Abstract
Molecular breast cancer sub-types derived from core-biopsy are central for individual outcome prediction and treatment decisions. Determining sub-types by non-invasive imaging procedures would benefit early assessment. Furthermore, identifying phenotypic traits of sub-types may inform our understanding of disease processes as we become able to monitor them longitudinally. We propose a model to learn phenotypic appearance concepts of four molecular sub-types of breast cancer. A deep neural network classification model predicts sub-types from multi-modal, multi-parametric imaging data. Intermediate representations of the visual information are clustered, and clusters are scored based on testing with concept activation vectors to assess their contribution to correctly discriminating sub-types. The proposed model can predict sub-types with competitive accuracy from simultaneous \({}^{18}\)F-FDG PET/MRI, and identifies visual traits in the form of shared and discriminating phenotypic concepts associated with the sub-types.
This work was supported by the Vienna Science and Technology Fund (WWTF, LS19-018, LS20-065), the Austrian Research Fund (FWF, P 35189), a CCC Research Grant, and a European Union’s Horizon 2020 research grant (No. 667211).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, Z., Bei, Y., Rudin, C.: Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2, 1–11 (2020)
Clough, J., Oksuz, I., Puyol Anton, E., Ruijsink, B., King, A., Schnabel, J.: Global and local interpretability for cardiac MRI classification (2019). https://arxiv.org/abs/1906.06188
Delso, G., et al.: Performance measurements of the siemens MMR integrated whole-body PET/MR scanner. J. Nucl. Med. 52(12), 1914–1922 (2011)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Fragomeni, S.M., Sciallis, A., Jeruss, J.S.: Molecular subtypes and local-regional control of breast cancer. Surg. Oncol. Clin. 27(1), 95–120 (2018)
Gamble, P., et al.: Determining breast cancer biomarker status and associated morphological features using deep learning. Commun. Med. 1 (2021)
Ghassemi, M., Oakden-Rayner, L., Beam, A.L.: The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3(11), e745–e750 (2021)
Ghorbani, A., Wexler, J., Zou, J., Kim, B.: Towards automatic concept-based explanations. In: Advances in Neural Information Processing Systems, vol. 32, pp. 9277–9286 (2019)
Graziani, M., Andrearczyk, V., Marchand-Maillet, S., Müller, H.: Concept attribution: explaining CNN decisions to physicians. Comput. Biol. Med. 123 (2020)
Ha, R., et al.: Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm. J. Digit. Imaging 32(2), 276–282 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, Y., et al.: Multi-parametric MRI-based radiomics models for predicting molecular subtype and androgen receptor expression in breast cancer. Front. Oncol. 11 (2021)
Janik, A., Dodd, J., Ifrim, G., Sankaran, K., Curran, K.: Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset. In: Proceedings of SPIE - The International Society for Optical Engineering (2021)
Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17(6), 441–458 (1996)
Kim, B., et al.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: International Conference on Machine Learning, pp. 2668–2677. PMLR (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lee, J.R., Kim, S., Park, I., Eo, T., Hwang, D.: Relevance-CAM: your model already knows where to look. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14944–14953 (2021)
Nielsen, T.O., et al.: Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin. Cancer Res. 10(16), 5367–5374 (2004)
Pereira, S., Meier, R., Alves, V., Reyes, M., Silva, C.A.: Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. In: Stoyanov, D., et al. (eds.) MLCN/DLF/IMIMIC -2018. LNCS, vol. 11038, pp. 106–114. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02628-8_12
Pinker, K., et al.: Improved differentiation of benign and malignant breast tumors with multiparametric 18fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin. Cancer Res. 20(13), 3540–3549 (2014)
Reyes, M., et al.: On the interpretability of artificial intelligence in radiology: challenges and opportunities. Radiol. Artif. Intell. 2(3), e190043 (2020)
Romeo, V., et al.: AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur. J. Nucl. Med. Mol. Imaging, 1–13 (2021)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Son, J., Lee, S.E., Kim, E.K., Kim, S.: Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis. Sci. Rep. 10(1), 1–11 (2020)
Sørlie, T., et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. 98(19), 10869–10874 (2001)
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319–3328. PMLR (2017)
Sung, H., et al.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021)
Wang, H., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 24–25 (2020)
Zhang, Y., et al.: Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers. Eur. Radiol. 31(4), 2559–2567 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fürböck, C., Perkonigg, M., Helbich, T., Pinker, K., Romeo, V., Langs, G. (2022). Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13437. Springer, Cham. https://doi.org/10.1007/978-3-031-16449-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-16449-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16448-4
Online ISBN: 978-3-031-16449-1
eBook Packages: Computer ScienceComputer Science (R0)
