ArticleCancers2021
Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Narrowing of glioma vascular caliber via chronic VEGFR2 blockade improves the uniformity of focused ultrasound-mediated small molecule drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2026Article
- Spectral CT findings of bladder urothelial carcinoma with gastric metastasis: a case report.BMC urology · 2025Article
- Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging.BMC medical imaging · 2024Article
- Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications.Translational cancer research · 2024Article
- Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation.Insights into imaging · 2024Article
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Dual-Energy CT Iodine Uptake of Head and Neck: Definition of Reference Values in a Big Data Cohort.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomics from dual-energy CT-derived iodine maps predict lymph node metastasis in head and neck squamous cell carcinoma.La Radiologia medica · 2024Article
- Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach.European journal of medical research · 2023Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Thoracic Diseases: Technique and Applications of Dual-Energy CT.Diagnostics (Basel, Switzerland) · 2023Review
- How has the field of metastatic breast cancer in bones evolved over the past 22 years?Journal of bone oncology · 2023Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.Biology · 2023Review
- Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence.Cancers · 2023Review
- Differentiating gastric schwannoma from gastric stromal tumor (≤5 cm) by histogram analysis based on iodine-based material decomposition images: a preliminary study.Frontiers in oncology · 2023Article
- Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status.Quantitative imaging in medicine and surgery · 2022Article
- The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer.BMC medical imaging · 2022Article
- Detection of axillary lymph node metastasis in breast cancer using dual-layer spectral computed tomography.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.
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