ArticleBreast cancer research and treatment2022
Supervised machine learning model to predict oncotype DX risk category in patients over age 50.
Article in Breast cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Are There Socio-Demographic Inequalities in the Utilisation of Tumour and ctDNA Somatic Mutation Testing in Solid Tumours? A Systematic Review.Cancer medicine · 2026Pooled it
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images.Scientific reports · 2025Article
- Oncotype DX recurrence score in node-positive patients in the post-RxPONDER era: a single-institution experience.Breast cancer research and treatment · 2025Article
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- Article
- A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles.Breast cancer research and treatment · 2024Article
- Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age.Breast cancer research and treatment · 2022Article
- Recurrence ScoreCancers · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
purposeRoutine use of the oncotype DX recurrence score (RS) in patients with early-stage, estrogen receptor-positive, HER2-negative (ER+/HER2-) breast cancer is limited internationally by cost and availability. We created a supervised machine learning model using clinicopathologic variables to predict RS risk category in patients aged over 50 years.
methodsFrom January 2012 to December 2018, we identified patients aged over 50 years with T1-2, ER+/HER2-, node-negative tumors. Clinicopathologic data and RS results were randomly split into training and validation cohorts. A random forest model with 500 trees was developed on the training cohort, using age, pathologic tumor size, histology, progesterone receptor (PR) expression, lymphovascular invasion (LVI), and grade as predictors. We predicted risk category (low: RS ≤ 25, high: RS > 25) using the validation cohort.
resultsOf the 3880 tumors identified, 1293 tumors comprised the validation cohort in patients of median (IQR) age 62 years (56-68) with median (IQR) tumor size 1.2 cm (0.8-1.7). Most tumors were invasive ductal (80.3%) of low-intermediate grade (80.5%) without LVI (80.9%). PR expression was ≤ 20% in 27.3% of tumors. Specificity for identifying RS ≤ 25 was 96.3% (95% CI 95.0-97.4) and the negative predictive value was 92.9% (95% CI 91.2-94.4). Sensitivity and positive predictive value for predicting RS > 25 was lower (48.3 and 65.1%, respectively).
conclusionOur model was highly specific for identifying eligible patients aged over 50 years for whom chemotherapy can be omitted. Following external validation, it may be used to triage patients for RS testing, if predicted to be high risk, in resource-limited settings.
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