ArticlePloS one2014
Multiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue.
Article in PloS one, 2014. 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.
- A Non-Contrast Multi-Parametric MRI Biomarker for Assessment of MR-Guided Focused Ultrasound Thermal Therapies.IEEE transactions on bio-medical engineering · 2024Article
- Simultaneous proton resonance frequency TMagnetic resonance in medicine · 2023Article
- Histology to 3D in vivo MR registration for volumetric evaluation of MRgFUS treatment assessment biomarkers.Scientific reports · 2021Article
- Gradually evaluating of suicidal risk in depression by semi-supervised cluster analysis on resting-state fMRI.Brain imaging and behavior · 2021Article
- Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors.IEEE transactions on bio-medical engineering · 2021Article
- TPediatric radiology · 2020Article
- Article
- Evaluation of pancreatic tumor development in KPC mice using multi-parametric MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2018Article
- MR-guided biopsy and focal therapy: new options for prostate cancer management.Current opinion in urology · 2018Review
- Value of diffusion-weighted imaging for monitoring tissue change during magnetic resonance-guided high-intensity focused ultrasound therapy in bone applications: anEuropean radiology experimental · 2018Article
- Assessment of acute thermal damage volumes in muscle using magnetization-prepared 3D TJournal of magnetic resonance imaging : JMRI · 2017Article
- Thermal combination therapies for local drug delivery by magnetic resonance-guided high-intensity focused ultrasound.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation.Molecular imaging and biology · 2017Article
- Quantitative Multi-Parametric Magnetic Resonance Imaging of Tumor Response to Photodynamic Therapy.PloS one · 2016Article
- Evaluation of MRI protocols for the assessment of lumbar facet joints after MR-guided focused ultrasound treatment.Journal of therapeutic ultrasound · 2016Article
- Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast.Magma (New York, N.Y.) · 2015Article
- Development of a high-field MR-guided HIFU setup for thermal and mechanical ablation methods in small animals.Journal of therapeutic ultrasound · 2015Article
- Magnetic resonance imaging assessment of effective ablated volume following high intensity focused ultrasound.PloS one · 2015Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified.
methodsMultiparametric MRI, consisting of quantitative T1, T2, Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Ratio (MTR) mapping, was performed in tumor-bearing mice before (n = 14), 1 h after (n = 14) and 72 h (n = 7) after HIFU treatment. A non-treated control group was included (n = 7). Cluster analysis using the Iterative Self Organizing Data Analysis (ISODATA) technique was performed on subsets of MRI parameters (feature vectors). The clusters resulting from the ISODATA segmentation were divided into a viable and non-viable class based on the fraction of pixels assigned to the clusters at the different experimental time points. ISODATA-derived non-viable tumor fractions were quantitatively compared to histology-derived non-viable tumor volume fractions.
resultsThe highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T1, T2, ADC}. R1 (1/T1), R2 (1/T2), ADC and MTR each were significantly increased in the ISODATA-defined non-viable tumor tissue at 1 h after HIFU treatment compared to viable, non-treated tumor tissue. R1, ADC and MTR were also significantly increased at 72 h after HIFU.
conclusionsThis study demonstrates that non-viable, HIFU-treated tumor tissue can be distinguished from viable, non-treated tumor tissue using multiparametric MRI analysis. Clinical application of the presented methodology may allow for automated, accurate and objective evaluation of HIFU treatment.
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