Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
Summary: arXiv:2604.16104v1 Announce Type: cross
Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Despite advancements in imaging technologies, conventional computed tomography (CT) imaging often faces challenges in accurately distinguishing between benign and malignant lesions. Furthermore, it lacks the capacity to provide interpretable diagnostic insights. To tackle these limitations, a recent study proposes a dual-modal artificial intelligence (AI) framework that integrates CT radiology with hematoxylin and eosin (H&E) histopathology for improved lung cancer diagnosis and subtype classification.
This innovative AI system employs convolutional neural networks (CNNs) to extract critical radiologic and histopathologic features. By incorporating clinical metadata, the framework enhances its robustness and reliability in diagnosis, ultimately aiming to support clinicians in making informed decisions.
Key Features of the Proposed AI Framework
- Integration of Modalities: The system fuses predictions from both CT imaging and histopathology using a weighted decision-level integration mechanism. This approach classifies various lung cancer subtypes, including:
- Adenocarcinoma
- Squamous Cell Carcinoma
- Large Cell Carcinoma
- Small Cell Lung Cancer
- Normal Tissue
- Explainable AI Techniques: To ensure interpretability, the framework utilizes several explainable AI techniques, such as:
- Grad-CAM
- Grad-CAM++
- Integrated Gradients
- Occlusion
- Saliency Maps
- SmoothGrad
Experimental Results
The experimental results of this dual-modal framework demonstrate impressive performance metrics, including:
- Accuracy: Up to 0.87
- Area Under the Receiver Operating Characteristic (AUROC): Above 0.97
- Macro F1-Score: 0.88
Among the explainable AI techniques, Grad-CAM++ achieved the highest levels of faithfulness and localization accuracy, showing a strong correspondence with expert-annotated tumor regions. This capability is crucial as it not only enhances diagnostic accuracy but also builds trust among clinicians who rely on AI-assisted technologies.
Implications for Clinical Decision Support
The findings from this study indicate that the multimodal integration of radiology and histopathology can significantly improve diagnostic performance while maintaining transparency in AI decision-making processes. This capability suggests a promising future for clinical decision support systems in precision oncology.
As healthcare continues to evolve with technology, the integration of such advanced AI frameworks may lead to more effective and personalized treatment strategies for lung cancer patients, ultimately contributing to better patient outcomes.