Tumor budding

Personalised treatment of cancer patients starts with an extensive evaluation of the cancer that should be treated. Public attention is drawn to next generation sequencing as the ultimate selection tool for treatment. However, fortunately most patients with colorectal cancer (CRC) do not need targeted therapies. The introduction of CRC population screening in The Netherlands, together with improvements in local treatment prevent the development of metastatic disease in the majority of patients.

The next challenges are prevention of overtreatment of patients with early CRC and/or with an intermediate risk CRC and adequate treatment of patient with increased risk CRC. The initial treatment decisions for these patients are partly based on histological parameters, such as invasion depth, differentiation grade, lymphatic invasion and extramural vascular invasion. Evidence from systematic reviews, however, suggests that tumour budding (i.e. the presence of single tumour cells or small tumour cell clusters up to four cells at the invasive front of the cancer) is one of the most powerful histological biomarkers that might help making treatment decisions. However, the implementation of this biomarker is hampered by the lack of standardisation in the histological assessment. Digital image analysis has been proposed as a highly reliable method to determine tumour budding, which will facilitate treatment decisions.

In this project we facilitate convolutional neural network in combination with differently stained whole-slide images of CRC to create a algorithm that is capable of detection tumor budding and tumor clusters (PDC). This will form the basis for detailed and reproducible analysis of the degree of budding within and at the invasive margin of CRC (intratumoral vs peritumoral). Location specific quantitative data will be derived for the number of buds and PDC, by calculating these data for every candidate location in a WSI.

Funding

  • KWF

People

John-Melle Bokhorst

John-Melle Bokhorst

PhD Candidate

Computational Pathology Group

Francesco Ciompi

Francesco Ciompi

Associate Professor

Computational Pathology Group

Jeroen van der Laak

Jeroen van der Laak

Professor

Computational Pathology Group

Tariq Haddad

Tariq Haddad

PhD Candidate

Computational Pathology Group

Femke Doubrava-Simmer

Femke Doubrava-Simmer

Postdoctoral researcher

Pathology, Radboudumc

Iris Nagtegaal

Iris Nagtegaal

Pathologist

Pathology, Radboudumc

Publications

  • J. Bokhorst, H. Pinckaers, P. van Zwam, I. Nagetgaal, J. van der Laak and F. Ciompi, "Learning from sparsely annotated data for semantic segmentation in histopathology images", Medical Imaging with Deep Learning, 2019;102:81-94.
  • J. Bokhorst, L. Rijstenberg, D. Goudkade, I. Nagtegaal, J. van der Laak and F. Ciompi, "Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning", Computational Pathology and Ophthalmic Medical Image Analysis, 2018.