ArticleBiomarker insights2018
Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.
Article in Biomarker insights, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- CanAssist Breast-based prognostication in low-intermediate estrogen receptor positive (ERTherapeutic advances in medical oncology · 2025Article
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- The usefulness of CanAssist Breast over Ki67 in breast cancer recurrence risk assessment.Cancer medicine · 2023Article
- Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial.Breast cancer research : BCR · 2023Article
- A comparative analysis of recurrence risk predictions in ER+/HER2- early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast.Breast cancer research and treatment · 2022Article
- Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.Journal of personalized medicine · 2022Review
- Robust Guidelines for the Management of HR+ Her2- EBC: Crucial Value of CanAssist Breast.South Asian journal of cancer · 2022Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Practical Consensus Recommendations for Optimizing Risk versus Benefit of Chemotherapy in Patients with HR Positive Her2 Negative Early Breast Cancer in India.South Asian journal of cancer · 2021Article
- CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario.Indian journal of surgical oncology · 2021Article
- Validation of CanAssist Breast immunohistochemistry biomarkers on an automated platform and its applicability in tissue microarray.International journal of clinical and experimental pathology · 2021Article
- Comparison of breast cancer prognostic tests CanAssist Breast and Oncotype DX.Cancer medicine · 2020Article
- Androgen Receptor mRNA levels determine the prognosis in triple-negative breast cancer patients.BMC cancer · 2020Article
- Overview of Breast Cancer and Implications of Overtreatment of Early-Stage Breast Cancer: An Indian Perspective.JCO global oncology · 2020Review
- Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.Cancer medicine · 2019Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression-based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning-based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a "CAB risk score" that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.