Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol - Scorecard - MDSpire
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Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol
Clinical Scorecard: Utilizing Machine Learning and Deep Learning Techniques on MRI for Glioblastoma Segmentation and Recurrence Prediction: A Multicenter Retrospective Study Protocol
At a Glance
Category
Detail
Condition
Glioblastoma (GB)
Key Mechanisms
Machine Learning (ML) and Deep Learning (DL) for MRI data analysis and tumor segmentation
Target Population
Patients with glioblastoma undergoing preoperative assessment
Care Setting
Multicenter retrospective study in neurosurgery units
Key Highlights
GB has high recurrence rates and limited survival.
Local recurrence typically occurs within 2 cm of the resection cavity.
AI-based models aim to predict the extent of tumor recurrence spread.
A semi-automatic segmentation tool will standardize tumor volume measurement.
The study will integrate clinical, imaging, and instrumental data for predictive modeling.
Guideline-Based Recommendations
Diagnosis
Utilize MRI features for preoperative assessment of glioblastoma.
Management
Tailor surgical resection strategies based on tumor location and predicted recurrence.
Monitoring & Follow-up
Assess model performance through various metrics including AUC and Dice score.
Risks
Consider the risk of postoperative neurological deficits based on surgical approach.
Patient & Prescribing Data
Patients with glioblastoma undergoing surgical intervention.
Personalized treatment strategies may improve outcomes based on predicted recurrence patterns.
Clinical Best Practices
Incorporate AI tools for predicting tumor recurrence in clinical decision-making.
Ensure accurate anatomical localization of tumors during preoperative planning.
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
