Artificial Intelligence based radiomic model in Craniopharyngiomas: A Systematic Review and Meta-Analysis on Diagnosis, Segmentation, and Classification.
Authors
Affiliations (5)
Affiliations (5)
- Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: [email protected].
- Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
- Department of Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract
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 ACPs having higher recurrence and worse outcomes. MRI 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. This systematic review and meta-analysis assess AI applications in diagnosing, segmenting, and classifying on CPs 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 (AUC) were extracted and synthesized. Eleven studies involving 1916 patients were included in the analysis. The pooled results revealed a sensitivity of 0.740 (95% 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 (AUC) 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). 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.