ArticleIEEE transactions on bio-medical engineering2015
Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.
Article in IEEE transactions on bio-medical engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 64 citations in OpenAlex.
- Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.Biomedical engineering online · 2023Article
- Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury.Hepatology international · 2023Article
- An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer.PeerJ. Computer science · 2023Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
- Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning.Frontiers in oncology · 2022Article
- Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.BioMed research international · 2022Article
- Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.BioMed research international · 2022Article
- Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning-Based Radiomics Nomogram With Dynamic Contrast-Enhanced MRI of the Osteosarcoma for Evaluation of Efficacy of Neoadjuvant Chemotherapy.Frontiers in oncology · 2021Article
- Precision diagnostics based on machine learning-derived imaging signatures.Magnetic resonance imaging · 2019Review
- Role of texture analysis in breast MRI as a cancer biomarker: A review.Journal of magnetic resonance imaging : JMRI · 2019Review
- Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.Medical physics · 2018Article
- Characterizing and eliminating errors in enhancement and subtraction artifacts in dynamic contrast-enhanced breast MRI: Chemical shift artifact of the third kind.Magnetic resonance in medicine · 2018Article
- Background, current role, and potential applications of radiogenomics.Journal of magnetic resonance imaging : JMRI · 2018Review
- Effects of MRI scanner parameters on breast cancer radiomics.Expert systems with applications · 2017Article
- Imaging heterogeneity of peptide delivery and binding in solid tumors using SPECT imaging and MRI.EJNMMI research · 2016Article
- Detection of Therapeutically Targetable Driver and Resistance Mutations in Lung Cancer Patients by Next-Generation Sequencing of Cell-Free Circulating Tumor DNA.Clinical cancer research : an official journal of the American Association for Cancer Research · 2016Observational
- A novel approach for next-generation sequencing of circulating tumor cells.Molecular genetics & genomic medicine · 2016Article
- How Can Advanced Imaging Be Used to Mitigate Potential Breast Cancer Overdiagnosis?Academic radiology · 2016Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
goalHeterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer.
methodsTumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay.
resultsReceiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94).
conclusionThe rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information. SIGNIFICANCE: HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.
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Registered trials
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.