Deep Learning for Glioblastoma Subtyping: A Technical overview
Glioblastoma (GBM) is an aggressive brain cancer requiring precise diagnosis and treatment strategies. Recent advances in deep learning offer powerful tools for analyzing whole slide images (WSIs) of tumor tissue,enabling more accurate subtyping and potentially improved patient outcomes. This article details the computational methods and resources utilized in a recent study focused on leveraging these technologies.
Data Acquisition and Preparation
A robust dataset is crucial for training and validating any machine learning model. This research employed a comprehensive collection of WSIs, categorized by IDH1 mutation status - a key biomarker in GBM.
* A total of 1396 slides were used, split into training (837 slides) and testing (559 slides) sets.
* The training set comprised 425 slides with IDH1 mutation and 698 slides without IDH1 mutation.
* An external cohort from EBRAINS provided independent validation data, including 333 slides with IDH1 mutation and 540 slides without.
This diverse dataset ensures the model’s generalizability and reliability.
Software and Hardware infrastructure
Accomplished deep learning projects rely on a well-defined software stack and sufficient computational resources. Here’s a breakdown of the tools and hardware used in this study:
Programming Language: Python (version 3.9.16) served as the foundation for all experiments and analyses.
Deep Learning Framework: PyTorch (version 2.0.1, CUDA 11.8) was employed for building, training, and deploying the deep learning models.
Model Architectures: Existing, publicly available implementations were adapted and refined.
* iBOT (http://github.com/bytedance/ibot) was modified for the TITANV model.
* CoCa (http://github.com/mlfoundations/open_clip) formed the basis for the TITAN model.
Hardware:
* Training: 4x and 8x NVIDIA A100 80GB GPUs were utilized for TITANV and TITAN training, respectively, leveraging distributed data parallelism.
* Downstream Analysis: A single NVIDIA 3090 24GB GPU was sufficient for all subsequent experiments.
WSI Processing: OpenSlide (version 4.3.1), openslide-python (version 1.2.0), and CLAM (http://github.com/mahmoodlab/CLAM) facilitated the processing of the large WSI files.
Additional Libraries:
* Scikit-learn (version 1.2.2) provided the *k*-Nearest Neighbors algorithm.
* LGSSL codebase (http://github.com/mbanani/lgssl) offered logistic regression and SimpleShot implementations.
* Scikit-survival (Version 0.23.1) was used for survival analysis.
* GigaPath (http://github.com/prov-gigapath/prov-gigapath), PRISM (https://huggingface.co/paige-ai/Prism), and CHIEF (http://github.com/hms-dbmi/CHIEF) were benchmarked as choice slide encoders.
* CLAM codebase (
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