Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

Journal: BMC medical imaging

PMID: 40319246

Abstract

OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. This study aims to develop and evaluate machine learning models using MRI-based radiomics features to differentiate these lesions.

Authors

Hang Qu
Qiqi Ban

Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu province, 225000, China.
Liangxue Zhou

Department of Neurosurgery, West China Hospital, Sichuan University, No.37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
Haihan Duan

College of Computer Science, Sichuan University, South Section 1, Yihuan Road, Chengdu, 610065, Sichuan, China.
Wei Wang

State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
AiJun Peng

Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China. Electronic address: 13801456336@163.com.

Keywords

Adenoma Adolescent Adult Aged Central Nervous System Cysts Craniopharyngioma Diagnosis, Differential Female Humans Machine Learning Magnetic Resonance Imaging Male Meningeal Neoplasms Meningioma Middle Aged Pituitary Neoplasms Radiomics Retrospective Studies Sella Turcica Support Vector Machine Young Adult

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View on PubMed Access via DOI PubMed (40319246)

Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

Abstract

Authors

Keywords

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