Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI

Renato Cuocolo; Lorenzo Ugga; Domenico Solari; Sergio Corvino; Alessandra D’Amico; Daniela Russo; Paolo Cappabianca; Luigi Maria Cavallo; Andrea Elefante

doi:10.1007/s00234-020-02502-z

Back

Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI

Journal article

Open access

Peer reviewed

Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI

Renato Cuocolo, Lorenzo Ugga, Domenico Solari, Sergio Corvino, Alessandra D’Amico, Daniela Russo, Paolo Cappabianca, Luigi Maria Cavallo and Andrea Elefante

Neuroradiology, Vol.62(12), pp.1649-1656

01/12/2020

DOI: https://doi.org/10.1007/s00234-020-02502-z

PMCID: 7666676

PMID: 32705290

Abstract

Consistency

Diagnostic Neuroradiology

Machine learning

Magnetic resonance imaging

Pituitary adenoma

Radiomics

Files and links (1)

url

https://doi.org/10.1007/s00234-020-02502-zView

Published (Version of record) Open

Metrics

1 Record Views

See more details

Details

Title: Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI
Creators - without role: Renato Cuocolo - University of Naples Federico II
Lorenzo Ugga - University of Naples Federico II
Domenico Solari - University of Naples Federico II
Sergio Corvino - University of Naples Federico II
Alessandra D’Amico - University of Naples Federico II
Daniela Russo - University of Naples Federico II
Paolo Cappabianca - University of Naples Federico II
Luigi Maria Cavallo - University of Naples Federico II
Andrea Elefante - University of Naples Federico II
Publication Details: Neuroradiology, Vol.62(12), pp.1649-1656
Publisher: Springer Berlin Heidelberg
Grant note: ;
Identifiers: 9945020708331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Journal article