Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping

• URL: http://arxiv.org/abs/2407.18105v1
• Date: Thu, 25 Jul 2024 15:08:54 GMT
• Title: Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
• Authors: Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar,
• Abstract summary: Multi-resolution graph models leverage the spatial relationships of patches at multiple magnifications, learning the context for each patch. We conduct the most thorough validation of a graph model for ovarian cancer subtyping to date. The accuracy of the combined foundation model and multi-resolution graph network offers a step towards the clinical applicability of these models.
• Score: 2.0661578265672094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single resolution. Multi-resolution graph models leverage the spatial relationships of patches at multiple magnifications, learning the context for each patch. In this study, we conduct the most thorough validation of a graph model for ovarian cancer subtyping to date. Seven models were tuned and trained using five-fold cross-validation on a set of 1864 whole slide images (WSIs) from 434 patients treated at Leeds Teaching Hospitals NHS Trust. The cross-validation models were ensembled and evaluated using a balanced hold-out test set of 100 WSIs from 30 patients, and an external validation set of 80 WSIs from 80 patients in the Transcanadian Study. The best-performing model, a graph model using 10x+20x magnification data, gave balanced accuracies of 73%, 88%, and 99% in cross-validation, hold-out testing, and external validation, respectively. However, this only exceeded the performance of attention-based multiple instance learning in external validation, with a 93% balanced accuracy. Graph models benefitted greatly from using the UNI foundation model rather than an ImageNet-pretrained ResNet50 for feature extraction, with this having a much greater effect on performance than changing the subsequent classification approach. The accuracy of the combined foundation model and multi-resolution graph network offers a step towards the clinical applicability of these models, with a new highest-reported performance for this task, though further validations are still required to ensure the robustness and usability of the models.

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