Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
This addresses the need for better clinical decision-making in oncology, though it appears incremental by building on existing multimodal and explainable AI methods.
This study tackled the problem of predicting pathological response in non-small cell lung cancer patients by proposing a multimodal deep learning framework with explainable AI, resulting in improved predictive accuracy and explainability.
This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small cell lung cancer patients undergoing neoadjuvant therapy. Due to the limitations of existing radiomics and unimodal deep learning approaches, we introduce an intermediate fusion strategy that integrates imaging and clinical data, enabling efficient interaction between data modalities. The proposed Multimodal Doctor-in-the-Loop method further enhances clinical relevance by embedding clinicians' domain knowledge directly into the training process, guiding the model's focus gradually from broader lung regions to specific lesions. Results demonstrate improved predictive accuracy and explainability, providing insights into optimal data integration strategies for clinical applications.