Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT

Abstract To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin’s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independen...

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Autores principales:	May Sadik, Jesús López-Urdaneta, Johannes Ulén, Olof Enqvist, Armin Krupic, Rajender Kumar, Per-Ola Andersson, Elin Trägårdh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/68300fb6b52045efada7176b61b3489c
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spelling	oai:doaj.org-article:68300fb6b52045efada7176b61b3489c2021-12-02T15:45:15ZArtificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT10.1038/s41598-021-89656-92045-2322https://doaj.org/article/68300fb6b52045efada7176b61b3489c2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89656-9https://doaj.org/toc/2045-2322Abstract To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin’s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017–2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25–0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.May SadikJesús López-UrdanetaJohannes UlénOlof EnqvistArmin KrupicRajender KumarPer-Ola AnderssonElin TrägårdhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q May Sadik Jesús López-Urdaneta Johannes Ulén Olof Enqvist Armin Krupic Rajender Kumar Per-Ola Andersson Elin Trägårdh Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
description	Abstract To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin’s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017–2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25–0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.
format	article
author	May Sadik Jesús López-Urdaneta Johannes Ulén Olof Enqvist Armin Krupic Rajender Kumar Per-Ola Andersson Elin Trägårdh
author_facet	May Sadik Jesús López-Urdaneta Johannes Ulén Olof Enqvist Armin Krupic Rajender Kumar Per-Ola Andersson Elin Trägårdh
author_sort	May Sadik
title	Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
title_short	Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
title_full	Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
title_fullStr	Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
title_full_unstemmed	Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
title_sort	artificial intelligence could alert for focal skeleton/bone marrow uptake in hodgkin’s lymphoma patients staged with fdg-pet/ct
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/68300fb6b52045efada7176b61b3489c
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Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT

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