Radiomics-based prediction of pituitary adenoma consistency: a systematic review and meta-analysis.
Authors
Affiliations (8)
Affiliations (8)
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
- ASL NA3sud, U.O.S.M. 55-57 Torre del Greco, Ercolano, Italy.
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
- Department of Neuroradiology, University Hospital "San Giovanni Di Dio E Ruggi d'Aragona", Salerno, Italy.
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples "Federico II", Naples, Italy.
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", P.zza L. Miraglia, 2, Naples, ZIP, 80138, Italy.
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", P.zza L. Miraglia, 2, Naples, ZIP, 80138, Italy. [email protected].
Abstract
Pituitary adenoma consistency significantly influences surgical strategy and outcomes, yet it cannot be reliably predicted using conventional imaging. By converting images into quantitative data, radiomics can identify imaging patterns that may not be distinguishable by standard visual interpretation. This study aims to evaluate the diagnostic performance of radiomics-based MRI models for predicting pituitary adenoma consistency, to determine its potential for presurgical planning. A systematic search of PubMed, EMBASE, and Scopus was performed on 12/03/2025 following PRISMA-DTA guidelines and a pre-registered protocol (PROSPERO/CRD420251246028). Eligible studies applied radiomics and machine learning/deep learning to predict pituitary adenoma consistency. We performed random-effects meta-analyses to evaluate the area under the receiver operating characteristic (ROC) curve (AUC). A hierarchical summary ROC (HSROC) model estimated sensitivity and specificity. Risk of bias and study quality were assessed with QUADAS-2 and METRICS. Fourteen studies were included. The pooled discriminative performance was good (AUC = 0.86,95% C.I. [0.76;0.92], I<sup>2</sup> = 92.45%, k = 14). The HSROC model showed a sensitivity = 0.74,95% C.I. [0.56;0.87] and specificity = 0.79, 95% C.I. [0.73;0.84]. No significant performance differences were observed across algorithms, dimensionality, sequence, although T2w and combined sequence models showed higher AUC trends. Risk of bias was low to moderate, and study quality was good overall. Heterogeneity resulted high and small-study effect emerged in the main analysis. Radiomics-based MRI models demonstrate good diagnostic performance for predicting pituitary adenoma consistency and may support presurgical assessment and planning. However, substantial heterogeneity, evidence of small-study effects, and limited external validation warrant cautious interpretation of the pooled estimates and currently restrict clinical generalizability. Future studies should prioritize multicenter external validation and standardized radiomics workflows.