Permutation-Invariant Cascaded Attentional Set Operator for Computational Nephropathology.

KEY POINTS

Permutation-invariant cascaded attentional set operator (PICASO) is a versatile set operator that uses Transformers to dynamically aggregate histopathologic features from a set of glomerular crops. For detecting active crescent in patients with IgA nephropathy on internal and external validation sets, PICASO achieved an area under the receiver-operating characteristic curve of 0.99 and 0.96, respectively. In the case-level classification of antibody-mediated rejection in kidney transplants, PICASO performed well, with an area under the receiver-operating characteristic curves of 0.97.

BACKGROUND

The advent of digital nephropathology offers the potential to integrate deep learning algorithms into the diagnostic workflow. We introduce permutation-invariant cascaded attentional set operator (PICASO), a novel permutation-invariant set operator to dynamically aggregate histopathologic features from instances. We applied PICASO to two nephropathology scenarios: detecting active crescent lesions in sets of glomerular crops with IgA nephropathy (IgAN) and case-level classification for antibody-mediated rejection (AMR) in kidney transplant.

METHODS

PICASO is a Transformer-based set operator that aggregates features from sets of instances to make predictions. It uses initial histopathologic vectors as a static memory component and continuously updates them on the basis of input embeddings. For active crescent detection in patients with IgAN, we obtained 6206 periodic acid–Schiff–stained glomerular crops (5792 no active crescent, 414 active crescent) from three different health institutes. For the AMR classification, we have 1655 periodic acid–Schiff–stained glomerular crops (769 AMR and 886 non-AMR images) from 89 biopsies. The performance of PICASO as a set operator was compared with other set operators, such as DeepSet, Set Transformer, DeepSet++, and Set Transformer++, using metrics including area under the receiver-operating characteristic curve (AUROC), area under the precision-recall curves, recall, and accuracy.

RESULTS

PICASO achieved superior performance in detecting active crescent in patients with IgAN, with an AUROC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) on internal validation and 0.96 (95% CI, 0.95 to 0.98) on external validation, significantly outperforming other set operators ( < 0.001). It also attained the highest AUROC of 0.97 (95% CI, 0.90 to 1.0, = 0.02) for case-level AMR classification. The area under the precision-recall curve, recall, and accuracy scores were also higher when using PICASO, and it significantly outperformed baselines ( < 0.001).

CONCLUSIONS

PICASO can potentially advance nephropathology by improving performance through dynamic feature aggregation.