A research paper recently published in the journal ScienceAdvances presented a novel deep learning approach to explore computational histopathological signatures prognostic that predict overall survival in high-grade gliomas (HGG), an aggressive brain tumor.
Study: Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning. Image Credit: Tomatheart/Shutterstock.com
This technique focuses on identifying sex-specific histopathological features of the tumor microenvironment (TME) and creating risk profiles tailored to male and female patients. By understanding these sex differences in tumor characteristics and survival outcomes, the researchers aim to guide personalized treatment decisions for patients with HGG.
Advances in HGG Treatment
HGGs are extremely fatal tumors with very low survival rates (approx. 15 to 18 months), even with treatments like surgery, radiotherapy, and chemotherapy. These tumors mainly form in the brain or spinal cord and are among the most common forms of cancer in children. They grow and spread quickly throughout the nervous system, easily invading healthy cells.
Studies have shown that sex differences play a significant role in survival outcomes, with males generally having worse survival rates than females. These differences are linked to factors such as hormonal, metabolic, and immune responses, as well as sex-specific genetic changes and molecular subtypes.
Traditionally, neuropathologists examine hematoxylin and eosin (H&E) stained tissue slides, a time-consuming process that can vary between observers. However, advancements in computational tools and digitized slides have led to the development of deep learning-based pathomics methods, allowing for faster and more accurate analysis of tumor characteristics.
Deep Learning for Analyzing H&E Stained Tissue
In this study, the authors used an end-to-end deep learning framework to analyze H&E-stained tissue slides from surgically removed high-grade glioma tumors. They employed a two-stage process with the residual network with 18-layer (ResNet18) models.
The first stage focused on segmenting viable tumor areas, and the second stage involved building sex-specific models to predict overall survival. Separate models were trained for male and female patients to identify survival-related tumor features. The study analyzed data from 514 patients and validated the models across three independent groups.
The deep learning framework used two convolutional neural networks (CNNs), including ResNet segmentation (ResNetSeg-18) and ResNet with Cox proportional hazards model (ResNet-Cox). The first, ResNetSeg-18, segmented tumor regions. The ResNet-Cox survival model then analyzed these segments to predict survival outcomes.
The ResNet-Cox model was applied both sex-specifically and as a general model for comparison. To improve accuracy, clinical and molecular data such as age, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine deoxyribonucleic acid (DNA) methyltransferase (MGMT) promoter methylation were also included.
Additionally, the models were evaluated using Kaplan-Meier (KM) curves and concordance indices (C-index) across training and validation sets. Furthermore, techniques like t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) were used to highlight tumor features related to survival.
Outcomes of Using Deep Learning Techniques
The study revealed that the sex-specific ResNet-Cox models outperformed the general model in differentiating low- and high-risk groups, showing higher accuracy. The male-specific model showed statistically significant separation in both the training set and independent test groups. High-risk areas in males were associated with features like microvascular proliferation (MVP) and pseudopalisading cells. This model achieved a C-index of 0.712 in the training set, with similar values across test cohorts.
Similarly, the female-specific model demonstrated a strong separation between risk groups, linking high-risk areas to infiltrating tumors and MVP. It showed a C-index of 0.673 for the training set, with consistent results in test cohorts.
Additionally, the authors created visual risk density maps to identify regions within the tumor TME that contributed to survival risk prediction. These maps indicated that in males, high-risk areas were linked to MVP and pseudopalisading cells, while in females, they were associated with infiltrating tumors and MVP. Quantitative analysis confirmed significant differences in tumor features between low- and high-risk groups for both sexes.
The researchers further improved the models by integrating clinical and molecular data, leading to multimodal ResNet-Cox models (mResNet-Cox). These models demonstrated better accuracy and separation between high and low-risk patients.
Key Applications
This research has significant potential for improving personalized treatment plans for patients with HGGs. Identifying sex-specific tumor features linked to survival sets the foundation for developing prognostic models. These models can classify patients into low- and high-risk groups, allowing for more targeted and effective treatment plans.
Deep learning-based analysis also offers a faster, more consistent method for evaluating tumor slides, reducing the time and variability associated with manual assessments. Incorporating genomic biomarkers alongside histopathological data further boosts the accuracy of these prognostic models. This approach could also be applied to other cancers and diseases, making it a valuable asset in precision medicine.
Conclusion and Future Directions
In summary, this research by Verma et al. showed that deep learning can identify sex-specific histopathological features in HGGs and develop predictive models for overall survival. It highlighted the importance of considering sex differences in the analysis and treatment of high-grade gliomas, potentially leading to more personalized and effective treatment strategies.
The development of these sex-specific prognostic models provides a valuable tool for customizing treatment plans and improving survival rates in patients. Future research could focus on incorporating additional clinical and molecular data to enhance the accuracy and effectiveness of these models.
Journal Reference
Verma, R., & et al. Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning. ScienceAdvances, 2024, 10, 34. DOI: 10.1126/sciadv.adi0302, https://www.science.org/doi/10.1126/sciadv.adi0302
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