ArticleCancer communications (London, England)2020
Ki-67 index, progesterone receptor expression, histologic grade and tumor size in predicting breast cancer recurrence risk: A consecutive cohort study.
Article in Cancer communications (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 40 citations in OpenAlex.
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- Metabolic phenotypes of doxorubicin-induced cardiotoxicity among patients with breast cancer.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Status and trends of early-onset cancers and their risk factors in China: population-based study.Journal of global health · 2026Article
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- Virtual screening,Toxicology reports · 2025Article
- Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.Cell reports. Medicine · 2025Article
- Predicting the Ki-67 proliferation index in cervical cancer: a preliminary comparative study of four non-Gaussian diffusion-weighted imaging models combined with histogram analysis.Quantitative imaging in medicine and surgery · 2024Article
- A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer.Journal of imaging informatics in medicine · 2024Article
- 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.Quantitative imaging in medicine and surgery · 2024Article
- Identification of Patients with Early HR+ HER2- Breast Cancer at High Risk of Recurrence.Geburtshilfe und Frauenheilkunde · 2024Article
- The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.Journal of translational medicine · 2024Article
- Study on the mechanism of MDSC-platelets and their role in the breast cancer microenvironment.Frontiers in cell and developmental biology · 2024Review
- Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.Breast cancer (Dove Medical Press) · 2024Article
- Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients.Journal of translational medicine · 2023Article
- Review
- Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods.Biomedicines · 2023Article
- Factors Influencing Lymph Node Positivity in HER2/neu+ Breast Cancer Patients.Current oncology (Toronto, Ont.) · 2023Article
- Single-cell sequencing identifies differentiation-related markers for molecular classification and recurrence prediction of PitNET.Cell reports. Medicine · 2023Article
- Development and Validation of an MRI Radiomics-Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer.Breast cancer (Dove Medical Press) · 2022Article
- Identifying and Validating of an Autophagy-Related Gene Signature for the Prediction of Early Relapse in Breast Cancer.Frontiers in endocrinology · 2022Article
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 21-gene recurrence score (RS) assay has been recommended by major guidelines for treatment decision in hormone receptor (HR)-positive early breast cancer (EBC). However, the genomic assay is not accessible and affordable worldwide. Alternatively, an increasing number of studies have shown that traditional immunohistochemistry (IHC) can partially or even completely replace the role of the 21-gene genomic assay. Here, we developed and validated a predictive model (IHC3 model) combining the Ki-67 index, progesterone receptor (PR) expression, histologic grade, and tumor size to predict the recurrence risk of HR-positive EBC.
methodsThe data from 389 patients (development set) with HR-positive, human epidermal growth factor receptor 2-negative, lymph node non-metastasized invasive breast cancer were used to construct the IHC3 model based on the Surexam
resultsThe pLR and pHR group could be easily stratified using the decision tree model without network dependence. The accuracies of the IHC3 model were 86.1% in the development set and 87.7% in the validation set. The predictive accuracy of the IHC3 model and Orucevic et al.'s nomogram for the whole cohort was 86.5% and 86.9%, respectively. After a 52-month of median follow-up, a significant difference was found in IDFS between of the IHC3 pLR and the pHR groups (P = 0.001) but not in the IDFS between the low- and high-recurrence risk groups according to the Surexam® 21-gene RS and the TAILORx clinical trial criteria (P = 0.556) or 21-gene binary RS group (P = 0.511).
conclusionsThe proposed IHC3 model could reliably predict low and high recurrence risks in most HR-positive EBC patients. This easy-to-use predictive model may be a reliable replacement for the 21-gene genomic assay in patients with EBC who have no access to or cannot afford the 21-gene genomic assay.
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