Brain tumour histopathology through the lens of deep learning: A systematic review.

Problem: Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more complex diseases, but it has been used relatively little in Glioblastoma (GBM) histopathological studies, which could benefit greatly due to the disease's complex pathogenesis.

Aim: Conduct a systematic review to investigate how ML/DL techniques have influenced the progression of brain tumour histopathological research, particularly in GBM.

Methods: 54 eligible studies were collected from the PubMed and ScienceDirect databases, and their information about the types of brain tumour/s used, types of -omics data used with histopathological data, origins of the data, types of ML/DL and its training and evaluation methodologies, and the ML/DL task it was set to perform in the study were extracted to inform us of trends in GBM-related ML/DL-based research.

Quantification of tumorsphere migration with a physics-based machine learning method.

Current analysis techniques available for migration assays only provide quantitative measurements for overall migration. However, the potential of regional migration analyses can open further insight into migration patterns and more avenues of experimentation with the same assays. Previously, we developed an analysis pipeline utilizing the finite element (FE) method to show its potential in analyzing glioblastoma (GBM) tumorsphere migration, especially in characterizing regional changes in the migration pattern.

Extent of resection affects prognosis for patients with glioblastoma in non-eloquent regions.

Glioblastoma (GBM) is a malignant cerebral neoplasm carrying poor prognosis. The importance of extent of resection (EoR) in GBM patient outcomes has been argued in the literature. Previous studies included tumors in eloquent regions of the brain.