OBJECTIVES: The utility of administrative claims data in oncology research is somewhat limited due to the lack of important clinical indicators of disease severity, including sites of metastases. The accuracy and reliability of diagnosis codes for sites of metastases in administrative claims data is largely unknown. The aims of this study were to examine algorithms to identify liver, lung, brain, and bone metastases from administrative claims among patients diagnosed with a solid tumor malignancy and to summarize the algorithm’s validity. METHODS: A systematic literature search was conducted according to PRISMA guidelines using three databases (MEDLINE, Embase, and Web of Science) to identify English-language validation studies published between 2005 and 2018 that examined algorithms for identifying liver, lung, brain, or bone metastases among patients with solid tumor malignancies from US administrative claims databases. Inclusion criteria required studies to use a reference standard and report at least one accuracy test measure (e.g., sensitivity, specificity). RESULTS: Among 3740 articles, five met the study’s inclusion criteria. There was high variation in demographic and clinical characteristics of studies’ samples. Primary tumors included prostate, breast, colorectal and lung. Three of these studies evaluated the validity of algorithms identifying bone metastases, one evaluated algorithms for liver metastases and one evaluated algorithms for brain metastases. All studies reported validation of algorithms based on International Classification of Diseases, Ninth Revision (ICD-9) codes. Three studies used medical records as reference standards while two used cancer registries. The sensitivity and specificity reported across studies examining algorithms to identify bone metastases ranged from 48% to 96%, and 53%, to 98%, respectively. CONCLUSIONS: There is limited evidence regarding the accuracy of algorithms to identify sites of metastases from administrative claims. Research is needed to determine whether the presence (or absence) of metastasis indicators in these data sources reliably reflects the patient’s condition.