Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol - Takeaways - MDSpire

Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol

  • By

  • Luana Conte

  • Erica Lo Turco

  • Rosaria V. Abbritti

  • Caterina Accettura

  • Giuseppe Raso

  • Edvige Iaboni

  • Ugo De Giorgi

  • Giorgio De Nunzio

  • Donato Cascio

  • Maria Caffo

  • July 10, 2026

  • 0 min

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  • 1

    The study focuses on glioblastoma (GB), an aggressive brain tumor with high recurrence rates and limited survival.

  • 2

    Machine Learning (ML) and Deep Learning (DL) techniques will be applied to MRI data to predict recurrence spread in GB patients.

  • 3

    A multicenter retrospective collection of clinical and radiological variables will be performed for eligible GB patients.

  • 4

    The study aims to develop a semi-automatic segmentation tool for tumor delineation in MRI to standardize volume measurement.

  • 5

    Model performance will be assessed using various metrics, including Area Under the Curve (AUC) and Dice score for segmentation.

Original Source(s)

Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol

  • by Luana Conte, Erica Lo Turco, Rosaria V. Abbritti, Caterina Accettura, Giuseppe Raso, Edvige Iaboni, Ugo De Giorgi, Giorgio De Nunzio, Donato Cascio, Maria Caffo

  • July 10, 2026

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