S. Timofeiov, M. Marinca, C. Bar, M. Breaban, V. Drug, V. Scripcariu

Conversion Rate to Resectability in Colorectal Cancer Liver Metastases: Need for Criteria Adapted to Current Therapy

Medical Imaging Analysis and Diagnostics

Therapeutic strategy for patients with colorectal cancer liver metastases (CRLM) is based on good monitoring and correct assignment to classes of liver resectability based on imaging criteria, taking into account the surgical risk. The objective of this paper is to identify the post-treatment time frame for confirming resectability (conversion to resecability) or permanent unresectability. The study is a prospective analysis based on a Scientific Protocol (Surveillance of patients with colorectal cancer liver metastases) used in the Ist Surgical Oncology Unit, Regional Institute of Oncology Iaşi, Romania. Surgical treatment, oncologic treatment, response to therapy, postoperative surgical complications, were assessed at 3, 6 and 9 months after start of the study.
In the interval July 2012 - January 2014, 106 patients were diagnosed with CRLM. According to the classes of liver resectability the patients were divided into four groups: group I (clear resectability), group II (possibly resectability), group III (susceptible resectability), group IV (unresectable metastases). Relevant for the study were only groups II and III. Thus, in group II patients the rate of conversion to resectability was 23.07% and in group III patients 26.66%. These results were obtained after 3, 6 and 9 months of therapy, respectively.
Rigorous surveillance of patients with CRLM according to a well-established scientific protocol, and their assignment to liver resectability classes represent the first step ofthe oncosurgical therapeutic strategy. An improvement in the rate of conversion to resectability could be achieved through regular assessment of treatment response based on international criteria that should include besides the number and size of target lesions the posttherapy morphological tumor changes.

This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE