Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preope...

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Autores principales:	Gloria Ravegnini, Martina Ferioli, Alessio Giuseppe Morganti, Lidia Strigari, Maria Abbondanza Pantaleo, Margherita Nannini, Antonio De Leo, Eugenia De Crescenzo, Manuela Coe, Alessandra De Palma, Pierandrea De Iaco, Stefania Rizzo, Anna Myriam Perrone
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	uterine tumors uterine sarcoma fibroids radiomics artificial intelligence deep learning Medicine R
Acceso en línea:	https://doaj.org/article/e6485373581d4ab1a1228696c32d337b
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spelling	oai:doaj.org-article:e6485373581d4ab1a1228696c32d337b2021-11-25T18:07:49ZRadiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review10.3390/jpm111111792075-4426https://doaj.org/article/e6485373581d4ab1a1228696c32d337b2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1179https://doaj.org/toc/2075-4426Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Methods: Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). Results: The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. Conclusion: To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.Gloria RavegniniMartina FerioliAlessio Giuseppe MorgantiLidia StrigariMaria Abbondanza PantaleoMargherita NanniniAntonio De LeoEugenia De CrescenzoManuela CoeAlessandra De PalmaPierandrea De IacoStefania RizzoAnna Myriam PerroneMDPI AGarticleuterine tumorsuterine sarcomafibroidsradiomicsartificial intelligencedeep learningMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1179, p 1179 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	uterine tumors uterine sarcoma fibroids radiomics artificial intelligence deep learning Medicine R
spellingShingle	uterine tumors uterine sarcoma fibroids radiomics artificial intelligence deep learning Medicine R Gloria Ravegnini Martina Ferioli Alessio Giuseppe Morganti Lidia Strigari Maria Abbondanza Pantaleo Margherita Nannini Antonio De Leo Eugenia De Crescenzo Manuela Coe Alessandra De Palma Pierandrea De Iaco Stefania Rizzo Anna Myriam Perrone Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
description	Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Methods: Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). Results: The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. Conclusion: To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.
format	article
author	Gloria Ravegnini Martina Ferioli Alessio Giuseppe Morganti Lidia Strigari Maria Abbondanza Pantaleo Margherita Nannini Antonio De Leo Eugenia De Crescenzo Manuela Coe Alessandra De Palma Pierandrea De Iaco Stefania Rizzo Anna Myriam Perrone
author_facet	Gloria Ravegnini Martina Ferioli Alessio Giuseppe Morganti Lidia Strigari Maria Abbondanza Pantaleo Margherita Nannini Antonio De Leo Eugenia De Crescenzo Manuela Coe Alessandra De Palma Pierandrea De Iaco Stefania Rizzo Anna Myriam Perrone
author_sort	Gloria Ravegnini
title	Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
title_short	Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
title_full	Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
title_fullStr	Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
title_full_unstemmed	Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
title_sort	radiomics and artificial intelligence in uterine sarcomas: a systematic review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e6485373581d4ab1a1228696c32d337b
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Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

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