3D Banff Lesion Scoring for Kidney Transplant Pathology: Feasibility and Utility for Volumetric Quantification

Yanfan Zhu, Juming Xiong, Junchao Zhu, Ruining Deng, Shilin Zhao, Yu Wang, Yaohong Wang, Chongyu Qu, Zhengyi Lu, Yuechen Yang, Mengmeng Yin, Yuqing Liu, Yihan Wang, Andrew J. Rauch, Haichun Yang, Yuankai Huo

Keywords: Banff Classification, Kidney Transplant Pathology, Deep Learning, Digital Pathology

TL;DR: We demonstrate a pilot 3D Banff glomerulitis scoring that reconstructs and tracks glomeruli across serial sections, yielding more consistent scores and stronger clinical associations than conventional 2D assessment.

Abstract: Quantifying glomerular inflammation in kidney-transplant biopsies is traditionally performed on single whole-slide sections using the Banff “most-severe section” rule, despite the inherently three-dimensional nature of lesion distribution. This 2D based assessment might lead to instability when inflammation varies across slices. This paper reports a pilot study examining the technical feasibility and clinical relevance of extending Banff scoring into three dimensions. To this end, we propose a 3D Banff lesion-scoring framework that reconstructs glomeruli from serial sections, aligns structural counterparts, tracks glomerular identities, and integrates inflammatory-cell counts in 3D. In the experiments, glomerulitis g-scores was used as an example Banff metric and applied to multi-section renal allograft biopsies. Our findings indicate that 3D Banff g-scores are more consistent across slices and , under the semi-automatic setting, correlate more strongly with clinical biomarkers than traditional 2D scores. These results show that 3D volumetric quantification offers promising added value, underscoring the potential benefit of 3D-aware Banff scoring for kidney transplant pathology.

Primary Subject Area: Application: Histopathology

Secondary Subject Area: Image Registration

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Submission Number: 239