Molecular-informed image classification for predicting drug sensitivity in cancer therapy.

Introduction: Understanding and predicting drug sensitivity in cancer therapy demands innovative approaches that integrate multi-modal data to enhance treatment efficacy. In alignment with the advancing scope of precision oncology and the molecularly informed therapeutic decision-making emphasized by contemporary cancer research, this work proposes a dynamic and structure-aware imaging framework for robust molecular-informed image classification. Traditional methodologies often suffer from rigid modeling assumptions and inadequate handling of complex, heterogeneous noise prevalent in biological imaging, which limits their predictive accuracy and generalizability. Methods: To address these challenges, we introduce a novel dynamic structure-aware imaging network (DSINet) coupled with a progressive structure-guided optimization (PSGO) strategy. DSINet dynamically adapts spatial filters based on local molecular content, preserves critical biological structures through attention mechanisms, and incorporates uncertainty-aware fusion across multiple resolutions. PSGO further refines the reconstruction by progressively focusing optimization on high-confidence regions and adaptively restructuring feature graphs to enhance robustness against variable imaging artifacts. Results and Discussion: Extensive experimental evaluations demonstrate that our method significantly outperforms techniques in classifying molecular patterns correlated with drug sensitivity, offering a reliable and interpretable foundation for advancing personalized cancer therapy strategies. This approach seamlessly integrates cutting-edge adaptive imaging models with the emerging needs of molecular-insight-driven therapeutic optimization, bridging critical gaps in current cancer informatics research.