From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas DOI Creative Commons
Aslı Beril Karakaş, Figen GÖKMEN, Mehmet Asım Özer

и другие.

Neurosurgical Review, Год журнала: 2025, Номер 48(1)

Опубликована: Апрель 29, 2025

Язык: Английский

Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants DOI

Ibrahim Mohammadzadeh,

Bardia Hajikarimloo, Behnaz Niroomand

и другие.

Neurosurgical Review, Год журнала: 2025, Номер 48(1)

Опубликована: Фев. 15, 2025

Язык: Английский

Процитировано

1
Development and Validation of an Early Recurrence Prediction Model for High-Grade Glioma Integrating Temporalis Muscle and Tumor Features: Exploring the Prognostic Value of Temporalis Muscle DOI
Quanwei Zhu, Xiaocong Hu,

Qihui Ye

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

This study aimed to develop and validate a predictive model for early recurrence of high-grade glioma (HGG) within 180 days, assess the prognostic value preoperative postoperative temporalis muscle metrics (area thickness), explore their significance in follow-up. Seventy-one molecularly confirmed HGG patients were included, with data sourced from local TCIA (The Cancer Imaging Archive) RHUH-GBM (Río Hortega University Hospital Glioblastoma) dataset. Tumor segmentation was performed using deep learning, radiomic features extracted following comparison manual segmentation. Feature selection conducted mutual information recursive feature elimination. A comprehensive integrating 3D tumor radiomics developed compared tumor-only identify optimal framework. SHAP analysis used evaluate interpretability importance. The TM_Tumor_HistGradientBoosting model, incorporating 16 including metrics, outperformed accuracy (0.89), recall (0.87), F1 score (0.88). highlighted that cross-sectional area strongly associated risk, while thickness significantly contributed prediction. Combining MRI substantially improved prediction HGG. Temporalis serve as objective sustainable indicators significant clinical

Язык: Английский

Процитировано

0
Development and Validation of a Predictive Machine Learning Model for Postoperative Long-Term Diabetes Insipidus Following Transsphenoidal Surgery for Sellar Lesions DOI

Simon Ammanuel,

Manasa Kalluri,

Jesse D Montoure

и другие.

Clinical Neurology and Neurosurgery, Год журнала: 2025, Номер 253, С. 108899 - 108899

Опубликована: Апрель 17, 2025

Язык: Английский

Процитировано

0
From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas DOI Creative Commons
Aslı Beril Karakaş, Figen GÖKMEN, Mehmet Asım Özer

и другие.

Neurosurgical Review, Год журнала: 2025, Номер 48(1)

Опубликована: Апрель 29, 2025

Язык: Английский

Процитировано

0