Surgical resection of colorectal liver metastases is associated with greater survival compared with non-surgical treatment, and a meaningful possibility of cure. However, the majority of patients are not eligible for resection and may require other non-surgical interventions, such as liver-directed therapies, to be converted to surgical eligibility. Given the number of available therapies, a general framework is needed that outlines the specific roles of chemotherapy, surgery, and locoregional treatments [including selective internal radiation therapy (SIRT) with Y-90 microspheres]. Using a data-driven, modified Delphi process, an expert panel of surgical oncologists, transplant surgeons, and hepatopancreatobiliary (HPB) surgeons convened to create a comprehensive, evidence-based treatment algorithm that includes appropriate treatment options for patients stratified by their eligibility for surgical treatment. The group coined a novel, more inclusive phrase for targeted locoregional tumor treatment (a blanket term for resection, ablation, and other emerging locoregional treatments): local parenchymal tumor destruction therapy. The expert panel proposed new nomenclature for 3 distinct disease categories of liver-dominant metastatic colorectal cancer that is consistent with other tumor types: (I) surgically treatable (resectable); (II) surgically untreatable (borderline resectable); (III) advanced surgically untreatable (unresectable) disease. Patients may present at any point in the algorithm and move between categories depending on their response to therapy. The broad intent of therapy is to transition patients toward individualized treatments where possible, given the survival advantage that resection offers in the context of a comprehensive treatment plan. This article reviews what is known about the role of SIRT with Y-90 as neoadjuvant, definitive, or palliative therapy in these different clinical situations and provides insight into when treatment with SIRT with Y-90 may be appropriate and useful, organized into distinct treatment algorithm steps.
View details for DOI 10.21037/jgo.2020.01.09
View details for Web of Science ID 000529848600022
View details for PubMedID 32399284
View details for PubMedCentralID PMC7212103