Europe PMC

This website requires cookies, and the limited processing of your personal data in order to function. By using the site you are agreeing to this as outlined in our privacy notice and cookie policy.

Epidemiology of colorectal liver metastases.

Author information

Affiliations

  • 1. Department of Surgery, University of Glasgow Royal Infirmary, Queen Elizabeth Building, Alexandra Parade, Glasgow, G31 2ER, UK.
      Authors
    • McMillan DC 1
    (1 author)

ORCIDs linked to this article

Surgical Oncology, 09 May 2007, 16(1):3-5
https://doi.org/10.1016/j.suronc.2007.04.008 PMID: 17493802 

Review

Abstract 

In summary, the natural history of patients presenting with synchronous liver metastases has been well documented. However, the epidemiology and natural history of patients presenting with metachronous disease is scant. It is clear, therefore, that the accurate detection of occult liver metastases at the time of presentation underpins further improvements in the selection of appropriate treatment for patients presenting with colorectal cancer. This will facilitate early referral for investigation and treatment of their liver metastases.

Full text links 

Read article at publisher's site: https://doi.org/10.1016/j.suronc.2007.04.008

References 

Articles referenced by this article (24)

Show 10 more references (10 of 24)

Citations & impact 

Impact metrics

93
Citations
Jump to Citations

Citations of article over time

Smart citations by scite.ai
Smart citations by scite.ai include citation statements extracted from the full text of the citing article. The number of the statements may be higher than the number of citations provided by EuropePMC if one paper cites another multiple times or lower if scite has not yet processed some of the citing articles.
Explore citation contexts and check if this article has been supported or disputed.
https://scite.ai/reports/10.1016/j.suronc.2007.04.008

Supporting
Mentioning
Contrasting
0
81
1

Article citations

Go to all (93) article citations

Similar Articles 

To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.