A literature review of treatment-specific clinical prediction models in patients with breast cancer

Natansh D. Modi, Michael J. Sorich, Andrew Rowland, Jessica M. Logan, Ross A. McKinnon, Ganessan Kichenadasse, Michael D. Wiese, Ashley M. Hopkins

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)


Despite advances in the breast cancer treatment, significant variability in patient outcomes remain. This results in significant stress to patients and clinicians. Treatment-specific clinical prediction models allow patients to be matched against historical outcomes of patients with similar characteristics; thereby reducing uncertainty by providing personalised estimates of benefits, harms, and prognosis. To achieve this objective, models need to be clinical-grade with evidence of accuracy, reproducibility, generalizability, and be user-friendly. A structured search was undertaken to identify treatment-specific clinical prediction models for therapeutic or adverse outcomes in breast cancer using clinicopathological data. Significant gaps in the presence of validated models for available treatments was identified, along with gaps in prediction of therapeutic and adverse outcomes. Most models did not have user-friendly tools available. With the aim being to facilitate the selection of the best medicine for a specific patient and shared-decision making, future research will need to address these gaps.

Original languageEnglish
Article number102908
Number of pages8
JournalCritical Reviews in Oncology/Hematology
Publication statusPublished - Apr 2020


  • Breast cancer
  • Clinical prediction model
  • Literature review
  • Treatment-specific


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