Salient Central Lesion Volume: A Standardized Novel Fully Automated Proxy for Brain FLAIR Lesion Volume in Multiple Sclerosis

被引:9
作者
Dwyer, Michael G. [1 ,2 ]
Bergsland, Niels [1 ]
Ramasamy, Deepa P. [1 ]
Weinstock-Guttman, Bianca [3 ]
Barnett, Michael H. [4 ,5 ]
Wang, Chenyu [4 ]
Tomic, Davorka [6 ]
Silva, Diego [6 ]
Zivadinov, Robert [1 ,2 ]
机构
[1] Univ Buffalo State Univ New York, Buffalo Neuroimaging Anal Ctr, Dept Neurol, Jacobs Sch Med & Biomed Sci, Buffalo, NY 14203 USA
[2] Univ Buffalo State Univ New York, Ctr Biomed Imaging, Clin Translat Sci Inst, Buffalo, NY 14203 USA
[3] Univ Buffalo State Univ New York, Jacobs Comprehens Multiple Sclerosis Ctr, Dept Neurol, Jacobs Sch Med & Biomed Sci, Buffalo, NY 14203 USA
[4] Royal Prince Alfred Hosp, Sydney Neuroimaging Anal Ctr, Brain & Mind Ctr, Sydney, NSW, Australia
[5] Royal Prince Alfred Hosp, Dept Neurol, Sydney, NSW, Australia
[6] Novartis Pharma AG, Basel, Switzerland
关键词
automated lesion detection; proxy; multiple sclerosis; MRI; ATROPHY MEASUREMENT; SEGMENTATION; MRI; TOOL; FEASIBILITY; ALGORITHM;
D O I
10.1111/jon.12650
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
BACKGROUND AND PURPOSE Quantitative neuroimaging is an important part of multiple sclerosis research and clinical trials, and measures of lesion volume (LV) and brain atrophy are key clinical trial endpoints. However, translation of these endpoints to heterogeneous historical datasets and nonstandardized clinical routine imaging has been difficult. The NeuroSTREAM technique was recently introduced as a robust and broadly applicable surrogate for brain atrophy measurement, but no such surrogate currently exists for conventional T2-LV. Therefore, we sought to develop a fully automated proxy for T2-LV with similar analytic value but increased robustness to common issues arising in clinical routine imaging. METHODS We created an algorithm to identify salient central lesion volume (SCLV), comprised of the subset of lesion voxels within a specific distance to the lateral ventricles (centrality) and with intensity at least a quantitatively-derived amount brighter than normal appearing tissue (salience). We evaluated this method on four datasets (clinical, inter-scanner, scan-rescan, and real-world multi-center), including 1.5T, 3T, Philips, Siemens, and GE scanners with heterogeneous protocols, to assess agreement with conventional T2-LV, comparative relationship with disability, reliability across scanners and between scans, and applicability to real-world scans. RESULTS SCLV correlated strongly with conventional T2-LV in both research-quality (r = .90, P < .001) and real-world (r = 0.87, P < 0.001) datasets. It also showed similar correlations with Expanded Disability Status Scale, as conventional T2-LV (r = 0.48 for T2-LV vs. r = 0.45 for SCLV). Inter-scanner reproducibility (ICC) was 0.86, p < 0.001 for SCLV compared to 0.84, p < 0.001 for conventional T2-LV, whereas scan-rescan ICC was 0.999 for SCLV versus 0.997 for T2-LV. CONCLUSIONS SCLV is a robust, fully-automated proxy for T2-LV in situations where conventional T2-LV is not easily or reliably calculated.
引用
收藏
页码:615 / 623
页数:9
相关论文
共 23 条
[1]   Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure [J].
Commowick, Olivier ;
Istace, Audrey ;
Kain, Michael ;
Laurent, Baptiste ;
Leray, Florent ;
Simon, Mathieu ;
Pop, Sorina Camarasu ;
Girard, Pascal ;
Ameli, Roxana ;
Ferre, Jean-Christophe ;
Kerbrat, Anne ;
Tourdias, Thomas ;
Cervenansky, Frederic ;
Glatard, Tristan ;
Beaumont, Jeremy ;
Doyle, Senan ;
Forbes, Florence ;
Knight, Jesse ;
Khademi, April ;
Mahbod, Amirreza ;
Wang, Chunliang ;
McKinley, Richard ;
Wagner, Franca ;
Muschelli, John ;
Sweeney, Elizabeth ;
Roura, Eloy ;
Llado, Xavier ;
Santos, Michel M. ;
Santos, Wellington P. ;
Silva-Filho, Abel G. ;
Tomas-Fernandez, Xavier ;
Urien, Helene ;
Bloch, Isabelle ;
Valverde, Sergi ;
Cabezas, Mariano ;
Javier Vera-Olmos, Francisco ;
Malpica, Norberto ;
Guttmann, Charles ;
Vukusic, Sandra ;
Edan, Gilles ;
Dojat, Michel ;
Styner, Martin ;
Warfield, Simon K. ;
Cotton, Francois ;
Barillot, Christian .
