An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Journal: Cell reports. Medicine
PMID: 38471504

Abstract

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

Authors

  • Alonso Garcia-Ruiz
    Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain.
  • Albert Pons-Escoda
    Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
  • Francesco Grussu
    University College London, London, United Kingdom.
  • Pablo Naval-Baudin
    Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain.
  • Camilo Monreal-Aguero
    Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain.
  • Gretchen Hermann
    Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
  • Roshan Karunamuni
    Department of Radiation Medicine and Applied Sciences, University of California San Diego, 3960 Health Sciences Dr, Mail Code 0865, La Jolla, CA, USA.
  • Marta Ligero
  • Antonio Lopez-Rueda
    Radiology Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain.
  • Laura Oleaga
    Department of Radiology, Clinical Diagnostic Imaging Centre, Hospital Clínic de Barcelona, Barcelona, Spain.
  • M Alvaro Berbís
    Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain; Faculty of Medicine, Autonomous University of Madrid, Madrid, Spain. Electronic address: a.berbis@htime.org.
  • Alberto Cabrera-Zubizarreta
    Radiology Department, HT Medica, 23008 Jaen, Spain.
  • Teodoro Martín-Noguerol
    MRI Unit, Radiology Department, HT médica Carmelo Torres 2, Jaén 23007, Spain. Electronic address: t.martin.f@htime.org.
  • Antonio Luna
    MRI Unit, Radiology Department, Health Time, Jaén, Spain. Electronic address: aluna70@htime.org.
  • Tyler M Seibert
    Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Radiology Department, University of California, San Diego, La Jolla, CA 92093, USA; Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA.
  • Carlos Majos
    Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain.
  • Raquel Perez-Lopez
    Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.

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