ArticleOncology letters2018
Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Ki-67 expression correlates with hepatocellular carcinoma recurrence and is predictable using radiomics features.Abdominal radiology (New York) · 2026Article
- Enhancing breast cancer diagnosis: non-invasive prediction of MKI-67 (Ki67) expression using ultrasound images.BMC cancer · 2025Article
- Ki-67-Playing a key role in breast cancer but difficult to apply precisely in the real world.BMC cancer · 2025Article
- Value of high frame rate contrast-enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma.Insights into imaging · 2024Article
- MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma.Cancers · 2024Article
- Article
- Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.Frontiers in oncology · 2024Article
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases.Journal of bone oncology · 2023Article
- Textural Features of Mouse Glioma Models Measured by Dynamic Contrast-Enhanced MR Images with 3D Isotropic Resolution.Tomography (Ann Arbor, Mich.) · 2023Article
- Prediction of the Ki-67 marker index in hepatocellular carcinoma based on Dynamic Contrast-Enhanced Ultrasonography with Sonazoid.Insights into imaging · 2022Article
- Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study.Molecular imaging and biology · 2022Article
- CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.Frontiers in oncology · 2022Article
- Review
- Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma.BMC medical imaging · 2021Article
- Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ.BMC medical imaging · 2021Article
- Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms.Journal of oncology · 2021Review
- A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging.Journal of Cancer · 2021Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to investigate the association between Ki-67 expression and radiomics features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with invasive breast cancer. A total of 53 cases with low-Ki-67 expression (Ki-67 proliferation index <14%) and 106 cases with high-Ki-67 expression (Ki-67 proliferation index >14%) were investigated. A systematic approach was applied that focused on the automated segmentation of lesions and extraction of radiomics features. For each lesion 5 morphology, 4 gray-scale histogram and 6 texture features were obtained, and statistical analyzes were performed to assess the differences in these features between the low- and high-Ki-67 expressions. One morphology metric (area), 3 gray-scale histogram indexes (standard deviation, skewness and kurtosis) and 3 texture features (contrast, homogeneity and inverse differential moment) demonstrated a significant difference (P<0.05), with low-Ki-67 expression lesions tending to be smaller, clearer and heterogeneous when compared with the high-Ki-67 expressed cases. These results may provide a noninvasive means to better understand the proliferation of breast cancer.
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