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Artif Intell Med Imaging. Aug 28, 2020; 1(2): 70-77
Published online Aug 28, 2020. doi: 10.35711/aimi.v1.i2.70
Artificial intelligence and pituitary adenomas: A review
Elvira Guerriero, Lorenzo Ugga, Renato Cuocolo
Elvira Guerriero, Lorenzo Ugga, Renato Cuocolo, Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
Author contributions: Guerriero E collected the data and wrote the paper; Ugga L collected the data and edited the paper; Cuocolo R collected the data and edited the paper.
Conflict-of-interest statement: No conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Renato Cuocolo, MD, PhD, Department of Advanced Biomedical Sciences, University of Naples “Federico II”, via Pansini 5, Naples 80131, Italy. renato.cuocolo@unina.it
Received: May 25, 2020
Peer-review started: May 25, 2020
First decision: July 4, 2020
Revised: July 15, 2020
Accepted: August 22, 2020
Article in press: August 22, 2020
Published online: August 28, 2020
Abstract

The aim of this review was to provide an overview of the main concepts in machine learning (ML) and to analyze the ML applications in the imaging of pituitary adenomas. After describing the clinical, pathological and imaging features of pituitary tumors, we defined the difference between ML and classical rule-based algorithms, we illustrated the fundamental ML techniques: supervised, unsupervised and reinforcement learning and explained the characteristic of deep learning, a ML approach employing networks inspired by brain’s structure. Pre-treatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging. Regarding pre-treatment assessment, ML methods were used to have information about tumor consistency, predict cavernous sinus invasion and high proliferative index, discriminate null cell adenomas, which respond to neo-adjuvant radiotherapy from other subtypes, predict somatostatin analogues response and visual pathway injury. Regarding neurosurgical outcome prediction, the following applications were discussed: Gross total resection prediction, evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery. Although clinical applicability requires more replicability, generalizability and validation, results are promising, and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.

Keywords: Pituitary adenoma, Machine learning, Deep learning, Radiomics, Texture analysis, Magnetic resonance imaging

Core Tip: Machine learning (ML) has seen an explosion of interest in medical imaging because of its capability of analyzing large amounts of data. Recent studies applied ML techniques to the imaging of pituitary adenomas. The purpose of our review was to describe the main concepts in ML and its current and potential applications in imaging analysis of pituitary tumors.