Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.

Villanueva-Meyer, Javier E; Bakas, Spyridon; Tiwari, Pallavi; Lupo, Janine M; Calabrese, Evan; Davatzikos, Christos; Bi, Wenya Linda; Ismail, Marwa; Akbari, Hamed; Lohmann, Philipp; Booth, Thomas C; Wiestler, Benedikt; Aerts, Hugo J W L; Rasool, Ghulam; Tonn, Joerg C; Nowosielski, Martha; Jain, Rajan; Colen, Rivka R; Pati, Sarthak; Baid, Ujjwal; Vollmuth, Philipp; Macdonald, David; Vogelbaum, Michael A; Chang, Susan M; Huang, Raymond Y; Galldiks, Norbert

doi:10.1016/S1470-2045(24)00316-4

journal article

Title:: Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.
Document type:: Journal Article; Review
Author(s):: Villanueva-Meyer, Javier E; Bakas, Spyridon; Tiwari, Pallavi; Lupo, Janine M; Calabrese, Evan; Davatzikos, Christos; Bi, Wenya Linda; Ismail, Marwa; Akbari, Hamed; Lohmann, Philipp; Booth, Thomas C; Wiestler, Benedikt; Aerts, Hugo J W L; Rasool, Ghulam; Tonn, Joerg C; Nowosielski, Martha; Jain, Rajan; Colen, Rivka R; Pati, Sarthak; Baid, Ujjwal; Vollmuth, Philipp; Macdonald, David; Vogelbaum, Michael A; Chang, Susan M; Huang, Raymond Y; Galldiks, Norbert
Abstract:: The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed. «
The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a consider... »
Journal title abbreviation:: Lancet Oncol
Year:: 2024
Journal volume:: 25
Journal issue:: 11
Pages contribution:: e581-e588
Fulltext / DOI:: doi:10.1016/S1470-2045(24)00316-4
Pubmed ID:: http://view.ncbi.nlm.nih.gov/pubmed/39481414
Print-ISSN:: 1470-2045
TUM Institution:: Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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