ArticleCancer research2016
Decoding Intratumoral Heterogeneity of Breast Cancer by Multiparametric In Vivo Imaging: A Translational Study.
Article in Cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 35 citations in OpenAlex.
- Combined Metabolic and Functional Tumor Volumes on [Journal of clinical medicine · 2023Article
- From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies.Biology · 2023Review
- Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.Nature biomedical engineering · 2023Article
- Advances in PET imaging of cancer.Nature reviews. Cancer · 2023Review
- Evaluation of functional and metabolic tumor volume using voxel-wise analysis in childhood rhabdomyosarcoma.Pediatric radiology · 2023Article
- Intratumoral in vivo staging of breast cancer by multi-tracer PET and advanced analysis.NPJ breast cancer · 2022Article
- Clinical advances in PET-MRI for breast cancer.The Lancet. Oncology · 2022Review
- Article
- Early Prediction of Tumor Response to Neoadjuvant Chemotherapy and Clinical Outcome in Breast Cancer Using a Novel FDG-PET Parameter for Cancer Stem Cell Metabolism.Journal of personalized medicine · 2020Article
- Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer.Tomography (Ann Arbor, Mich.) · 2020Article
- Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14-16, 2018; Vienna, Austria.Molecular imaging and biology · 2020Article
- Clustering approach to identify intratumour heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma.European radiology · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
- Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients.BMC cancer · 2018Article
- Standardization of Small Animal Imaging-Current Status and Future Prospects.Molecular imaging and biology · 2018Review
- Fluorine-19 Magnetic Resonance Imaging and Positron Emission Tomography of Tumor-Associated Macrophages and Tumor Metabolism.Contrast media & molecular imaging · 2017Article
- Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data.Contrast media & molecular imaging · 2017Article
- PET and MRI: Is the Whole Greater than the Sum of Its Parts?Cancer research · 2016Article
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Authors and funding
20 authors at 3 institutions in 2 countries.
Funding
Abstract
Differential diagnosis and therapy of heterogeneous breast tumors poses a major clinical challenge. To address the need for a comprehensive, noninvasive strategy to define the molecular and functional profiles of tumors in vivo, we investigated a novel combination of metabolic PET and diffusion-weighted (DW)-MRI in the polyoma virus middle T antigen transgenic mouse model of breast cancer. The implementation of a voxelwise analysis for the clustering of intra- and intertumoral heterogeneity in this model resulted in a multiparametric profile based on [(18)F]Fluorodeoxyglucose ([(18)F]FDG)-PET and DW-MRI, which identified three distinct tumor phenotypes in vivo, including solid acinar, and solid nodular malignancies as well as cystic hyperplasia. To evaluate the feasibility of this approach for clinical use, we examined estrogen receptor-positive and progesterone receptor-positive breast tumors from five patient cases using DW-MRI and [(18)F]FDG-PET in a simultaneous PET/MRI system. The postsurgical in vivo PET/MRI data were correlated to whole-slide histology using the latter traditional diagnostic standard to define phenotype. By this approach, we showed how molecular, structural (microscopic, anatomic), and functional information could be simultaneously obtained noninvasively to identify precancerous and malignant subtypes within heterogeneous tumors. Combined with an automatized analysis, our results suggest that multiparametric molecular and functional imaging may be capable of providing comprehensive tumor profiling for noninvasive cancer diagnostics. Cancer Res; 76(18); 5512-22. ©2016 AACR.
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