Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers

Area of Science:

• Oncology
• Biomarker Research
• Medical Imaging

Background:

• Accurate prediction of tumor response to treatment is crucial for clinical decision-making.
• Multimodal biomarkers offer complementary information for improved treatment outcome prognosis compared to unimodal biomarkers.
• Challenges in multimodal data include heterogeneity, incompleteness, small dataset sizes, and class imbalance.

Purpose of the Study:

• To propose a modular framework for cancer treatment outcome prediction using multimodal biomarkers.
• To address challenges associated with multimodal data, such as heterogeneity, incompleteness, and imbalanced datasets.
• To demonstrate the framework's feasibility and advantages in stratifying oropharyngeal squamous cell carcinoma (OPSCC) patients.

Main Methods:

• A four-module framework: synthetic data generation, deep feature extraction, multimodal feature fusion, and classification.
• Utilized positron emission tomography (PET) imaging data and microRNA (miRNA) biomarkers for OPSCC patient stratification.
• Integrated and validated various algorithms within each module of the framework.

Main Results:

• The proposed framework demonstrated superior prognosis performance in stratifying OPSCC patients.
• The framework effectively handled multimodal data heterogeneity and incompleteness.
• Comparative analysis showed the efficiency and advantages of the developed framework over other methods.

Conclusions:

• The modular framework provides an effective approach for cancer treatment outcome prediction using multimodal biomarkers.
• The framework enables seamless integration, validation, and comparison of different algorithms.
• This approach has the potential to significantly enhance clinical decision-making in cancer treatment.