AI Medical Compendium Journal:
Neuro-oncology advances

Showing 1 to 4 of 4 articles

A machine learning-assisted systematic review of preclinical glioma modeling: Is practice changing with the times?

Neuro-oncology advances
BACKGROUND: Despite improvements in our understanding of glioblastoma pathophysiology, there have been no major improvements in treatment in recent years. Animal models are a vital tool for investigating cancer biology and its treatment, but have kno...

Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.

Neuro-oncology advances
BACKGROUND: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when...

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Neuro-oncology advances
BACKGROUND: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilize...

Radiomic analysis of magnetic resonance imaging predicts brain metastases velocity and clinical outcome after upfront radiosurgery.

Neuro-oncology advances
BACKGROUND: Brain metastasis velocity (BMV) predicts outcomes after initial distant brain failure (DBF) following upfront stereotactic radiosurgery (SRS). We developed an integrated model of clinical predictors and pre-SRS MRI-derived radiomic scores...