Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors

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dc.authoridKIZILAY, Ahmet/0000-0003-3048-6489
dc.authoridGÜNDÜZ, EMRAH/0000-0001-8857-7290
dc.authoridALCIN, Omer/0000-0002-2917-3736;
dc.authorwosidKIZILAY, Ahmet/ABI-8293-2020
dc.authorwosidGÜNDÜZ, EMRAH/AAA-4350-2021
dc.authorwosidALCIN, Omer/AAH-3525-2020
dc.authorwosidPiazza, Cesare/H-5925-2018
dc.contributor.authorGunduz, Emrah
dc.contributor.authorAlcin, Omer Faruk
dc.contributor.authorKizilay, Ahmet
dc.contributor.authorPiazza, Cesare
dc.date.accessioned2024-08-04T20:51:45Z
dc.date.available2024-08-04T20:51:45Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractPurpose of review Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. Recent findings The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy. All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.en_US
dc.identifier.doi10.1097/MOO.0000000000000782
dc.identifier.endpage113en_US
dc.identifier.issn1068-9508
dc.identifier.issn1531-6998
dc.identifier.issue2en_US
dc.identifier.pmid34907957en_US
dc.identifier.scopus2-s2.0-85125965909en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage107en_US
dc.identifier.urihttps://doi.org/10.1097/MOO.0000000000000782
dc.identifier.urihttps://hdl.handle.net/11616/100523
dc.identifier.volume30en_US
dc.identifier.wosWOS:000765490100006en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherLippincott Williams & Wilkinsen_US
dc.relation.ispartofCurrent Opinion in Otolaryngology & Head and Neck Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial intelligenceen_US
dc.subjectdeep learningen_US
dc.subjectmachine learningen_US
dc.subjectparotid gland tumorsen_US
dc.subjectradiomicsen_US
dc.titleRadiomics and deep learning approach to the differential diagnosis of parotid gland tumorsen_US
dc.typeReview Articleen_US

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