Imaging of colorectal nodal disease

Lishan Cai, Zuhir Bodalal, Stefano Trebeschi, Selam Waktola, Tania C. Sluckin, Miranda Kusters, Monique Maas, Regina Beets-Tan, Sean Benson

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)


Imaging plays an important role in many aspects of nodal colorectal cancer from diagnosis, staging, and treatment selection to fundamental research on the mechanisms driving lesion development. The range of modalities includes anatomical imaging such as computed tomography (CT), magnetic resonance imaging (MRI), and functional imaging with the development of novel tracer and contrast agents for use in positron emission tomography, in addition to dynamic contrast-enhanced and diffusion-weighted MRI. Traditionally, CT has been used for the assessment of colon tumors and rectal tumors that have metastasized to lymph nodes and further to other distant organs such as the liver. MRI is a versatile technique that offers low-radiation insights for diagnosis and lesion characterization. The high resolution of microscopy using resected and biopsied tissue samples, while not accounting for the heterogeneity of the entire tumor burden, is important in the characterization of primary tumors in terms of pathway analyses and will become increasingly important for patient treatment stratification. The use of imaging in routine clinical care has resulted in large amounts of patient data that may be exploited by artificial intelligence to achieve new scientific insights, as well as to improve cost efficiency through automation.
Original languageEnglish
Title of host publicationThe Lymphatic System in Colorectal Cancer
Subtitle of host publicationBasic Concepts, Pathology, Imaging, and Treatment Perspectives
Number of pages14
ISBN (Electronic)9780128242971
ISBN (Print)9780128242988
Publication statusPublished - 1 Jan 2022

Publication series

NameThe Lymphatic System in Colorectal Cancer: Basic Concepts, Pathology, Imaging, and Treatment Perspectives


  • Artificial intelligence
  • Colorectal cancer
  • Computed tomography
  • Magnetic resonance imaging

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