ArticleMolecular imaging and biology2017
A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation.
Article in Molecular imaging and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors.NMR in biomedicine · 2025Article
- 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
- Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer.Cancers · 2022Article
- Article
- An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.Scientific reports · 2020Article
- Data-driven identification of tumor subregions based on intravoxel incoherent motion reveals association with proliferative activity.Magnetic resonance in medicine · 2019Article
- Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.Cancer research · 2019Article
- Running in the wheel: Defining individual severity levels in mice.PLoS biology · 2018Article
- Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI.Magnetic resonance in medicine · 2018Article
- Linking imaging to omics utilizing image-guided tissue extraction.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposeWe aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies. PROCEDURES: Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2-3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.
resultsWhile the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r
conclusionsThe precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.
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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.