Abstract:
PURPOSE:Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery. METHODS:Data of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images. Feature stability analysis and a multistep feature selection were performed. After oversampling to balance the classes, 80% of the data was used for hyperparameter tuning via stratified 5-fold cross-validation, while a 20% hold-out set was employed for its final testing, using an Extra Trees ensemble meta-algorithm. The reference standard was based on surgical findings. RESULTS:A total of 1118 texture features were extracted, of which 741 were stable. After removal of low variance (n = 4) and highly intercorrelated (n = 625) parameters, recursive feature elimination identified a subset of 14 features. After hyperparameter tuning, the Extra Trees classifier obtained an accuracy of 93%, sensitivity of 100%, and specificity of 87%. The area under the receiver operating characteristic and precision-recall curves was 0.99. CONCLUSION:Preoperative T2-weighted MRI texture analysis and machine learning could predict pituitary macroadenoma consistency.
journal_name
Neuroradiologyjournal_title
Neuroradiologyauthors
Cuocolo R,Ugga L,Solari D,Corvino S,D'Amico A,Russo D,Cappabianca P,Cavallo LM,Elefante Adoi
10.1007/s00234-020-02502-zsubject
Has Abstractpub_date
2020-12-01 00:00:00pages
1649-1656issue
12eissn
0028-3940issn
1432-1920pii
10.1007/s00234-020-02502-zjournal_volume
62pub_type
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