Ugga, Lorenzo (2020) Radiomic data mining and machine learning on preoperative pituitary adenoma MRI. [Tesi di dottorato]
Preview |
Text
Ugga_Lorenzo_33.pdf Download (19MB) | Preview |
| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Radiomic data mining and machine learning on preoperative pituitary adenoma MRI |
| Creators: | Creators Email Ugga, Lorenzo lorenzo.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: | nome email Cuocolo, Alberto cuocolo@unina.it |
| Tutor: | nome email Brunetti, Arturo UNSPECIFIED |
| 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.
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |
