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4203 citation(s) found for "Yu, Chen".

P1‐325: Predicting Progression to Mild Cognitive Impairment in Cognitively Unimpaired Individuals Using Neuroimaging Biomarkers
Stonnington, Cynthia M. , Lee, Wendy , Bauer, Robert, Sharieff, Sameen, Chen, Yinghua , Caselli, Richard J. , Reiman, Eric M. , Locke, Dona E.C. , Chen, Kewei

Mayo Clinic

Alzheimer's & Dementia | October 17, 2016 | Volume: 12 | Issue: 7S_Part_11 | Pages: No page available

10.1016/j.jalz.2016.06.1076 Get It

P4‐031: Integrative Network Analysis of Multiple Alzheimer's Disease Rnaseq Studies From the Accelerating Medicine Partnership‐Alzheimer's Disease Consortium
Zhang, Bin , Komashko, Vitalina, Mostafavi, Sara , Funk, Cory C. , Allen, Mariet , Amberkar, Sandeep , Hide, Winston, Schadt, Eric , Jager, Philip L. De , Ertekin‐Taner, Nilüfer , Logsdon, Ben , Chen, Mingming , Perumal, Thanneer Malai , Hide, Winston, Friend, Stephen , Bennett, David A. , Price, Nathan D. , Mangravite, Lara M.

Mayo Clinic

Alzheimer's & Dementia | October 17, 2016 | Volume: 12 | Issue: 7S_Part_21 | Pages: No page available

10.1016/j.jalz.2016.06.2120 Get It

P2‐243: Higher BMI is Associated with Greater Cerebral Glucose Metabolism in Late Middle‐Aged and Elderly Subjects Regardless of APOE ε4 Genotype
Weise, Christopher M., Chen, Yinghua , Goradia, Dhruman D., Savage, Cary R. , Caselli, Richard J. , Krakoff, Jonathan , Reiman, Eric M. , Chen, Kewei

Mayo Clinic

Alzheimer's & Dementia | October 17, 2016 | Volume: 12 | Issue: 7S_Part_14 | Pages: No page available

10.1016/j.jalz.2016.06.1411 Get It

P4‐149: Peripheral Apoe Levels are Associated with Regional Gray Matter Volume, Cerebral Glucose Metabolism and Cognitive Performance in Cognitively Normal APOE E3/4 Carriers
Lee, Wendy , Chen, Yinghua , Reiman, Eric M. , Caselli, Richard J. , Nielsen, Henrietta M. , Chen, Kewei , Chen, Yinghua, Bu, Guojun

Mayo Clinic

Alzheimer's & Dementia | October 17, 2016 | Volume: 12 | Issue: 7S_Part_22 | Pages: No page available

10.1016/j.jalz.2016.06.2240 Get It

P3‐285: Patch‐Based Sparse Coding and Multivariate Surface Morphometry for Predicting Amnestic Mild Cognitive Impairment and Alzheimer’S Disease in Cognitively Unimpaired Individuals
Zhang, Jie , Li, Qingyang , Bauer, Robert, Reiman, Eric M. , Caselli, Richard J. , Stonnington, Cynthia M. , Wang, Yalin , Shi, Jie , Chen, Kewei

Mayo Clinic

Alzheimer's & Dementia | October 17, 2016 | Volume: 12 | Issue: 7S_Part_19 | Pages: No page available

10.1016/j.jalz.2016.06.1939 Get It

Dataset for reporting of thymic epithelial tumours: recommendations from the International Collaboration on Cancer Reporting (ICCR)
Detterbeck, Frank, Marx, Alexander , Roden, Anja C. , Marchevsky, Alberto M. , Mukai, Kiyoshi , Marino, Mirella , Bakker, Michael A. den , Yang, Woo‐Ick, Judge, Meagan , Hirschowitz, Lynn, Nicholson, Andrew G. , Chen, Gang

Mayo Clinic

Histopathology | October 14, 2016 | Volume: 70 | Issue: 4 | Pages: 522-538

10.1111/his.13099 | PMID 27735079 Get It

AimsThe International Collaboration on Cancer Reporting (ICCR) is a not‐for‐profit organization formed by the Royal Colleges of Pathologists of Australasia and the United Kingdom, the College of American Pathologists, the Canadian Association of Pathologists–Association Canadienne des Pathologists in association with the Canadian Partnership Against Cancer, and the European Society of Pathology. Its goal is to produce standardized, internationally agreed, evidence‐based datasets for use throughout the world.Methods and resultsThis article describes the development of a cancer dataset by the multidisciplinary ICCR expert panel for the reporting of thymic epithelial tumours. The dataset includes ‘required’ (mandatory) and ‘recommended’ (non‐mandatory) elements, which are validated by a review of current evidence and supported by explanatory text. Seven required elements and 12 recommended elements were agreed by the international dataset authoring committee to represent the essential information for the reporting of thymic epithelial tumours.ConclusionsThe use of an internationally agreed, structured pathology dataset for reporting thymic tumours provides all of the necessary information for optimal patient management, facilitates consistent and accurate data collection, and provides valuable data for research and international benchmarking. The dataset also provides a valuable resource for those countries and institutions that are not in a position to develop their own datasets.