Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous tumor that originates from normal B-cells. A limited number of studies have investigated the role of acellular stromal microenvironment on outcome in DLBCL. Here, we propose a novel digital proximity signature (DPS) for predicting overall survival (OS) in DLBCL patients. We propose a novel end-to-end multi-task deep learning model for cell detection and classification and investigate the spatial proximity of collagen (type VI) and tumor cells for estimating the DPS. To the best of our knowledge, this is the first study that performs automated analysis of tumor and collagen on DLBCL to identify potential prognostic factors. Experimental results favor our cell classification algorithm over conventional approaches. In addition, our pilot results show that strongly associated tumor-collagen regions are statistically significant (p = 0.03) in predicting OS in DLBCL patients.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Coiffier, B., et al.: CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. New Engl. J. Med. 346(4), 235–242 (2002)
de Jonge, A.V., et al.: Diffuse large B-cell lymphoma with MYC gene rearrangements: current perspective on treatment of diffuse large B-cell lymphoma with MYC gene rearrangements; case series and review of the literature. Eur. J. Cancer 55, 140–146 (2016)
Chen, Z., et al.: Novel risk stratification of de novo diffuse large B cell lymphoma based on tumour-infiltrating T lymphocytes evaluated by flow cytometry. Ann. Hematol. 98(2), 391–399 (2019)
Zhu, X., et al.: Lung cancer survival prediction from pathological images and genetic data—an integration study. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE (2016)
Wang, S., Yao, J., Xu, Z., Huang, J.: Subtype cell detection with an accelerated deep convolution neural network. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 640–648. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_74
Zhu, X., et al.: WSISA: making survival prediction from whole slide histopathological images. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Yuan, Y., et al.: Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 4(157), 157ra143 (2012)
Sirinukunwattana, K., et al.: A novel texture descriptor for detection of glandular structures in colon histology images. In: Medical Imaging 2015: Digital Pathology, vol. 9420 (2015)
Sirinukunwattana, K., et al.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)
Qaiser, T., et al.: Her 2 challenge contest: a detailed assessment of automated her 2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 72(2), 227–238 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Qaiser, T., Pugh, M., Margielewska, S., Hollows, R., Murray, P., Rajpoot, N. (2019). Digital Tumor-Collagen Proximity Signature Predicts Survival in Diffuse Large B-Cell Lymphoma. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-23937-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23936-7
Online ISBN: 978-3-030-23937-4
eBook Packages: Computer ScienceComputer Science (R0)