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Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study.

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第一作者： Isabel C,Dahm

第一单位： Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany.

作者： Isabel C,Dahm ^[1] ; Manuel,Kolb ^[2] ; Sebastian,Altmann ^[3] ; Konstantin,Nikolaou ^[4] ; Sergios,Gatidis ^[1] ; Ahmed E,Othman ^[3] ; Alessa,Hering ^[5] ; Jan H,Moltz ^[6] ; Felix,Peisen ^[1]

作者单位： Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany. ^[1] Department of Radiology, Te Whatu Ora Waikato, Hamilton 3240, New Zealand. ^[2] Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Langenbeckstr. 1, 55131 Mainz, Germany. ^[3] Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany.;Image-Guided and Functionally Instructed Tumor Therapies (iFIT), The Cluster of Excellence (EXC 2180), 72076 Tuebingen, Germany. ^[4] Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany.;Diagnostic Image Analysis Group, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands. ^[5] Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany. ^[6]

关键词 RECIST 1.1 artificial intelligence and machine learning melanoma

DOI 10.3390/cancers16234009

PMID 39682195

发布时间 2025-01-04

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