ArticleMolecular imaging and biology2022
Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study.
Article in Molecular imaging and biology, 2022. 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, 37 citations in OpenAlex.
- Moran's index habitat and peritumoral radiomics based on digital breast tomosynthesis for predicting Ki-67 status in breast cancer.Quantitative imaging in medicine and surgery · 2026Article
- Noninvasive Assessment of Ki-67 Expression in Breast Cancer Using Ultrasound Radiomics: A Multi-institutional Study.Cancer research and treatment · 2026Article
- Intratumoral and peritumoral-based radiomics for assessment of lymphovascular invasion in invasive breast cancer: model development and validation.Translational cancer research · 2026Article
- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 2026Article
- Prediction of Ki-67 expression in invasive breast cancer with dual-modality radiomics.Scientific reports · 2026Article
- Construction of novel radiomics nomogram model based on preoperative CT to predict lymphovascular tumor embolus and recurrence-free survival in early T1-2a stage lung adenocarcinomas.BMC medical imaging · 2026Article
- MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.BMC medical imaging · 2026Article
- Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models -- dual-center study.BMC cancer · 2025Article
- Review
- Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.Breast cancer research : BCR · 2025Article
- Intratumoral and peritumoral radiomics based on 2D ultrasound imaging in breast cancer was used to determine the optimal peritumoral range for predicting KI-67 expression.Journal of ultrasound · 2025Article
- Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas.BMC medical imaging · 2025Article
- Bi-Regional Machine Learning Radiomics Based on CT Noninvasively Predicts LOX Expression Level and Overall Survival in Hepatocellular Carcinoma.Cancer medicine · 2025Article
- Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort.British journal of cancer · 2025Article
- Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study.BMC medical imaging · 2025Article
- Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.Breast cancer research : BCR · 2025Article
- Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.Breast cancer research : BCR · 2024Article
- Noninvasive radiomic biomarkers for predicting pseudoprogression and hyperprogression in patients with non-small cell lung cancer treated with immune checkpoint inhibition.Oncoimmunology · 2024Article
- Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.Frontiers in oncology · 2024Article
- Editorial to the Special Issue Entitled "Optical Surgical Navigation".Molecular imaging and biology · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo noninvasively evaluate the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) magnetic resonance imaging (MRI) images separately and combined to predict the Ki-67 level based on radiomics. PROCEDURES: A total of 209 patients with pathologically confirmed breast cancer were consecutively enrolled from September 2017 to March 2021, who underwent DM, DBT, DCE-MRI, and DW MRI scans. Radiomics features were calculated from intratumoral and peritumoral regions in each modality and selected with the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures (RSs) were built based on intratumoral, peritumoral, and combined intra- and peritumoral regions. The prediction performance of the RSs was evaluated using the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity as comparison metrics. A nomogram was constructed by integrating the multi-model RS and important clinical predictors and assessed by calibration and decision curve analysis.
resultsThe combined intra- and peritumoral RSs improved the AUC compared with intra- or peritumoral RSs in each modality. The DCE plus DW MRI yielded higher AUC and specificity but lower sensitivity compared with the DM plus DBT. The nomogram incorporating the multi-model RS, age, and lymph node metastasis status achieved the best prediction performance in the training (AUC, nomogram vs. fusion RS vs. clinical model, 0.922 vs. 0.917 vs. 0.672) and validation (AUCs, nomogram vs. fusion RS vs. clinical model, 0.866 vs. 0.838 vs. 0.661) cohorts. DCA analysis confirmed the potential clinical utility of the nomogram.
conclusionsPeritumoral regions can provide complementary information to intratumoral regions in mammography and MRI for the prediction of Ki-67 levels. The MRI performed better than mammography in terms of AUC and specificity but weaker in sensitivity. The nomogram has a predictive advantage over each modality and could be a potential tool for predicting Ki-67 levels in breast cancer.
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