Multimodal Prototyping for cancer survival prediction (2407.00224v1)

Abstract: Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokenizing the WSIs into smaller patches (>10,000 patches) and transcriptomics into gene groups, which are then integrated using a Transformer for predicting outcomes. However, this process generates many tokens, which leads to high memory requirements for computing attention and complicates post-hoc interpretability analyses. Instead, we hypothesize that we can: (1) effectively summarize the morphological content of a WSI by condensing its constituting tokens using morphological prototypes, achieving more than 300x compression; and (2) accurately characterize cellular functions by encoding the transcriptomic profile with biological pathway prototypes, all in an unsupervised fashion. The resulting multimodal tokens are then processed by a fusion network, either with a Transformer or an optimal transport cross-alignment, which now operates with a small and fixed number of tokens without approximations. Extensive evaluation on six cancer types shows that our framework outperforms state-of-the-art methods with much less computation while unlocking new interpretability analyses.

• The paper proposes a dual prototyping approach that summarizes gigapixel histology images and transcriptomic profiles for efficient and interpretable cancer survival predictions.
• It employs a Gaussian mixture model to achieve over 300× compression on image data while using biological pathway prototypes to transform gene expression data.
• Extensive evaluations on six TCGA cancer types show superior predictive accuracy and lower computational demand compared to state-of-the-art techniques.

Multimodal Prototyping for Cancer Survival Prediction

The paper explores a novel multimodal method for predicting cancer survival by integrating gigapixel histology whole-slide images (WSIs) and transcriptomic profiles. The contemporary approach involves segmenting WSIs into a multitude of smaller patches (exceeding $10^4$ patches) and transcriptomics data into gene groups, subsequently leveraging a Transformer to predict outcomes. However, this method is notably resource-intensive and complicates interpretability analyses due to the large number of tokens. In contrast, the proposed method hypothesizes that a more efficient summarization can be achieved by condensing WSIs through morphological prototypes and characterizing transcriptomic profiles with biological pathway prototypes, both in an unsupervised manner.

Key Contributions

1. Morphological Prototyping: Here, the authors employ morphological prototypes to summarize WSI histology patches, implementing a Gaussian mixture model (GMM) that achieves over $300\times$ compression. This significantly condenses the data by reducing redundancy in morphological information.
2. Pathway Prototyping: Transcriptomic profiles are characterized using biological pathway prototypes based on established cellular functions. This approach leverages pre-existing biological knowledge, enabling an unsupervised transformation of gene expression data into pathway-level summaries.
3. Efficient Token Fusion: A multimodal fusion network processes these summarized tokens, which is implemented using either a Transformer or optimal transport cross-alignment. Due to the reduced number of tokens, this method avoids computational approximations, rendering it more efficient and interpretable.
4. Extensive Evaluation and Performance: The method has been thoroughly evaluated on six cancer types from The Cancer Genome Atlas (TCGA), showing superior performance compared to state-of-the-art methods while demanding significantly fewer computational resources (Giga-FLOPs). The integration provides both predictive accuracy and novel interpretability analyses.

Practical and Theoretical Implications

This work holds substantial implications in both practical and theoretical domains:

• Enhanced Efficiency and Scalability: By reducing the volume of tokens via prototyping, it addresses the large-p (large input dimensionality), small-n (small sample size) issue, ensuring that the model remains computationally feasible even for small cohorts.
• Interpretability: The summarization through prototypical tokens facilitates post-hoc interpretable analyses. For example, visualizing the top-10 pathways that interact with a particular morphological prototype enables deeper insights into the biological underpinnings of disease progression.
• Future Methodologies in Multimodal Fusion: The paper proposes a unified framework incorporating both Transformer-based cross-attention and optimal transport-based cross-alignment. This unification broadens the spectrum of applicable multimodal fusion strategies, paving the way for future innovations that may combine or reconfigure these methods to suit varied applications.

Speculation on Future Developments in AI

Looking forward, this framework could evolve in numerous ways:

1. Adaptive Prototyping: Future models could implement data-driven methods to dynamically determine the number of prototypes rather than pre-specifying them. This could be achieved using frameworks such as Dirichlet processes.
2. Incorporation of Single-cell Data: Advanced models could incorporate single-cell RNA sequencing data, leveraging single-cell foundation models that have recently demonstrated substantial capacity in capturing cellular heterogeneity.
3. Broader Clinical Applications: Beyond predicting survival, similar methods could be applied to other clinical endpoints such as recurrence risk and progression-free intervals. These could provide invaluable tools for personalized medicine.

Conclusion

In sum, the incorporation of multimodal prototyping as outlined in this paper presents a significant step towards more efficient, scalable, and interpretable cancer prognosis models. Through the innovative condensation and characterization of histology and transcriptomic data, this approach has not only enhanced predictive accuracy but also provided a more interpretable basis for understanding interactions between distinct biomedical modalities. Future developments will likely expand on this foundation, incorporating adaptive and individualized elements to further push the boundaries of AI in clinical research.