Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models - Top-Institutions - MDSpire

Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models

  • By

  • Raquel García-Pablo

  • Marta Canela-Capdevila

  • Alberto Martínez-Caballero

  • Rocío Benavides-Villareal

  • Albert Moragas-Fernández

  • Andrea Jiménez-Franco

  • Berta Piqué-Smith

  • Camila Montesinos-Guevara

  • Jordi Camps

  • Jorge Joven

  • Angel Torrado-Carvajal

  • Meritxell Arenas

  • April 30, 2026

  • 0 min

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Top Institutions in Oncology

Brief introduction explaining scope and methodology.

  • #1

    MD Anderson Cancer Center
    MD Anderson Cancer Center

    Houston, TX

    Key Differentiators

    • Radiation Oncology
    • Medical Physics
    • Radiology
    • Artificial Intelligence in Oncology

  • #2

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Radiation Oncology
    • Radiology
    • Computational Oncology

  • #3

    Dana-Farber Cancer Institute / Brigham and Women’s Hospital
    Dana-Farber Cancer Institute / Brigham and Women’s Hospital

    Boston, MA

    Key Differentiators

    • Radiation Oncology
    • Medical Imaging
    • Bioinformatics

  • #4

    University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center

    Dallas, TX

    Key Differentiators

    • Radiation Oncology
    • Medical Physics
    • Machine Learning

  • #5

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Radiation Oncology
    • Biomedical Informatics
    • Artificial Intelligence

Original Source(s)

Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models

  • by Raquel García-Pablo, Marta Canela-Capdevila, Alberto Martínez-Caballero, Rocío Benavides-Villareal, Albert Moragas-Fernández, Andrea Jiménez-Franco, Berta Piqué-Smith, Camila Montesinos-Guevara, Jordi Camps, Jorge Joven, Angel Torrado-Carvajal, Meritxell Arenas

  • April 30, 2026

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