Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Abstract Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall su...

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Autori principali:	P. Rauch, H. Stefanits, M. Aichholzer, C. Serra, D. Vorhauer, H. Wagner, P. Böhm, S. Hartl, I. Manakov, M. Sonnberger, E. Buckwar, F. Ruiz-Navarro, K. Heil, M. Glöckel, J. Oberndorfer, S. Spiegl-Kreinecker, K. Aufschnaiter-Hiessböck, S. Weis, A. Leibetseder, W. Thomae, T. Hauser, C. Auer, S. Katletz, A. Gruber, M. Gmeiner
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2023-06-01
Serie:	Scientific Reports
Accesso online:	https://doi.org/10.1038/s41598-023-36298-8
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