A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation

Autor: Dominic LaBella, Omaditya Khanna, Shan McBurney-Lin, Ryan Mclean, Pierre Nedelec, Arif S. Rashid, Nourel hoda Tahon, Talissa Altes, Ujjwal Baid, Radhika Bhalerao, Yaseen Dhemesh, Scott Floyd, Devon Godfrey, Fathi Hilal, Anastasia Janas, Anahita Kazerooni, Collin Kent, John Kirkpatrick, Florian Kofler, Kevin Leu, Nazanin Maleki, Bjoern Menze, Maxence Pajot, Zachary J. Reitman, Jeffrey D. Rudie, Rachit Saluja, Yury Velichko, Chunhao Wang, Pranav I. Warman, Nico Sollmann, David Diffley, Khanak K. Nandolia, Daniel I Warren, Ali Hussain, John Pascal Fehringer, Yulia Bronstein, Lisa Deptula, Evan G. Stein, Mahsa Taherzadeh, Eduardo Portela de Oliveira, Aoife Haughey, Marinos Kontzialis, Luca Saba, Benjamin Turner, Melanie M. T. Brüßeler, Shehbaz Ansari, Athanasios Gkampenis, David Maximilian Weiss, Aya Mansour, Islam H. Shawali, Nikolay Yordanov, Joel M. Stein, Roula Hourani, Mohammed Yahya Moshebah, Ahmed Magdy Abouelatta, Tanvir Rizvi, Klara Willms, Dann C. Martin, Abdullah Okar, Gennaro D’Anna, Ahmed Taha, Yasaman Sharifi, Shahriar Faghani, Dominic Kite, Marco Pinho, Muhammad Ammar Haider, Michelle Alonso-Basanta, Javier Villanueva-Meyer, Andreas M. Rauschecker, Ayman Nada, Mariam Aboian, Adam Flanders, Spyridon Bakas, Evan Calabrese

Publikováno v: Scientific Data, Vol 11, Iss 1, Pp 1-8 (2024)

Abstract Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment

Externí odkaz: https://doaj.org/article/a98787bda3824eadba3e679915417559

White matter and literacy: A dynamic system in flux

Autor: Ethan Roy, Adam Richie-Halford, John Kruper, Manjari Narayan, David Bloom, Pierre Nedelec, Andreas M. Rauschecker, Leo P. Sugrue, Timothy T. Brown, Terry L. Jernigan, Bruce D. McCandliss, Ariel Rokem, Jason D. Yeatman

Publikováno v: Developmental Cognitive Neuroscience, Vol 65, Iss , Pp 101341- (2024)

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-l

Externí odkaz: https://doaj.org/article/9fc672d0ba5f430ea244001faa4bf9c9

An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro)

Autor: Cassie Kline, Schuyler Stoller, Lennox Byer, David Samuel, Janine M. Lupo, Melanie A. Morrison, Andreas M. Rauschecker, Pierre Nedelec, Walter Faig, Dena B. Dubal, Heather J. Fullerton, Sabine Mueller

Publikováno v: Frontiers in Oncology, Vol 12 (2022)

BackgroundNeurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus o

Externí odkaz: https://doaj.org/article/e60cd29d6bf64a1aa641b8b501baf2c7

Microstructural development from 9 to 14 years: Evidence from the ABCD Study

Autor: Clare E. Palmer, Diliana Pecheva, John R. Iversen, Donald J. Hagler, Jr, Leo Sugrue, Pierre Nedelec, Chun Chieh Fan, Wesley K. Thompson, Terry L. Jernigan, Anders M. Dale

Publikováno v: Developmental Cognitive Neuroscience, Vol 53, Iss , Pp 101044- (2022)

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter

Externí odkaz: https://doaj.org/article/f913e7f198b148fe8340c627e417aa09

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

Autor: Michael Tran Duong, Pierre Nedelec, Evan Calabrese, John B. Colby, Tyler Gleason, Leo P. Sugrue, Andreas M. Rauschecker, Christopher P. Hess, David A. Weiss, Jeffrey D. Rudie

Publikováno v: Radiology. Artificial intelligence, vol 4, iss 1
Radiol Artif Intell

PURPOSE: To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. MATERIALS AND METHOD

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76a93f66b1a5813f317ca4e6f7990d35
https://escholarship.org/uc/item/6356841n