Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning,...

Area of Science:

• Neuro-oncology
• Computational Pathology
• Immunology

Background:

• Gliomas are primary brain tumors requiring accurate grading for prognosis and treatment.
• Myeloid cells in the tumor microenvironment correlate with glioma malignancy and patient survival.
• Manual histological evaluation of glioma grades is time-consuming and subjective.

Purpose of the Study:

• To develop and evaluate deep learning models for automated multiclass classification of glioma grades.
• To investigate tumor microenvironment characteristics, particularly myeloid cell patterns, for glioma grading.
• To assess the utility of computational pathology as a diagnostic aid for brain tumors.

Main Methods:

• Implemented a deep learning protocol for learning, discovering, and quantifying tumor microenvironment elements on a glioma dataset.
• Utilized image augmentation to address data imbalance and small dataset size (206 images, 5 classes).
• Evaluated whole slide supervised learning classification using 6 distinct model architectures, including DenseNet121, and performed unsupervised cell-to-cell analysis.

Main Results:

• The DenseNet121 architecture achieved 69% accuracy, a 9% improvement over baseline, particularly for challenging WHO grade 2 and 3 gliomas.
• Cross-validation was employed for all experiments.
• Tumor microenvironment analysis highlighted the role of myeloid cells in characterizing glioma grades.

Conclusions:

• Deep learning approaches, specifically DenseNet121, offer a promising tool for accurate glioma grading.
• Analysis of the tumor microenvironment, focusing on myeloid cells, provides valuable insights into glioma characteristics.
• These computational methods can enhance diagnostic accuracy and streamline workflows for pathologists and oncologists.