A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response...

Area of Science:

• Oncology
• Medical Imaging
• Machine Learning

Background:

• Precision oncology requires joint modeling of tumor grading and treatment response using multimodal data.
• Current methods often rely on invasive grading and lack structural constraints in multimodal fusion.
• Independent modeling of grading and response limits clinical utility.

Purpose of the Study:

• To develop a grade-guided multimodal collaborative modeling framework for integrated non-invasive tumor grading and treatment response prediction.
• To enable interpretable mechanism analysis for precision oncology.
• To improve clinical decision-making by addressing limitations of existing approaches.

Main Methods:

• Utilized deep learning models (3D ResNet-18, MLP, CNN-Transformer) for multimodal feature fusion and response modeling.
• Incorporated tumor grading as a weakly supervised prior within a unified framework.
• Employed a grade-guided feature fusion mechanism to emphasize discriminative information.

Main Results:

• Achieved 84.6% accuracy and 0.81 kappa for tumor grading prediction, consistent with pathological grading.
• Attained 0.85 AUC, 0.81 precision, and 0.79 recall for treatment response prediction, outperforming existing models.
• Identified stable treatment-sensitive and resistant subtypes with significant stratification differences.

Conclusions:

• The proposed framework offers an integrated solution for non-invasive grading and treatment response prediction.
• Demonstrated potential for clinical risk assessment and personalized treatment decision-making in oncology.
• Validated the value of grade-guided multimodal fusion for enhanced cancer care.