Decoding Brain Tumors: Unleashing the Power of Deep Learning to Grade Gliomas with Precision and Insightful Tumor Microenvironment Analysis what did we do

Unraveling the Intricacies with Deep Learning and Tumor Microenvironment Analysis. We investigated different deep learning architectures and approaches for a more efficientdiagnoses e.g. single slide or single cell, offering insights into glioma characteristics .
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Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features - Journal of Imaging Informatics in Medicine

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset — 206 images of 5 classes — as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline’s accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists. Graphical Abstract

Gliomas, primary brain tumors originating from neural stem cells or glial precursors, pose diagnostic challenges addressed by innovative methodologies. Employing deep learning for automated multiclass tumor grading, our study introduces a protocol exploring tumor microenvironment elements. Despite dataset constraints, image augmentation mitigated imbalances, enhancing accuracy by 9%. The DenseNet121 architecture outperformed, reaching 69%, particularly in WHO grade 2 and 3 cases.

Microenvironment analysis underscored myeloid cells' significance, offering potential diagnostic enhancements for intraoperative evaluations and treatment selection, streamlining workflows for pathologists and oncologists.

In the picture above you can see different tumor grades. However, it is not always so easy and even pathologists  have difficulties. 

Moreover, if for a second we put aside the classification purpose, and we look carefully what make us differentiating patches of slides of higher grades we found something related to the myeling cells, those neighborhoods are shown below:

Code and used data are available here:

https://github.com/octpsmon/TME_analysis_protocol_n_glioma_grading 

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Pathology
Life Sciences > Health Sciences > Clinical Medicine > Diagnosis > Pathology
Computational Biology
Mathematics and Computing > Mathematics > Applications of Mathematics > Computational Biology
Machine Learning
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Image Processing
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