DEPLOYing Deep Learning for Accurate CNS Tumor Diagnostics

DEPLOYing Deep Learning for Accurate CNS Tumor Diagnostics

Table of Contents

Introduction

In the treatment of central nervous system (CNS) tumors, pathological diagnosis is crucial, but traditional methods (such as histopathology) can be inconsistent and subjective. DNA methylation profiling using high-throughput sequencing has the potential to be a valuable tool for the classification of CNS tumors due to the fact that each type of tumor has its own unique methylation signature, which reflects both their origins and the epigenomic changes that occur during tumor growth. These methylation profiles provide a detailed map of molecular features that can aid in making more precise diagnoses, thereby improving treatment outcomes. However, high costs and limited availability make this approach inaccessible in under-resourced diagnostic centers.

To bridge this gap, researchers developed DEPLOY —a deep-learning model that predicts DNA methylation and classifies CNS tumor types using standard histopathology slides. This innovation brings the benefits of methylation-based insights to a wider audience, making sophisticated diagnostic tools available even in resource-constrained settings. Their work, recently published in Nature Medicine, highlights DEPLOY’s ability to provide an integrated approach for CNS tumor classification, assisting pathologists in enhancing diagnostic precision, especially in difficult cases.

Developing DEPLOY: A Multi-Model Approach to CNS Tumor Diagnosis

The study aimed to improve CNS tumor classification using deep learning by combining multiple models for greater accuracy. Authors developed three approaches:

  • An indirect model predicting DNA methylation from H&E-stained slides
  • A direct model classifying tumors from slide images
  • A demographic model using patient information (age, sex, tumor location)

By integrating these models, they created DEPLOY, which outperformed any individual model. The strength of this approach lies in its ability to incorporate diverse data sources, combining visual, molecular, and demographic features to achieve a holistic and precise diagnostic tool.

DEPLOY was trained on 1,796 patients from an in-house cohort and validated on external datasets, including over 2,000 patients from the Digital Brain Tumor Atlas (DBTA), Children’s Brain Tumor Network (CBTN), and NCI (National Cancer Institute)–Prospective cohorts. It accurately predicted DNA methylation, surpassing previous transcriptomics prediction attempts from histopathology slides. This multi-cohort validation not only highlights DEPLOY’s accuracy but also demonstrates its applicability across diverse patient populations, an important factor in real-world clinical use. To test its generalizability, DEPLOY was further validated on external cohorts—CBTN (492 patients) and NCI–Prospective (286 patients)—covering 58 unique tumor types. The model consistently predicted DNA methylation patterns with high accuracy across different patient groups, highlighting its robustness in detecting molecular features of CNS tumors. A pronounced overlap in well-predicted probes was observed among all cohorts, demonstrating the reliability of the model.

DEPLOY’s predictions accurately reflected expected molecular differences between specific CNS tumor types, confirming its ability to identify meaningful distinctions. This means that DEPLOY is not only classifying tumors but also effectively capturing the biological basis underlying different tumor types, making it a powerful tool in understanding tumor biology. Further analysis showed that DEPLOY’s predictions aligned closely with known biological patterns, effectively capturing key tumor characteristics.

Classifying Complex CNS Tumor Cases with DEPLOY’s Precision

DEPLOY’s effectiveness was validated on external datasets, consistently achieving high accuracy: 83% of the time for top one predictions and 93% for top two. When tested on independent cohorts like the Digital Brain Tumor Atlas (DBTA) and Children’s Brain Tumor Network (CBTN), DEPLOY maintained similarly strong performance, highlighting its robustness across diverse patient groups. Notably, its accuracy improved with higher confidence scores, reaching 95% for top one predictions in high-confidence samples.

The model proved particularly adept at classifying complex CNS tumor cases, such as glioblastomas and pilocytic astrocytomas, emphasizing its value in difficult diagnostic situations. In fact, DEPLOY provided more definitive diagnoses compared to traditional methods, significantly impacting clinical decision-making. In a challenging subset from the NCI cohort, DEPLOY correctly refined or changed diagnoses in 96% of cases, demonstrating its potential to improve CNS tumor classification and patient outcomes.

Beyond Classification: DEPLOY Unveils Spatial Diversity in Tumors

DEPLOY not only classifies CNS tumor types but also provides insights into the spatial heterogeneity of tumors by making predictions at the tile level. This tile-level analysis allows DEPLOY to potentially detect multiple subtypes within a single slide, a capability that traditional methylation-based classifiers lack since they analyze samples in bulk without revealing intratumoral diversity.

Authors demonstrated this feature by applying DEPLOY to a dual-genotype oligoastrocytoma. At the tile level, DEPLOY’s predictions aligned well with both histologic and molecular features, effectively capturing tumor heterogeneity. The integrated model correctly identified IDH mutant astrocytoma (A-IDH) and oligodendroglioma (O-IDH), consistent with the known diagnosis. While the indirect model also made accurate predictions, the direct model showed some limitations due to overlapping histologic features between certain tumor types, such as pilocytic astrocytomas and O-IDH. This example underscores the strengths of DEPLOY’s indirect and integrated approaches in providing nuanced insights into complex cases.

Conclusion

DEPLOY represents a transformative advance in CNS tumor diagnostics by significantly enhancing diagnostic accuracy, especially in complex cases that challenge conventional histopathology. Its ability to predict DNA methylation patterns from standard pathology slides bridges a critical gap, making sophisticated molecular insights accessible to a wider range of healthcare settings. This accessibility is particularly impactful in low-resource environments where traditional sequencing-based methods are not feasible. Furthermore, DEPLOY’s capacity to visualize tumor heterogeneity at the tile level introduces a nuanced understanding of intratumoral diversity, enabling more tailored treatment strategies. This approach not only optimizes patient outcomes but also sets a new standard for integrating AI-driven diagnostics in personalized medicine, paving the way for similar models in other fields of oncology.

Outsourcing Bioinformatics Analysis: How Bridge Informatics (BI) Can Help

The development and application of deep learning models like DEPLOY highlight the growing importance of bioinformatics in oncology research. Bridge Informatics (BI) specializes in assisting researchers with complex computational analysis of genomic and histopathological data.  Our team of experienced bioinformaticians can help you:

  • Develop custom image analysis pipelines for histopathology slides.
  • Integrate diverse data sources, such as imaging, genomic, and clinical data, for comprehensive analysis.
  • Validate and deploy deep learning models for accurate and reliable CNS tumor classification.

Click here to contact BI for a free consultation and learn how we can accelerate your CNS tumor research.

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