Adaptive Dual-Channel Neural Network with Triangulation Topology Optimization for Kidney Cancer Diagnosis and Surgery Planning Using Clinical Metadata

Heterogeneity in tumor size, kind, and stage makes it difficult to diagnose kidney cancer and prepare for surgery, making the decision between partial & radical nephrectomy more difficult. This study proposes an Adaptive Dual-Channel Pulse-Coupled Neural Network with Triangulation Topology Aggregation Optimizer (ADP-CNN-TTAO) that integrates computed tomography (CT) imaging and clinical metadata for more reliable decision support. Using the publicly available KiTS21 dataset comprising 300 annotated patient cases with diverse tumor subtypes, the method combines Iterative Robust Peak-Aware Guided Filtering (IRPAGF) for CT preprocessing, robust imputation for missing clinical variables, Analytical Clifford Fourier Mellin Transform (ACFMT) for imaging feature extraction, and Steerable Transformers (ST) for metadata representation. Experimental evaluation with cross–validation shows consistently high classification performance across papillary, chromophobe, clear cell, and oncocytoma subtypes, outperforming state-of-the-art baselines. Importantly, tumor volume and stage emerged as key determinants for surgical planning. While results demonstrate strong potential for clinical decision support, the approach requires further validation on multi-center datasets and real-world prospective trials to confirm its generalizability and clinical impact.