Document Type


Publication Date



This article is the author’s final published version in JCO Clinical Cancer Informatics, Volume 7, October 2023, Article number e2300061.

The published version is available at Copyright © 2023 American Society of Clinical Oncology. All rights reserved.


PURPOSE: To explore medications and their administration patterns in real-world patients with breast cancer.

METHODS: A retrospective study was performed using TriNetX, a federated network of deidentified, Health Insurance Portability and Accountability Act-compliant data from 21 health care organizations across North America. Patients diagnosed with breast cancer between January 1, 2013, and May 31, 2022, were included. We investigated a rule-based and unsupervised learning algorithm to extract medications and their administration patterns. To group similar administration patterns, we used three features in k-means clustering: total number of administrations, median number of days between administrations, and standard deviation of the days between administrations. We explored the first three lines of therapy for patients classified into six groups on the basis of their stage at diagnosis (early as stages I-III

RESULTS: In early-stage HR+/

CONCLUSION: Although there is a general agreement with the NCCN Guidelines, real-world medication data exhibit variability in the medications and their administration patterns.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

PubMed ID




Included in

Oncology Commons