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J Neurol Surg B Skull Base
DOI: 10.1055/a-2607-0735
Original Article

Radiomics for Preoperative Assessment of Pituitary Adenoma Consistency with T2-Weighted MRI: A Multicenter Study

Edoardo Agosti
1   Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
,
Renato Cuocolo
2   Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
,
Marcello Mangili
1   Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
,
Vittorio Rampinelli
3   Unit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
,
Pierlorenzo Veiceschi 
4   Unit of Neurosurgery, ARNAS Ospedale Civico of Palermo, Palermo, Italy
,
Martina Cappelletti
5   Unit of Neurosurgery, Ospedale Ca' Foncello, Treffviso, Italy
,
Pier Paolo Panciani
1   Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
,
Amedeo Piazza
6   Neurosurgery Division, Department of Neuroscience, “Sapienza” University of Rome, Rome, Italy
,
Ilaria Bove
7   Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli Federico II, Naples, Italy
,
Domenico Solari
7   Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli Federico II, Naples, Italy
,
Luigi Maria Cavallo
7   Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli Federico II, Naples, Italy
,
Davide Locatelli
8   Division of Neurological Surgery, Department of Biotechnology and Life Sciences, University of Insubria-Varese, ASST Sette Laghi, Ospedale di Circolo e Fondazione Macchi, Varese, Italy
,
Francesco Doglietto
9   Facoltà di Medicina e Chirurgia, Università Cattolica del Sacro Cuore, Rome, Italy
10   Division of Neurosurgery, Department of Neuroscience, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
,
Alessandro Fiorindi
1   Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
,
Marco Maria Fontanella
1   Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
,
Lorenzo Ugga
11   Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,” Naples, Italy
› Author Affiliations
Funding This work was supported in part by the “Centro di ricerche per gli adenomi ipofisari e le patologie sellari” of the Insubria University in Varese, Italy.
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Abstract

Introduction

Pituitary adenoma (PA) consistency significantly influences the outcomes of endoscopic endonasal surgery. Radiomics represents a promising tool for objective and quantitative assessment using T2-weighted magnetic resonance imaging (MRI).

Methods

A multicenter retrospective database was collected (2012–2023), including 394 patients with preoperative T2-weighted MRI and histologically confirmed PAs after endoscopic endonasal surgical removal. Tumor segmentation was performed manually on coronal T2-weighted images using ITK-SNAP software. Radiomic features were extracted with Pyradiomics. A 60:40 dataset split was used to train an Extra Trees classifier, and recursive feature elimination was used to select features. Model performance was assessed using sensitivity, specificity, and the area under the curve of receiver operating characteristic (AUC-ROC) curve metrics.

Results

From 1,106 extracted radiomic features, 65 were identified as most predictive following variance and correlation filtering. The sensitivity, specificity, and accuracy of the ET classifier were 74%, 74%, and 63% (±10%), respectively. The AUC-ROC curve was 0.59.

Conclusion

Despite its moderate accuracy and AUC-ROC curve, the ET model showed promising performance to predict preoperative PA consistency, underlying the power of radiomics-driven models in PA surgical planning.

Keywords

pituitary adenoma - consistency - preoperative MRI - radiomics - machine learning

Ethical Approval

This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The IRB approved this study.


Supplementary Material



Publication History

Received: 20 February 2025

Accepted: 12 May 2025

Accepted Manuscript online:
13 May 2025

Article published online:
04 June 2025

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