Ugga, Lorenzo (2020) Radiomic data mining and machine learning on preoperative pituitary adenoma MRI. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Resource language: English
Title: Radiomic data mining and machine learning on preoperative pituitary adenoma MRI
Creators:
CreatorsEmail
Ugga, Lorenzolorenzo.ugga@unina.it
Date: 7 December 2020
Number of Pages: 54
Institution: Università degli Studi di Napoli Federico II
Department: Scienze Biomediche Avanzate
Dottorato: Scienze biomorfologiche e chirurgiche
Ciclo di dottorato: 33
Coordinatore del Corso di dottorato:
nomeemail
Cuocolo, Albertocuocolo@unina.it
Tutor:
nomeemail
Brunetti, ArturoUNSPECIFIED
Date: 7 December 2020
Number of Pages: 54
Keywords: Radiomics; pituitary adenoma; MRI
Settori scientifico-disciplinari del MIUR: Area 06 - Scienze mediche > MED/36 - Diagnostica per immagini e radioterapia
Date Deposited: 18 Feb 2021 10:33
Last Modified: 07 Jun 2023 10:21
URI: http://www.fedoa.unina.it/id/eprint/14099

Collection description

Pituitary adenomas are among the most frequent intracranial tumors, accounting for the majority of sellar/suprasellar masses in adults. MRI is the preferred imaging modality for detecting pituitary adenomas. Radiomics represents the conversion of digital medical images into mineable high-dimensional data. This process is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology and that these relationships can be revealed via quantitative image analyses. The aim of this thesis is to apply machine learning algorithms on parameters obtained by texture analysis on MRI images in order to distinguish functional from non-functional pituitary macroadenomas, to predict their ki-67 proliferation index class, and to predict pituitary macroadenoma surgical consistency prior to an endoscopic endonasal procedure.

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