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Craniopharyngioma

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Endoscopic endonasal transsphenoidal surgery using the iArmS operation support robot: initial experience in 43 patients.

Neurosurgical focus
Objective The intelligent arm-support system, iArmS, which follows the surgeon's arm and automatically fixes it at an adequate position, was developed as an operation support robot. iArmS was designed to support the surgeon's forearm to prevent hand ...

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

Cancer gene therapy
Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis method and standard therapy cannot treat ACPs effectively. In this paper, we aimed to identify key gen...

Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images.

Scientific reports
Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would r...

A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and vi...

Ferroptosis-related biomarkers for adamantinomatous craniopharyngioma treatment: conclusions from machine learning techniques.

Frontiers in endocrinology
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...

Diagnostic support in pediatric craniopharyngioma using deep learning.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
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.

Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

Neuro-oncology
BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, p...