ORIGINAL ARTICLE   

Journal of Neurosurgical Sciences 2024 Jan 29

DOI: 10.23736/S0390-5616.23.06162-3

Copyright © 2023 EDIZIONI MINERVA MEDICA

language: English

Prediction of diabetes insipidus occurrence after endoscopic endonasal removal of sellar lesions using MRI-based radiomics and machine learning

Ciro MASTANTUONI ¹, Lorenzo UGGA ², Domenico SOLARI ¹ ✉, Serena D’ANIELLO ², Gaia SPADARELLA ², Renato CUOCOLO ³, Filippo F. ANGILERI ⁴, Luigi M. CAVALLO ¹

¹ Division of Neurosurgery, Department of Neurosciences and Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy; ² Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy; ³ Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy; ⁴ Unit of Neurosurgery, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy

BACKGROUND: Pituitary adenomas and craniopharyngiomas are the most common lesions of the sellar region. These tumors are responsible for invasion or compression of crucial neurovascular structures. The involvement of the pituitary stalk warrants high rates of both pre- and post- operative diabetes insipidus. The aim of our study was to assess the accuracy of machine learning analysis from preoperative MRI of pituitary adenomas and craniopharyngiomas for the prediction of DI occurrence.
METHODS: All patients underwent MRI exams either on a 1.5- or 3-T MR scanner from two Institutions, including coronal T2-weighted (T2-w) and contrast-enhanced T1-weighted (CE T1-w) Turbo Spin Echo sequences. Feature selection was carried out as a multi-step process, with a threshold of 0.75 to identify robust features. Further feature selection steps included filtering based on feature variance (threshold >0.01) and pairwise correlation (threshold <0.80). A Bayesian Network model was trained with 10-fold cross validation employing SMOTE to balance classes exclusively within the training folds.
RESULTS: Thirty patients were included in this study. In total 2394 features were extracted and 1791 (75%) resulted stable after ICC analysis. The number of variant features was 1351 and of non-colinear features was 125. Finally, 10 features were selected by oneR ranking. The Bayesian Network model showed an accuracy of 63% with a precision of 77% for DI prediction (0.68 area under the precision-recall curve).
CONCLUSIONS: We assessed the accuracy of machine learning analysis of texture-derived parameters from preoperative MRI of pituitary adenomas and craniopharyngiomas for the prediction of DI occurrence.

KEY WORDS: Magnetic resonance imaging; Craniopharyngiomas; Pituitary neoplasms