Breast cancer metastases in lymph nodes and the TNM classification system
This challenge will focus on the detection and classification of breast cancer metastases in lymph nodes. Lymph nodes are small glands that filter lymph, the fluid that circulates through the lymphatic system. The lymph nodes in the axilla are the first place breast cancer is likely to spread. Metastatic involvement of lymph nodes is one of the most important prognostic factors in breast cancer. Prognosis is poorer when cancer has spread to the lymph nodes. This is why lymph nodes are surgically removed and examined microscopically. However, the diagnostic procedure for pathologists is tedious and time-consuming. But most importantly, small metastases are very difficult to detect and sometimes they are missed.
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. In breast cancer, TNM staging takes into account the size of the tumour (T-stage), whether the cancer has spread to the regional lymph nodes (N-stage), and whether the tumour has metastasised to other parts of the body (M-stage). 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. Further information can be found in the Evaluation section.
Clinical pathology and histological slide preparation
In clinical pathology, human tissue is examined through a microscope by a pathologist: a medical doctor specialised in detecting and characterising diseases on a cellular level. Tissue samples are most often collected during surgery or via biopsy, and need to be further processed in order to make glass slides which hold histological sections of just a few micrometers thick.
Tissue processing includes fixation, embedding, cutting and staining. During the preparation of histological slides, different stains can be used for various purposes. The hematoxylin and eosin (H&E) stain however, is most widely used; it induces sharp blue/pink contrasts across various (sub)cellular structures and it is applied across many different tissue types.
Digital pathology, is a new, rapidly expanding field in medical imaging in which whole-slide scanners are used to digitise glass slides at high resolution (up to 160nm per pixel). The availability of whole-slide images (WSI) has garnered the interest of the medical image analysis community, resulting in increasing numbers of publications on histopathologic image analysis.
Whole-slide images are generally stored in a multi-resolution pyramid structure. Image files contain multiple down-sampled versions of the original image. Each image in the pyramid is stored as a series of tiles, to facilitate rapid retrieval of subregions of the image.
A typical whole-slide image is approximately 200000 x 100000 pixels on the highest resolution level with 3 byte RGB pixel format. This means 55.88GB of uncompressed pixel data from a single level.
The aim of CAMELYON17 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. Last year’s CAMELYON16 focused on the detection of lymph node metastases, both on a lesion-level and on a slide-level. This year we move up to patient-level analysis, which requires combining the detection and classification of metastases in multiple lymph node slides into one outcome: a pN-stage. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists. Therefore, an automated solution for assessing the pN-stage in breast cancer patients, would hold great promise to reduce the workload of pathologists, while at the same time, reduce the subjectivity in diagnosis.