ArticleProceedings of the National Academy of Sciences of the United States of America2018
Linking imaging to omics utilizing image-guided tissue extraction.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective.Molecular cancer · 2024Review
- Machine learning identifies multi-parametric functional PET/MR imaging cluster to predict radiation resistance in preclinical head and neck cancer models.European journal of nuclear medicine and molecular imaging · 2023Article
- Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data.Nature biomedical engineering · 2023Article
- Mapping tissue heterogeneity in solid tumours using PET-MRI and machine learning.Nature biomedical engineering · 2023Article
- Advances in PET imaging of cancer.Nature reviews. Cancer · 2023Review
- A data management infrastructure for the integration of imaging and omics data in life sciences.BMC bioinformatics · 2022Article
- Construction of a Diagnostic Model for Lymph Node Metastasis of the Papillary Thyroid Carcinoma Using Preoperative Ultrasound Features and Imaging Omics.Journal of healthcare engineering · 2022Article
- DJ-1 (Park7) affects the gut microbiome, metabolites and the development of innate lymphoid cells (ILCs).Scientific reports · 2020Article
- Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform.JCO clinical cancer informatics · 2020Article
- Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.Cancer research · 2019Article
- Artificial intelligence for precision oncology: beyond patient stratification.NPJ precision oncology · 2019Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characterize a disease. However, this requires spatially accurate coregistration of these data by image-driven sampling as well as fast sample-preparation methods. Here, a unique image-guided milling machine and workflow for precise extraction of tissue samples from small laboratory animals or excised organs has been developed and evaluated. The samples can be delineated on tomographic images as volumes of interest and can be extracted with a spatial accuracy better than 0.25 mm. The samples remain cooled throughout the procedure to ensure metabolic stability, a precondition for accurate in vitro analysis.
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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.