NEXT-GENERATION AI-DRIVEN FRAMEWORKS FOR EARLY LUNG CANCER DETECTION USING ADVANCED IMAGING PROCESSING AND PREDICTIVE MODELING TECHNIQUES

Abstract

Early detection of lung cancer remains a critical determinant of patient survival, yet current clinical screening practices continue to face major challenges arising from heterogeneous image acquisition protocols, subtle lesion morphology, and significant inter-reader variability. To address these limitations, this study introduces next-generation AI-driven frameworks that integrate advanced imaging processing pipelines with predictive modeling techniques for robust and clinically actionable early lung cancer detection. The framework begins with physics-informed harmonization strategies combining denoising, super-resolution, and cross-protocol domain adaptation to generate standardized inputs that preserve diagnostically salient textures across diverse scanner vendors and acquisition settings. Building on these pre-processed datasets, a multi-scale nodule discovery module integrates 3D convolutional encoders with transformer-based detectors and topology-aware segmentation, enabling accurate characterization of challenging sub-centimeter, subsolid, and juxta-pleural nodules. Beyond detection, the framework advances malignancy risk prediction through the fusion of radiomics and deep learning embeddings, which synergistically combine handcrafted intensity, shape, and texture descriptors with self-supervised representations to enhance data efficiency and generalizability across sites. Multimodal graph transformers are then employed to incorporate imaging findings with clinical risk factors such as age, smoking history, and comorbidities, as well as longitudinal biomarkers including attenuation changes and nodule growth kinetics. This integration produces time-aware malignancy risk scores that are rigorously calibrated through conformal prediction, thereby providing trustworthy uncertainty estimates essential for clinical decision support. Training is guided by curriculum learning strategies and distributionally robust optimization objectives that account for institutional imbalances, protocol drift, and data scarcity. Evaluation across multi-institutional datasets and public benchmarks demonstrates consistent improvements in sensitivity for nodules ≤6 mm, reduction of false positives per study, superior calibration performance, and tangible reductions in time-to-diagnosis when compared with existing guideline-based criteria. Furthermore, decision-curve analysis highlights the clinical utility of the proposed framework in reducing unnecessary follow-ups while maintaining high early-stage detection rates. To support clinical adoption, the study incorporates governance overlays addressing algorithmic fairness, privacy-preserving federated learning, and interpretable evidence generation through saliency mapping and radiomics attribution. By embedding these governance elements within a reader-in-the-loop design, the framework not only enhances trustworthiness but also promotes sustainable deployment in real-world screening ecosystems. Overall, this next-generation AI-driven approach demonstrates the potential to significantly improve early lung cancer detection, foster equitable care, and accelerate translation of AI innovations into clinical practice.