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PURPOSE: This study aimed to identify and synthesize published algorithms for identifying the initiation of a new line of therapy (LOT) for metastatic lung, breast, and colorectal cancer in real-world data (RWD). METHODS: We conducted a scoping review of published, English-language studies describing algorithms for identifying any LOTs with systemic anti-cancer therapy (SACT) for either non-metastatic or metastatic lung, breast, or colorectal cancer in RWD between January 1, 2014, and April 29, 2024. Dual reviewers independently screened titles, abstracts, and full-text articles, with disagreements resolved by a third reviewer. Data were extracted, categorized, synthesized, and summarized in narrative and tabular formats. RESULTS: The review identified 25 studies, mainly (64%) from the United States. Electronic health/medical records (EHRs) were the most frequently utilized (72%) RWD source. Twenty-four studies (96%) described RWD algorithms for identifying the initiation of a new LOT for metastatic lung, breast, or colorectal cancer. In 23 studies, algorithms required observing a new, adjuvant, SACT after an "incident" metastatic diagnosis code, which had been preceded by a metastasis-free lookback period of varied duration. Three studies' algorithms required observation of the completion of non-metastatic LOTs before initiation of a new LOT for metastatic cancer. Three studies validated their algorithms. CONCLUSIONS: Different algorithms are being used to identify LOT initiation for metastatic cancer. Most algorithms require an incident diagnosis of metastasis before considering subsequent SACT as newly initiated LOT for metastatic cancer. However, definitions of metastasis onset and gap duration to therapy initiation vary.

More information Original publication

DOI

10.1002/pds.70370

Type

Journal article

Publication Date

2026-05-01T00:00:00+00:00

Volume

35

Keywords

LOT, algorithms, frameworks, line of therapy, observational studies, oncology, scoping review, Humans, Colorectal Neoplasms, Lung Neoplasms, Breast Neoplasms, Algorithms, Female, Electronic Health Records, Antineoplastic Agents