Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

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Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
1 Online-Ressource.
Language
Englisch

Bibliographic citation
Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis ; day:16 ; month:3 ; year:2024 ; pages:1-9
Japanese journal of radiology ; (16.3.2024), 1-9

Creator
Nakajo, Masatoyo
Hirahara, Daisuke
Jinguji, Megumi
Ojima, Satoko
Hirahara, Mitsuho
Tani, Atsushi
Takumi, Koji
Kamimura, Kiyohisa
Ohishi, Mitsuru
Yoshiura, Takashi
Contributor
SpringerLink (Online service)

DOI
10.1007/s11604-024-01546-y
URN
urn:nbn:de:101:1-2405210712050.743293209024
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:54 AM CEST

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Associated

  • Nakajo, Masatoyo
  • Hirahara, Daisuke
  • Jinguji, Megumi
  • Ojima, Satoko
  • Hirahara, Mitsuho
  • Tani, Atsushi
  • Takumi, Koji
  • Kamimura, Kiyohisa
  • Ohishi, Mitsuru
  • Yoshiura, Takashi
  • SpringerLink (Online service)

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