for radiological assistance to classify them into their respective cohort.
Humans Magnetic Resonance Imaging Adolescent Pituitary Neoplasms Female Craniopharyngioma Child, Preschool Child Male Deep Learning
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Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.

Neuroradiology Feb 19, 2024
PURPOSE: To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep learning-based reconstruction (DLR) for depiction of pituitary adenoma and parasellar regions on contrast-enhanced MRI.
Deep Learning Imaging, Three-Dimensional Contrast Media Adult Male Middle Aged Image Interpretation, Computer-Assisted Neoplasm, Residual Magnetic Resonance Imaging Pituitary Neoplasms Adenoma Humans Retrospective Studies Female Aged
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Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.

Pituitary Jan 6, 2024
PURPOSE: Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and mach...
Pituitary Neoplasms Adenoma Humans Artificial Intelligence Machine Learning
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Deep learning based identification of pituitary adenoma on surgical endoscopic images: a pilot study.

Neurosurgical review Nov 1, 2023
Accurate tumor identification during surgical excision is necessary for neurosurgeons to determine the extent of resection without damaging the surrounding tissues. No conventional technologies have achieved reliable performance for pituitary adenoma...
Treatment Outcome Pituitary Neoplasms Adenoma Humans Retrospective Studies Endoscopy Deep Learning Pilot Projects
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MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

BMC medical informatics and decision making Jan 23, 2023
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and ...
Humans Meningeal Neoplasms Machine Learning Magnetic Resonance Imaging Brain Neoplasms Pituitary Neoplasms Deep Learning Glioma Meningioma
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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.
Pituitary Neoplasms Humans Image Processing, Computer-Assisted Adenoma Cavernous Sinus Magnetic Resonance Imaging Retrospective Studies Pituitary Diseases Neoplasm Invasiveness Female Deep Learning
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Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network.

European radiology Nov 18, 2022
OBJECTIVES: To develop a U-Net-based deep learning model for automated segmentation of craniopharyngioma.
Humans Pituitary Neoplasms Neural Networks, Computer Deep Learning Craniopharyngioma Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.

Clinical neurology and neurosurgery May 17, 2022
OBJECTIVES: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index...
Ki-67 Antigen Magnetic Resonance Imaging Pituitary Neoplasms Adenoma Humans Retrospective Studies Deep Learning
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Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation.

Neurosurgery Apr 8, 2022
BACKGROUND: Unplanned readmission after transsphenoidal resection of pituitary adenoma can occur in up to 10% of patients but is unpredictable.
Retrospective Studies Humans Predictive Value of Tests Machine Learning Pituitary Neoplasms Bayes Theorem Adenoma Patient Readmission
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