Histopathology image embedding based on foundation models features aggregation for patient treatment response prediction (2408.03954v1)

Abstract: Predicting the response of a patient to a cancer treatment is of high interest. Nonetheless, this task is still challenging from a medical point of view due to the complexity of the interaction between the patient organism and the considered treatment. Recent works on foundation models pre-trained with self-supervised learning on large-scale unlabeled histopathology datasets have opened a new direction towards the development of new methods for cancer diagnosis related tasks. In this article, we propose a novel methodology for predicting Diffuse Large B-Cell Lymphoma patients treatment response from Whole Slide Images. Our method exploits several foundation models as feature extractors to obtain a local representation of the image corresponding to a small region of the tissue, then, a global representation of the image is obtained by aggregating these local representations using attention-based Multiple Instance Learning. Our experimental study conducted on a dataset of 152 patients, shows the promising results of our methodology, notably by highlighting the advantage of using foundation models compared to conventional ImageNet pre-training. Moreover, the obtained results clearly demonstrates the potential of foundation models for characterizing histopathology images and generating more suited semantic representation for this task.

• The paper demonstrates that aggregating foundation model features boosts prediction accuracy of cancer treatment response, achieving up to a 7.6% ROC AUC gain.
• It employs a novel methodology combining local and global feature extraction using attention-based Multiple Instance Learning on whole slide images.
• The study highlights the potential of AI in clinical decision-making while addressing challenges such as accurately identifying negative responders.

Predicting Cancer Treatment Response Using Histopathology Image Embedding via Foundation Models

The prediction of patient response to cancer treatments presents a significant challenge in medical research, particularly for conditions such as Diffuse Large B-Cell Lymphoma (DLBCL). This paper addresses this challenge by leveraging advancements in the domain of artificial intelligence and machine learning, specifically through the use of foundation models. The authors propose a methodology that utilizes features extracted from whole slide images (WSIs) to predict treatment response, thereby offering potential improvements in clinical decision-making.

Foundation models have garnered attention for their ability to process large-scale, unlabeled datasets through self-supervised learning (SSL). This paper demonstrates the application of such models to the specific problem of DLBCL treatment response prediction. The core methodology of this work involves using pre-trained foundation models as feature extractors to build both local and global representations of WSIs. Local representations are created for smaller regions of tissue, which are then aggregated into a global representation using attention-based Multiple Instance Learning (MIL).

The experimental paper, conducted on a dataset of 152 DLBCL patients, showed that using foundation models provided a distinct advantage compared to traditional approaches such as ImageNet pre-trained models. Notably, by aggregating various foundation models, the methodology captures a richer and more informative semantic representation of histopathology images. This is evidenced by notable metrics such as a ROC AUC gain of up to 7.6% over models that do not use foundation models.

The paper's experimental results emphasize a significant finding: leveraging foundation models in computational pathology tasks improves the efficiency of histopathology image characterization. This potential is largely due to the ability of foundation models to encapsulate a diverse range of representations that are crucial for effective tissue analysis. Despite the promising results, the paper acknowledges ongoing challenges, particularly in identifying negative responders, which points towards a need for more representative datasets and the refinement of class balance techniques.

Practically, the proposed methodology holds promise for enhancing early-phase treatment planning by allowing clinicians to adjust treatments according to a patient's predicted response. Theoretically, this paper adds value to ongoing discussions about the applicability of foundation models in medical imaging, highlighting their superiority over conventional image classification models for certain tasks in computational pathology.

Looking forward, there are clear avenues for future work. The exploration of advanced aggregation methods and the development of new foundation models remain of interest, as they may further improve model performance. Additionally, expanding the dataset to include diverse demographics and a wider range of tissue types will enhance the robustness and generalizability of the predictions.

In summary, this paper contributes to the understanding of how foundation models can be effectively applied in medical image analysis, specifically in predicting cancer treatment responses. It provides a detailed experimental validation of their advantages over traditional pre-trained models and sets the stage for continued research in refining and adopting these models in clinical practice.