ArticleQuantitative imaging in medicine and surgery2024
The added value of ultrasound imaging biomarkers to clinicopathological factors for the prediction of high-risk Oncotype DX recurrence scores in patients with breast cancer.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.BMC medical imaging · 2026Article
- Factors predicting the risk of breast cancer: construction and validation of a nomogram model.Quantitative imaging in medicine and surgery · 2026Article
- Correlation between ultrasonic features and expression of immunohistochemical factors in invasive ductal carcinoma of the breast.Quantitative imaging in medicine and surgery · 2025Article
- Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer.Frontiers in oncology · 2025Article
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10 authors.
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Abstract
Background: The Oncotype DX (ODX) recurrence score (RS), a 21-gene assay, has been proven to recognize patients at high risk of recurrence (RS ≥26) who would benefit from chemotherapy. However, it has limited availability and high costs. Our study thus aimed to identify ultrasound (US) imaging biomarkers and develop a prediction model for identifying patients with a high ODX RS. Methods: In this retrospective study, consecutive patients with T1-3N0-1M0 breast cancer who were hormone receptor positive and human epidermal growth factor receptor 2 (HER2) negative who had an available ODX RS were reviewed. Patients treated from May 2012 and December 2015 were placed into a training cohort, and those treated from January 2016 to January 2017 were placed in a validation cohort. Clinicopathologic data were collected, and preoperative US scans were analyzed. Univariable and multivariable regression analyses were performed to evaluate the independent predictors for a high-risk of breast cancer in the training cohort, and a nomogram was developed and evaluated with the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: A total of 363 patients were in the training cohort and 160 in the validation cohort, with the proportion with a high RS (RS 26-100) being 14% and 13.1%, respectively. Echogenic halo, enhanced posterior echo, low level of progesterone receptor (PR), and high Ki-67 index were identified as independent risk factors for high RS (all P values <0.05). The nomogram was constructed based on the combined model, which showed a better discrimination ability than did the clinicopathological model [combined model: AUC =0.95, 95% confidence interval (CI): 0.93-0.97; clinicopathological model: AUC =0.89, 95% CI: 0.86-0.92; P=0.001] and greater clinical benefit according to DCA. Furthermore, the nomogram was found to be effective in the validation cohort (AUC =0.90, 95% CI: 0.84-0.94), especially in patients with stage T1N0M0 disease (AUC =0.91, 95% CI: 0.84-0.95). Conclusions: US features may serve as valuable imaging biomarkers for the prediction of high recurrence risk in patients with T1-3N0-1M0 breast cancer and hormone receptor (HR)-positive and HER2-negative status. A nomogram incorporating PR status, Ki-67 index, and US imaging biomarkers showed a good discrimination ability in the early selection of patients at high risk of recurrence, especially in those with stage T1N0M0 disease.
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