Multi-scale foundational AI descriptors enable accurate tumor localization in digitized renal cell carcinoma pathology

Background: Renal cell carcinoma (RCC) diagnosis relies on accurate tumor localization on pathology whole slide images, but suffers from substantial inter-observer variability, which can impact treatment selection. Recent advances in digital pathology AI tools have resulted in foundation models (FMs) that are trained on diverse H&E images to learn patterns of tissue morphology. However, the performance of these models has been evaluated at a single scale, whereas clinical pathology evaluation leverages multiple scales and magnifications. Our objective was to develop and validate multi-scale pathology FM signatures for tumor localization in renal cancers. Methods: H&E-stained whole slide images of RCC were curated from a public repository. For each case, tissue regions were sampled at three histologic scales (5×, 10×, and 20× magnification). Using the pathology FM MUSK, quantitative representations of tissue morphology were generated from each region and at each scale, and a supervised classifier trained on available pathologist annotations was used to discriminate tumor from non-tumor tissue. A multi-scale signature was constructed by integrating feature representations across all three magnifications for each region. We confirmed our findings using additional FMs (CONCH, HOPTIMUS) and by comparing against models trained at individual magnifications. Results: A total of 118 slides (2 institutions) were included, split by patient into training (n = 94) and hold-out validation (n = 24) sets. Slides were sampled to yield 300,000+ labeled tiles (~112,000 tumor and ~222,000 non-tumor). The multi-scale MUSK model yielded the best overall accuracy for discriminating tumor versus benign tissue in hold-out validation (AUC ~0.95; accuracy ~0.89). By comparison, MUSK models at individual magnifications performed markedly worse (AUC ~0.90 to 0.92; accuracy ~0.82 to 0.85). Models trained using CONCH and HOPTIMUS demonstrated similar trends, with multi-scale models achieving AUC ~0.94 and accuracy ~0.89 that were improved relative to single magnification models (AUC ~0.91, accuracy 0.81-0.84). Conclusions: Integrating foundation model representations across multiple scales and magnifications yields accurate tumor localization of RCC on digital pathology. These findings will be validated in larger RCC cohorts and evaluated for impact on treatment selection.