{"doi":"10.1093/jnci/djaa181","title":"Identifying Preferred Breast Cancer Risk Predictors: A Holistic Perspective","abstract":"Cancer risk prediction is the cornerstone of precision cancer control, and breast cancer risk prediction stands out as a prototype. Risk calculators based on clinical, biological, behavioral, and epidemiologic factors are available for many cancers, but breast cancer has the most established history and arguably the largest number of available tools for risk prediction. Although there are various metrics for quantifying risk, since the 1989 publication by Gail et al. (1) that became the National Cancer Institute’s Breast Cancer Risk Assessment Tool (2), the field has been largely focused on absolute breast cancer risk over an interval as a measure for targeting prevention and screening interventions. In this issue of JNCI, MacInnis and colleagues (3) address a contentious issue, namely, whether short-term (5-year) risk should be preferred as the driver of intervention decisions over the traditionally used long-term (lifetime) risk. The investigators zero in on a definition of “preferred” that speaks to the accuracy of the predicted risk, comparing the diagnostic performance of 5-year and lifetime risk predictions from the IBIS (v8b) (4) and BOADICEA (v3) (5) tools in data from the Breast Cancer Prospective Family Study Cohort (PFSC) (6). In this editorial, we discuss the process of evaluating risk prediction tools as exemplified by the study of MacInnis et al. (3) while raising a broader question of what constitutes a preferred tool in the context of risk prediction and communication. Beyond being accurate, we propose 2 additional qualities for tools to be preferred—they should be meaningful and actionable. We define each and argue that all 3 properties should be considered when identifying preferred prediction tools for informing targeted cancer control strategies. The type of validation study represented by that of MacInnis et al. (3) is both common and necessary. Every risk prediction tool is developed and calibrated within a specific cohort before being offered to the field for potentially much broader use. Given inevitable differences across cohorts in population composition, screening practices, and length of follow-up, there is no guarantee that absolute risks from a model calibrated to 1 cohort will match a different cohort. In general, predicted long-term risks will exceed those observed in a cohort with short-term follow-up and vice versa. This may explain why MacInnis et al. (3) found that 5-year risk predictions seemed to perform better than lifetime risk predictions (at least for women younger than 40 years) in the PFSC, which had a follow-up interval close to 10 years. Still, their suggestion that 5-year risk is to be preferred in a clinical setting based on this finding bears further scrutiny. First, once a risk prediction tool has been developed, whether it is for predicting 5-year, 10-year, or lifetime risk, it is essentially an algorithm calibrated to a specific training dataset. A new validation dataset is agnostic to the time horizon of the prediction tool; we might as well call our 5-year prediction algorithm A and our lifetime prediction algorithm B. Further, the standard validation metric, the concordance index or AUC, is a check on the ordering of the predicted risks rather than their magnitude. In principle, therefore, a lifetime risk prediction could perform better or worse in terms of Area Under the Curve (AUC) than a 5-year risk prediction on a validation set with relatively short-term follow-up. In the study of MacInnis et al. (3), the 5-year risk prediction happened to perform better, at least for women younger than 40 years. The point is that this is not necessarily a consequence of the time horizon of the prediction tool; rather, it is a reflection that algorithm A happens to align better with the validation data than algorithm B. Indeed, the fact that algorithm A is preferred over B when validating performance against a certain cohort could be a feature of that cohort and not a generalizable pr","journal":"JNCI Journal of the National Cancer Institute","year":2020,"id":124285,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9566,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":336040,"name":"Yu Shen","orcid":"0000-0002-3899-7868","position":1,"is_corresponding":false},{"id":107383,"name":"Ya‐Chen Tina Shih","orcid":"0000-0001-7290-3864","position":2,"is_corresponding":false},{"id":107378,"name":"Ruth Etzioni","orcid":"0000-0002-9164-6370","position":0,"is_corresponding":true}],"reference_count":3,"raw_metadata":null,"created_at":"2026-07-18T23:15:07.789881Z","pmid":"33301010","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}