Dual-Attention Multimodal Network for Explainable Lung Nodule Malignancy Assessment Through Integration of CT Imaging and Molecular Phase-Separation Biomarkers
Keywords:
lung nodule malignancy, dual-attention network, multimodal fusion, computed tomography, phase separation, explainable AI, clinical decision support, federated learningAbstract
Lung cancer remains the leading cause of cancer mortality worldwide, with low-dose computed tomography screening generating a large number of indeterminate pulmonary nodules that demand precise risk stratification. While deep learning approaches applied to CT images have substantially improved nodule classification, they often lack the molecular contextualization necessary for personalized decision-making and fail to provide actionable explanations for their predictions. Concurrently, molecular biology has uncovered the relevance of biomolecular condensates formed through liquid-liquid phase separation, such as the YAP-MAML2 transcriptional co-activator complexes, as drivers of aggressive tumor phenotypes. This paper proposes a dual-attention multimodal network architecture that integrates volumetric CT imaging with molecular phase-separation biomarkers to deliver explainable malignancy assessment. The system employs a dual-attention mechanism that combines channel-spatial self-attention within the imaging pathway and cross-modal attention between image embeddings and condensate state features, generating spatial attention maps that highlight nodule regions alongside molecular feature importance scores. We discuss system-level structural trade-offs including data harmonization from heterogeneous clinical sources, federated learning strategies to preserve patient privacy, computational sustainability, robustness to domain shift, and fairness across demographic subgroups. The deployment framework is situated within emerging governance standards and regulatory pathways for artificial intelligence as a medical device, emphasizing explainability as a cornerstone for clinical trust. By bridging radiological phenotyping and molecular biophysics, the proposed architecture addresses critical gaps in multimodal fusion for lung cancer diagnostics and charts a course toward integrated precision oncology platforms.
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