OncoSurge: A Strategy for Improving Resectability With Curative Intent in Metastatic Colorectal Cancer

Abstract

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, our aim was to create a therapeutic decision model identifying appropriate procedure sequences. Methods We 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 ≤ 4 or > 4 metastases and unilobar or bilobar involvement. Resection was appropriate postchemotherapy, independent of tumor response in the case of ≤ 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.