A Strategy for Improving Resectability with Curative Intent in Metastatic Colorectal Cancer
Published in: Journal of Clinical Oncology, v. 23, no. 28, Oct. 1, 2005, p. 7125-7134
PURPOSE: Most patients with colorectal liver metastases present to general surgeons and oncologists without a specialist interest in their management. Since treatment strategy is frequently dependent on the response to earlier treatments, the authors aim was to create a therapeutic decision model identifying appropriate procedure sequences. METHODS: The authors used the RAND Corporation/University of California, Los Angeles Appropriateness Method (RAM) assessing strategies of resection, local ablation and chemotherapy. After a comprehensive literature review, an expert panel rated appropriateness of each treatment option for a total of 1,872 ratings decisions in 252 cases. A decision model was constructed, consensus measured and results validated using 48 virtual cases, and 34 real cases with known outcomes. RESULTS: Consensus was achieved with overall agreement rates of 93.4 to 99.1%. Absolute resection contraindications included unresectable extrahepatic disease, more than 70% liver involvement, liver failure, and being surgically unfit. Factors not influencing treatment strategy were age, primary tumor stage, timing of metastases detection, past blood transfusion, liver resection type, pre-resection carcinoembryonic antigen (CEA), and previous hepatectomy. Immediate resection was appropriate with adequate radiologically-defined resection margins and no portal adenopathy; other factors included presence of < or = 4 or > 4 metastases and unilobar or bilobar involvement. Resection was appropriate postchemotherapy, independent of tumor response in the case of < or = 4 metastases and unilobar liver involvement. Resection was appropriate only for > 4 metastases or bilobar liver involvement, after tumor shrinkage with chemotherapy. When possible, resection was preferred to local ablation. CONCLUSION: The results were incorporated into a decision matrix, creating a computer program (OncoSurge). This model identifies individual patient resectability, recommending optimal treatment strategies. It may also be used for medical education.