Recursive partitioning model-based analysis for survival of colorectal cancer patients with lung and liver oligometastases treated with stereotactic body radiation therapy.

Abstract

Liver and lung are common sites of metastases from colorectal cancer (CRC). Stereotactic body radiation therapy (SBRT) represents a valid treatment, with high rates of local control (LC). In this study, we applied recursive partitioning model-based analysis (RPA) to define class risks for overall survival (OS) and progression free survival (PFS) in oligometastatic CRC patients. In this monocentric analysis, we included patients with lung or liver metastases. Patients were candidate to SBRT if a maximum of 5 metastases. End points of the present analysis were LC, PFS, and OS. The binary classification tree approach with RPA was applied to stratify the patients into risk groups based on OS and PFS. 218 patients were treated with SBRT on 371 metastases. Majority of patients (56%) was treated on single lesion, followed by 2 (26.1%) and 3 lesions (14.7%). Median follow-up was 22.7months. Rates of LC were 84.2% at 1year and 73.8% at 3years. Rates of PFS at 1 and 3years were 42.2% and 14.9%, respectively. RPA identified 3 classes for PFS, according to age and number of metastases with 3-year PFS of 30.6%, 13.5% and 8.4%. Overall survival was 87.2% at 1year, 51.9% at 3years, and 36.8% at 5years. RPA identified 3 nodes. Class 1 included patients with liver metastases (3-year OS 35.2%). Class 2 included patients with lung metastases and DFI ≤ 48months (3-year OS 65%). Class 3 included patients with lung metastases and DFI > 48months (3-year OS 73.5%). Stereotactic body radiation therapy can be considered an effective treatment for the management of liver and lung metastases from CRC. With RPA, we identified prognostic risk class to define patients who could benefit the most from SBRT.