A mathematical framework for pre-mass detection of renal tissue anomalies from computed tomography scans: exploration of a biomechanical signature based on enhancement kinetics.

BACKGROUND: Early detection of renal cancer remains challenging, as current imaging techniques predominantly identify tumors only after a visible mass has formed. Lesions smaller than 1 cm are particularly difficult to detect reliably using routine computed tomography (CT). This study presents a novel mathematical framework for detecting early renal tissue anomalies before visible mass formation, grounded in the hypothesis that tumorigenesis induces subtle biomechanical alterations in tissue enhancement patterns that are observable on CT imaging.

METHODS: We introduced the Local Biomechanical Index (LBI), a voxel-wise metric quantifying local deviations in Hounsfield unit values relative to an intra-patient renal reference, and a volumetric enrichment ratio (Rt/g) measuring the spatial concentration of extreme LBI values. The single-phase framework was evaluated on a random sample of 20 cases from the KiTS19 dataset using fixed parameters: a 3-mm local radius, the 99th percentile threshold, a 10 mm × 10 mm × 10 mm three-dimensional (3D) window, and an enrichment threshold of 2.0. Voxel-level recall, precision, F1-score, and Rt/g were calculated against the reference tumor masks and summarized using means, standard deviations (SDs), and 95% confidence intervals (CIs).

RESULTS: Across the 20 cases, the mean recall was 0.575 (SD, 0.231; 95% CI: 0.474-0.676), the mean precision was 0.449 (SD, 0.292; 95% CI: 0.321-0.577), and the mean F1-score was 0.431 (SD, 0.221; 95% CI: 0.334-0.528). The mean Rt/g was 5.223 (SD, 5.427; 95% CI: 2.842-7.604). Recall exceeded 0.50 in 11 of 20 cases (55%), while Rt/g exceeded 2.0 in 14 cases (70%), indicating that candidate anomalies were frequently concentrated within tumor-associated regions. Because KiTS19 provides tumor annotations but does not annotate all other renal abnormalities, the reported precision represents agreement with the reference tumor masks rather than definitive precision for detecting all renal anomalies.

CONCLUSIONS: These findings provide preliminary evidence that early tumor-related biomechanical alterations may be detectable through spatial analysis of enhancement deviations in CT images. The proposed framework operates without tumor segmentation, relies on interpretable statistical measures, and can be fully automated. Although further validation on large longitudinal datasets is required, this approach opens a promising research direction for segmentation-free early detection of renal cancer and for integrating biomechanical signatures into radiological screening workflows.