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A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information.
Ramakrishnan, Divya; Jekel, Leon; Chadha, Saahil; Janas, Anastasia; Moy, Harrison; Maleki, Nazanin; Sala, Matthew; Kaur, Manpreet; Petersen, Gabriel Cassinelli; Merkaj, Sara; von Reppert, Marc; Baid, Ujjwal; Bakas, Spyridon; Kirsch, Claudia; Davis, Melissa; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Aneja, Sanjay; Memon, Fatima; Aboian, Mariam S.
Afiliación
  • Ramakrishnan D; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA. divya.ramakrishnan@yale.edu.
  • Jekel L; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Chadha S; University of Essen School of Medicine, Essen, Germany.
  • Janas A; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Moy H; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Maleki N; Charité University School of Medicine, Berlin, Germany.
  • Sala M; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Kaur M; Wesleyan University, Middletown, CT, USA.
  • Petersen GC; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Merkaj S; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • von Reppert M; Tulane University School of Medicine, New Orleans, LA, USA.
  • Baid U; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Bakas S; Ludwig Maximilian University School of Medicine, Munich, Germany.
  • Kirsch C; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Davis M; University of Göttingen School of Medicine, Göttingen, Germany.
  • Bousabarah K; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Holler W; Ulm University School of Medicine, Ulm, Germany.
  • Lin M; Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
  • Westerhoff M; University of Leipzig School of Medicine, Leipzig, Germany.
  • Aneja S; Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
  • Memon F; Department of Radiology and Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Aboian MS; Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
Sci Data ; 11(1): 254, 2024 Feb 29.
Article en En | MEDLINE | ID: mdl-38424079
ABSTRACT
Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas Límite: Humans Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas Límite: Humans Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido