CAMELYON

Overview

Built on the success of its predecessor, CAMELYON17 is the second grand challenge in pathology organised by the Diagnostic Image Analysis Group (DIAG) and Department of Pathology of the Radboud University Medical Center (Radboudumc) in Nijmegen, The Netherlands.

The goal of this challenge is to evaluate new and existing algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists. The presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Therefore, an automated solution would hold great promise to reduce the workload of pathologists while at the same time reduce the subjectivity in diagnosis.

Detection of breast cancer metastases in lymph nodes.
Detection of breast cancer metastases in lymph nodes.

Task

The TNM system is an internationally accepted means to classify the extent of cancer spread in patients with a solid tumour. It is one of the most important tools for clinicians to help them select a suitable treatment option and to obtain an indication of prognosis. Since the histological assessment of lymph node metastases is an essential part of TNM classification, CAMELYON17 will focus on the pathologic N-stage, in short: pN-stage.

In clinical practice several lymph nodes are surgically removed after which these nodes are processed in the pathology laboratory. In this challenge we forged artificial patients, with 5 slides provided for each patient where each slide corresponds to exactly one lymph node.

The task in this challenge is to determine a pN-stage for every patient in the test dataset. To compose a pN-stage, the number of positive lymph nodes (i.e. nodes with a metastasis) are counted. For the evaluation of the results we use five class quadratic weighted kappa where the classes are the pN-stages.

Website

Further information, registration, and the results are available on the challenge website.

Challenge Lymph Nodes Metastases machine learning

Related

Publications

P. Bandi, O. Geessink, Q. Manson, M. van Dijk, M. Balkenhol, M. Hermsen, B. Bejnordi, B. Lee, K. Paeng, A. Zhong, Q. Li, F. Zanjani, S. Zinger, K. Fukuta, D. Komura, V. Ovtcharov, S. Cheng, S. Zeng, J. Thagaard, A. Dahl, H. Lin, H. Chen, L. Jacobsson, M. Hedlund, M. Cetin, E. Halici, H. Jackson, R. Chen, F. Both, J. Franke, H. Kusters-Vandevelde, W. Vreuls, P. Bult, B. van Ginneken, J. van der Laak, G. Litjens. From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge. IEEE Trans Med Imaging, 2018.
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G. Litjens, P. Bandi, B. Ehteshami Bejnordi, O. Geessink, M. Balkenhol, P. Bult, A. Halilovic, M. Hermsen, R. van de Loo, R. Vogels, Q. Manson, N. Stathonikos, A. Baidoshvili, P. van Diest, C. Wauters, M. van Dijk, J. van der Laak. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. Gigascience, 2018.
PDF Project DOI
B. Ehteshami Bejnordi, M. Veta, P. van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. van der Laak, t. Consortium. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 2017.
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