Related Experiment Video
Updated: May 12, 2025
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
Published on: December 15, 2023
Artificial Intelligence-Based Radiomic Model in Craniopharyngiomas: A Systematic Review and Meta-Analysis on
Ibrahim Mohammadzadeh1, Bardia Hajikarimloo1, Behnaz Niroomand1
1Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background:
Craniopharyngiomas (CPs) are rare, benign brain tumors originating from Rathke's pouch remnants, typically located in the sellar/parasellar region. Accurate differentiation is crucial due to varying prognoses, with adamantinomatous CPs having higher recurrence and worse outcomes. Magnetic resonance imaging struggles with overlapping features, complicating diagnosis. This study evaluates the role of artificial intelligence (AI) in diagnosing, segmenting, and classifying CPs, emphasizing its potential to improve clinical decision-making, particularly for radiologists and neurosurgeons.
Methods:
This systematic review and meta-analysis assess AI applications in diagnosing, segmenting, and classifying on CP patients. A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science for studies employing AI models in patients with CP. Performance metrics such as sensitivity, specificity, accuracy, and area under the curve were extracted and synthesized.
Results:
Eleven studies involving 1916 patients were included in the analysis. The pooled results revealed a sensitivity of 0.740 (95% confidence interval [CI]: 0.673-0.808), specificity of 0.813 (95% CI: 0.729-0.898), and accuracy of 0.746 (95% CI: 0.679-0.813). The area under the curve for diagnosis was 0.793 (95% CI: 0.719-0.866), and for classification, it was 0.899 (95% CI: 0.846-0.951). The sensitivity for segmentation was found to be 0.755 (95% CI: 0.704-0.805).
Conclusions:
AI-based models show strong potential in enhancing the diagnostic accuracy and clinical decision-making process for CPs. These findings support the use of AI tools for more reliable preoperative assessment, leading to better treatment planning and patient outcomes. Further research with larger datasets is needed to optimize and validate AI applications in clinical practice.