SCIENTIFIC REPORTS, 2018, 8
[2]   Neurological software tool for reliable atrophy measurement (NeuroSTREAM) of the lateral ventricles on clinical-quality T2-FLAIR MRI scans in multiple sclerosis [J].
Dwyer, Michael G. ;
Silva, Diego ;
Bergsland, Niels ;
Horakova, Dana ;
Ramasamy, Deepa ;
Durfee, Jaqueline ;
Vaneckova, Manuela ;
Havrdova, Eva ;
Zivadinov, Robert .
NEUROIMAGE-CLINICAL, 2017, 15 :769-779
[3]   MR SIGNAL ABNORMALITIES AT 1.5-T IN ALZHEIMER DEMENTIA AND NORMAL AGING [J].
FAZEKAS, F ;
CHAWLUK, JB ;
ALAVI, A ;
HURTIG, HI ;
ZIMMERMAN, RA .
AMERICAN JOURNAL OF ROENTGENOLOGY, 1987, 149 (02) :351-356
[4]   Clinical Feasibility of Synthetic MIDI in Multiple Sclerosis: A Diagnostic and Volumetric Validation Study [J].
Granberg, T. ;
Uppman, M. ;
Hashim, F. ;
Cananau, C. ;
Nordin, L. E. ;
Shams, S. ;
Berglund, J. ;
Forslin, Y. ;
Aspelin, P. ;
Fredrikson, S. ;
Kristoffersen-Wiberg, M. .
AMERICAN JOURNAL OF NEURORADIOLOGY, 2016, 37 (06) :1023-1029
[5]   BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities [J].
Griffanti, Ludovica ;
Zamboni, Giovanna ;
Khan, Aamira ;
Li, Linxin ;
Bonifacio, Guendalina ;
Sundaresan, Vaanathi ;
Schulz, Ursula G. ;
Kuker, Wilhelm ;
Battaglini, Marco ;
Rothwell, Peter M. ;
Jenkinson, Mark .
NEUROIMAGE, 2016, 141 :191-205
[6]   Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images [J].
Jain, Saurabh ;
Sima, Diana M. ;
Ribbens, Annemie ;
Cambron, Melissa ;
Maertens, Anke ;
Van Hecke, Wim ;
De Mey, Johan ;
Barkhof, Frederik ;
Steenwijk, Martijn D. ;
Daams, Marita ;
Maes, Frederik ;
Van Huffel, Sabine ;
Vrenken, Hugo ;
Smeets, Dirk .
NEUROIMAGE-CLINICAL, 2015, 8 :367-375
[7]   Visual rating of age-related white matter changes on magnetic resonance imaging -: Scale comparison, interrater agreement, and correlations with quantitative measurements [J].
Kapeller, P ;
Barber, R ;
Vermeulen, RJ ;
Adèr, H ;
Scheltens, P ;
Freidl, W ;
Almkvist, O ;
Moretti, M ;
del Ser, T ;
Vaghfeldt, P ;
Enzinger, C ;
Barkhof, F ;
Inzitari, D ;
Erkinjunti, T ;
Schmidt, R ;
Fazekas, F .
STROKE, 2003, 34 (02) :441-445
[8]   A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions [J].
Maurer, CR ;
Qi, RS ;
Raghavan, V .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2003, 25 (02) :265-270
[9]   Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI [J].
Meier, Dominik S. ;
Guttmann, Charles R. G. ;
Tummala, Subhash ;
Moscufo, Nicola ;
Cavallari, Michele ;
Tauhid, Shahamat ;
Bakshi, Rohit ;
Weiner, Howard L. .
JOURNAL OF NEUROIMAGING, 2018, 28 (01) :36-47
[10]   Three-Dimensional Shape and Surface Features Distinguish Multiple Sclerosis Lesions from Nonspecific White Matter Disease [J].
Newton, Braeden D. ;
Wright, Katy ;
Winkler, Mandy D. ;
Bovis, Francesca ;
Takahashi, Masaya ;
Dimitrov, Ivan E. ;
Sormani, Maria Pia ;
Pinho, Marco C. ;
Okuda, Darin T. .
JOURNAL OF NEUROIMAGING, 2017, 27 (06) :613-619