Accurate lung nodule segmentation and malignancy assessment are crucial for early cancer detection and treatment planning but are challenging due to small sizes and diverse shapes. Current methods face challenges due to visual similarity to surrounding structures, weak boundary contrast and treat segmentation and classification separately. To overcome these limitations, this study proposes DPMT-Net, a Dual-Prompt Attention Multi-Task 3D Network that integrates dual prompt encoding for task-specific guidance and clinical feature integration within a unified multi-task architecture for lung nodule segmentation and malignancy classification. The dual-prompt strategy employs a basic encoder with spatial point prompts for segmentation guidance and an enhanced encoder incorporating anatomical embeddings with multi-head self-attention for malignancy and localization predictions, enabling task-adaptive modulation through a shared encoder-decoder backbone. The framework incorporates complementary attention mechanisms for texture-sensitive malignancy assessment and centroid-focused spatial attention for precise localization. Extensive evaluation on the LIDC-IDRI dataset demonstrates that our framework achieves a Dice coefficient of 88.74%, Intersection over Union of 81.21%, malignancy classification accuracy of 82.25% across benign (82.39%), indeterminate (85.11%), and malignant (76.53%) categories, and anatomical localization accuracy of 98.05%. These results confirm the effectiveness of our prompt-guided multi-task approach in providing anatomically consistent, clinically interpretable, and accurate lung nodule analysis.
Sutradhar et al. (Wed,) studied this question.