A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.

Journal: Scientific data
PMID:

Abstract

Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.

Authors

  • Beatriz Ocaña-Tienda
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain. beatriz.ocana@uclm.es.
  • Julián Pérez-Beteta
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
  • José D Villanueva-García
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
  • José A Romero-Rosales
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
  • David Molina-García
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
  • Yannick Suter
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. yannick.suter@artorg.unibe.ch.
  • Beatriz Asenjo
    Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain.
  • David Albillo
    Radiology Department, MD Anderson Cancer Center, Madrid, Spain.
  • Ana Ortiz de Mendivil
    Radiology Department, Sanchinarro University Hospital, HM Hospitales, Madrid, Spain.
  • Luis A Pérez-Romasanta
    Radiation Oncology Department, Hospital Universitario de Salamanca, Salamanca, Spain.
  • Elisabet González-Del Portillo
    Radiation Oncology Department, Hospital Universitario de Salamanca, Salamanca, Spain.
  • Manuel Llorente
    Radiology Department, MD Anderson Cancer Center, Madrid, Spain.
  • Natalia Carballo
    Radiology Department, MD Anderson Cancer Center, Madrid, Spain.
  • Fátima Nagib-Raya
    Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain.
  • Maria Vidal-Denis
    Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain.
  • Belén Luque
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
  • Mauricio Reyes
    Center for Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland.
  • Estanislao Arana
    Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
  • Víctor M Pérez-García
    Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.