Abstract
PURPOSE: The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment. METHODS: The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as well as a breast conserving surgery intra-operative data. RESULTS: The purposed model outperformed all baselines, statistically significantly, with average balanced accuracy of 91.6%. When applied to intra-operative data, the purposed model improved the false positive rate of the baselines. The estimated attention distribution for status of different hormone receptors agreed with reported metabolic findings in the literature. CONCLUSION: Deployment of ex-vivo models is challenging due to the tissue heterogeneity of intra-operative data. The proposed Evidential Graph Transformer is a powerful tool that while providing the attention distribution of biochemical subbands, improve the surgical deployment power by providing decision confidence.
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References
Ahmedt-Aristizabal, D., Armin, M.A., Denman, S., Fookes, C., Petersson, L.: Graph-based deep learning for medical diagnosis and analysis: past, present and future. Sensors 21(14), 4758 (2021). https://doi.org/10.3390/S21144758
Akbarifar, F., et al.: Graph-based analysis of mass spectrometry data for tissue characterization with application in basal cell carcinoma surgery. In: SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11598 (2021)
Balog, J., et al.: In vivo endoscopic tissue identification by rapid evaporative ionization mass spectrometry (REIMS). Angewandte Chemie Int. Ed. 54(38), 11059–11062 (2015). https://doi.org/10.1002/anie.201502770
Budczies, J., et al.: Glutamate enrichment as new diagnostic opportunity in breast cancer. Int. J. Cancer 136(7), 1619–1628 (2015). https://doi.org/10.1002/ijc.29152
Demas, D.M., et al.: Glutamine metabolism drives growth in advanced hormone receptor positive breast cancer. Front. Oncol. 9 (2019). https://doi.org/10.3389/fonc.2019.00686
Dunnwald, L.K., Rossing, M.A., Li, C.I.: Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Res. 9(1), R6 (2007). https://doi.org/10.1186/bcr1639
Durasov, N., Bagautdinov, T., Baque, P., Fua, P.: Masksembles for uncertainty estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13539–13548 (2021)
Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. In: Methods and Applications, AAAI Workshop on Deep Learning on Graphs (2021)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1050–1059. PMLR, New York, New York, USA, 20–22 June 2016
Hargreaves, A.C., Mohamed, M., Audisio, R.A.: Intra-operative guidance: methods for achieving negative margins in breast conserving surgery. J. Surg. Oncol. 110(1), 21–25 (2014). https://doi.org/10.1002/JSO.23645
Jamzad, A., et al.: Graph transformers for characterization and interpretation of surgical margins. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 88–97. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_9
Jsang, A.: Subjective Logic: A Formalism for Reasoning Under Uncertainty. Springer, Cham Verlag (2016). https://doi.org/10.1007/978-3-319-42337-1
Kim, S., Kim, D.H., Jung, W.H., Koo, J.S.: Expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer. Endocrine-Related Cancer 20(3), 339–348 (2013). https://doi.org/10.1530/ERC-12-0398
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations. ICLR (2017)
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Santilli, A., et al.: Domain adaptation and self-supervised learning for surgical margin detection. Int. J. Comput. Assist. Radiol. Surg. 1–9 (2021). https://doi.org/10.1007/s11548-021-02381-6
Santilli, A., et al.: Self-supervised learning for detection of breast cancer in surgical margins with limited data. In: Proceedings - International Symposium on Biomedical Imaging, April 2021, pp. 980–984, April 2021. https://doi.org/10.1109/ISBI48211.2021.9433829
Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Syeda, A.: Self-supervision and uncertainty estimation in surgical margin detection (2023)
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Jamzad, A. et al. (2023). Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_53
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