AIMC Topic: Pituitary Neoplasms
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A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.
Frontiers in endocrinology
Dec 13, 2024
OBJECTIVE: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after su...
Genotype-negative multiple endocrine neoplasia type 1 with prolactinoma, hyperparathyroidism, and subclinical Cushing's syndrome accompanied by hyperglycemia: a case report.
Frontiers in endocrinology
Dec 12, 2024
BACKGROUND: Multiple endocrine neoplasia type 1 (MEN1) is a rare autosomal dominant disorder, accompanied by multiple endocrine neoplasms of the parathyroid, pancreas, pituitary, and other neoplasms in the adrenal glands. However, in some cases, pati...
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.
Ferroptosis-related biomarkers for adamantinomatous craniopharyngioma treatment: conclusions from machine learning techniques.
Frontiers in endocrinology
Nov 13, 2024
INTRODUCTION: Adamantinomatous craniopharyngioma (ACP) is difficult to cure completely and prone to recurrence after surgery. Ferroptosis as an iron-dependent programmed cell death, may be a critical process in ACP. The study aimed to screen diagnost...
Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms.
Scientific reports
Sep 27, 2024
This study aimed to predict arginine vasopressin deficiency (AVP-D) following transsphenoidal pituitary adenoma surgery using machine learning algorithms. We reviewed 452 cases from December 2013 to December 2023, analyzing clinical and imaging data....
Clinical Application of Artificial Intelligence in Prediction of Intraoperative Cerebrospinal Fluid Leakage in Pituitary Surgery: A Systematic Review and Meta-Analysis.
World neurosurgery
Sep 10, 2024
BACKGROUND: Postoperative cerebrospinal fluid (CSF) leakage is the leading adverse event in transsphenoidal surgery. Intraoperative CSF (ioCSF) leakage is one of the most important predictive factors for postoperative CSF leakage. This systematic rev...
Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning.
PloS one
Sep 6, 2024
Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnormal cell growth within the brain, exhibits various sizes and shapes, making their manual detection f...
Video-Based Performance Analysis in Pituitary Surgery - Part 2: Artificial Intelligence Assisted Surgical Coaching.
World neurosurgery
Aug 8, 2024
BACKGROUND: Superior surgical skill improves surgical outcomes in endoscopic pituitary adenoma surgery. Video-based coaching programs, pioneered in professional sports, have shown promise in surgical training. In this study, we developed and assessed...
A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network.
World neurosurgery
Jun 12, 2024
BACKGROUND: Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical and anatomic risk factors of intraoperative CSF leak are not well defined. We applied a two-dimension...
Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.
Journal of imaging informatics in medicine
Jun 6, 2024
This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 t...
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