Multimodal adversarial representation learning for breast cancer prognosis prediction

Area of Science:

• Oncology
• Bioinformatics
• Machine Learning

Background:

• Accurate breast cancer prognosis is vital for patient care and clinical decisions.
• Integrating diverse data modalities (gene expression, clinical, copy number alteration) improves prediction but faces challenges in reducing modality gaps.
• Existing methods often fail to effectively align multimodal data distributions, limiting prognostic performance.

Purpose of the Study:

• To develop a novel framework that effectively integrates multimodal data for improved breast cancer prognosis prediction.
• To address the challenge of modality gap by creating a modality-invariant embedding space.
• To enhance feature expression and prognostic accuracy using advanced deep learning techniques.

Main Methods:

• Proposed a multimodal data adversarial representation framework (MDAR) to translate source modalities into target modality distributions, reducing heterogeneity.
• Applied reconstruction and classification losses to the embedding space for further constraint.
• Designed a multi-scale bilinear convolutional neural network (MS-B-CNN) for enhanced uni-modality feature expression.
• Utilized an extremely randomized trees classifier with stacked features from the embedding space for prediction.

Main Results:

• The proposed MDAR framework demonstrated improved prognostic performance validated by 10-fold cross-validation.
• Comparative analysis on the METABRIC dataset (1980 patients) showed a significant 7.4% enhancement in Matthews correlation coefficient (Mcc).
• The method effectively reduced the modality gap, leading to more accurate breast cancer prognosis predictions.

Conclusions:

• The multimodal data adversarial representation framework (MDAR) offers a significant advancement in breast cancer prognosis prediction.
• Effective integration of heterogeneous data through a modality-invariant embedding space is crucial for improving accuracy.
• This approach holds promise for enhancing clinical decision-making and patient management in oncology.