Haase Robert, Pinetz Thomas, Kobler Erich, Bendella Zeynep, Zülow Stefan, Schievelkamp Arndt-Hendrik, Schmeel Frederic Carsten, Panahabadi Sarah, Stylianou Anna Magdalena, Paech Daniel, Foltyn-Dumitru Martha, Wagner Verena, Schlamp Kai, Heussel Gudula, Holtkamp Mathias, Heussel Claus Peter, Vahlensieck Martin, Luetkens Julian A, Schlemmer Heinz-Peter, Haubold Johannes, Radbruch Alexander, Effland Alexander, Deuschl Cornelius, Deike Katerina
From the Department of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany (R.H., E.K., Z.B., S.Z., A.-H.S., F.C.S., S.P., A.M.S., D.P., A.R., K.D.); Institute of Applied Mathematics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany (T.P., A.E.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (D.P.); Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.P., H.-P.S.); Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany (M.F.-D., K.S., G.H., C.P.H.); Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany (M.F.-D.); Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany (V.W., C.P.H.); Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (M.H., J.H., C.D.); Translational Lung Research Center Heidelberg (TLRC), Member of the German Center of Lung Research (DZL), Heidelberg, Germany (C.P.H.); Praxisnetz, Radiology and Nuclear Medicine, Bonn, Germany (M.V.); Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany (J.A.L.); German Center for Neurodegenerative Diseases (DZNE), Helmholtz Association of German Research Centers, Bonn, Germany (A.R., K.D.); and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA (K.D.).
Invest Radiol. 2025 Aug 1;60(8):543-551. doi: 10.1097/RLI.0000000000001166. Epub 2025 Feb 18.
Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this study was to test the benefit of a deep learning-based contrast signal amplification in true single-dose T1-weighted (T-SD) images creating artificial double-dose (A-DD) images for metastasis detection in brain magnetic resonance imaging.
In this prospective, multicenter study, a deep learning-based method originally trained on noncontrast, low-dose, and T-SD brain images was applied to T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8 years; 23 women) acquired externally between November 2022 and June 2023. Four readers with different levels of experience independently reviewed T-SD and A-DD images for metastases with 4 weeks between readings. A reference reader reviewed additionally acquired true double-dose images to determine any metastases present. Performances were compared using Mid-p McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.
All readers found more metastases using A-DD images. The 2 experienced neuroradiologists achieved the same level of sensitivity using T-SD images (62 of 91 metastases, 68.1%). While the increase in sensitivity using A-DD images was only descriptive for 1 of them (A-DD: 65 of 91 metastases, +3.3%, P = 0.424), the second neuroradiologist benefited significantly with a sensitivity increase of 12.1% (73 of 91 metastases, P = 0.008). The 2 less experienced readers (1 resident and 1 fellow) both found significantly more metastases on A-DD images (resident, T-SD: 61.5%, A-DD: 68.1%, P = 0.039; fellow, T-SD: 58.2%, A-DD: 70.3%, P = 0.008). They were therefore able to use A-DD images to increase their sensitivity to the neuroradiologists' initial level on regular T-SD images. False-positive findings did not differ significantly between sequences. However, readers showed descriptively more false-positive findings on A-DD images. The benefit in sensitivity particularly applied to metastases ≤5 mm (5.7%-17.3% increase in sensitivity).
A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.
双剂量对比增强脑成像可改善肿瘤轮廓描绘及隐匿性转移灶的检测,但受钆基对比剂对患者和环境影响的担忧所限。本研究的目的是测试基于深度学习的对比信号放大在真实单剂量T1加权(T-SD)图像中的益处,创建人工双剂量(A-DD)图像用于脑磁共振成像中的转移灶检测。
在这项前瞻性多中心研究中,一种最初在非对比、低剂量和T-SD脑图像上训练的基于深度学习的方法被应用于2022年11月至2023年6月期间从外部获取的30名参与者(平均年龄±标准差,58.5±11.8岁;23名女性)的T-SD图像。四名经验水平不同的阅片者独立审查T-SD和A-DD图像以检测转移灶,两次阅片间隔4周。一名参考阅片者审查额外获取的真实双剂量图像以确定存在的任何转移灶。使用Mid-p McNemar检验比较敏感性,使用Wilcoxon符号秩检验比较假阳性结果。
所有阅片者使用A-DD图像发现了更多转移灶。两名经验丰富的神经放射科医生使用T-SD图像时达到了相同的敏感性水平(91个转移灶中的62个,68.1%)。虽然使用A-DD图像时敏感性的增加对其中一名医生仅为描述性(A-DD:91个转移灶中的65个,+3.3%,P = 0.424),但第二名神经放射科医生显著受益,敏感性增加了1