Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age Journal Article
| Authors: | Williams, A. D.; Pawloski, K. R.; Wen, H. Y.; Sevilimedu, V.; Thompson, D.; Morrow, M.; El-Tamer, M. |
| Article Title: | Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age |
| Abstract: | Purpose: The use of the Oncotype DX recurrence score (RS) to predict chemotherapy benefit in patients with hormone receptor-positive/HER2 negative (HR+/HER2-) breast cancer has recently expanded to include postmenopausal patients with N1 disease. RS availability is limited in resource-poor settings, however, prompting the development of statistical models that predict RS using clinicopathologic features. We sought to assess the performance of our supervised machine learning model in a cohort of patients > 50 years of age with N1 disease. Methods: We identified patients > 50 years of age with pT1-2N1 HR+/HER2- breast cancer and applied the statistical model previously developed in a node-negative cohort, which uses age, pathologic tumor size, histology, progesterone receptor expression, lymphovascular invasion, and tumor grade to predict RS. We measured the model’s ability to predict RS risk category (low: RS ≤ 25; high: RS > 25). Results: Our cohort included 401 patients, 60.6% of whom had macrometastases, with a median of 1 positive node. The majority of patients had a low-risk observed RS (85.8%). For predicting RS category, the model had specificity of 97.3%, sensitivity of 31.8%, a negative predictive value of 87.9%, and a positive predictive value of 70.0%. Conclusion: Our model, developed in a cohort of node-negative patients, was highly specific for identifying cN1 patients > 50 years of age with a low RS who could safely avoid chemotherapy. The use of this model for identifying patients in whom genomic testing is unnecessary would help decrease the cost burden in resource-poor settings as reliance on RS for adjuvant treatment recommendations increases. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
| Keywords: | adult; human tissue; aged; major clinical study; genetics; lymph node metastasis; sensitivity and specificity; metabolism; neoplasm recurrence, local; breast cancer; gene expression profiling; epidermal growth factor receptor 2; cohort analysis; pathology; breast neoplasms; tumor marker; prediction; risk assessment; tumor recurrence; breast tumor; aging; receptors, estrogen; estrogen receptor; progesterone receptor; statistical model; predictive value; diagnostic test accuracy study; risk prediction; machine learning; cancer prognosis; humans; prognosis; human; female; article; recurrence score; biomarkers, tumor; supervised machine learning; breast cancer recurrence; node positive |
| Journal Title: | Breast Cancer Research and Treatment |
| Volume: | 196 |
| Issue: | 3 |
| ISSN: | 0167-6806 |
| Publisher: | Springer |
| Date Published: | 2022-12-01 |
| Start Page: | 565 |
| End Page: | 570 |
| Language: | English |
| DOI: | 10.1007/s10549-022-06763-5 |
| PUBMED: | 36269526 |
| PROVIDER: | scopus |
| PMCID: | PMC10328094 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 December 2022 -- Source: Scopus |
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