Measuring Subtle HD Data Representation and Multimodal Imaging Phenotype Embedding for Precision Medicine.

Area of Science:

• Medical Imaging Analysis
• Biomedical Data Science
• Machine Learning in Healthcare

Background:

• High-dimensional (HD) imaging biomarkers enhance characterization but pose system performance challenges due to their abundance.
• Dimensionality reduction techniques are crucial for managing HD data, especially in multimodal imaging scenarios.
• Existing methods may not optimally preserve critical information during the projection from HD to lower-dimensional (LD) space.

Purpose of the Study:

• To develop and evaluate novel embedding techniques for transforming HD imaging biomarkers into LD representations.
• To address the challenge of data abundance in HD imaging while preserving essential phenotypic information.
• To improve the performance of disease diagnosis and patient stratification using multimodal imaging data.

Main Methods:

• Modified Isomap algorithm incorporating Parzen-Rosenblatt density function for uniform graph projection.
• Development of Gaussian and Kaniadakis entropy-driven (κ-Gaussian) embedding methods for multimodal phenotypic biomarker interaction.
• Comprehensive testing on diverse multimodal imaging datasets (CT, PET, X-ray, MRI, ultrasound, thermography) from 5158 cases across multiple diseases.

Main Results:

• The proposed embedding methods effectively transformed HD to LD attributes, retaining vital information and enabling phenotypic interactions.
• Achieved high diagnostic accuracies, including up to 78.5% for lung cancer, 88.4% for pneumonia, and 82.9% for thermography-based disease detection.
• κ-Gaussian and Gaussian embedding demonstrated improved accuracies, particularly for lung cancer (up to 80.4%) and glioblastoma (up to 65.01%).
• Survival models and Kaplan-Meier curves confirmed the ability of embedding approaches to distinguish patient outcomes, highlighting preserved HD multimodal imaging characteristics.

Conclusions:

• Novel embedding techniques successfully reduce the dimensionality of HD imaging biomarkers while preserving critical information.
• These methods enhance disease classification accuracy and demonstrate potential for patient stratification in precision medicine.
• The preservation of multimodal imaging characteristics is key for advancing diagnostic and prognostic capabilities in various diseases.