ArticleTomography (Ann Arbor, Mich.)2020
MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.
Article in Tomography (Ann Arbor, Mich.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
22 citing papers in PubMed.
- Nano-radiomics of molecular MRI for early amyloid associated patterns in an Alzheimer's disease mouse model via an automatic pipeline.BMC medical imaging · 2026Article
- Automated detection of primary soft tissue sarcomas of the extremities using artificial intelligence and ChatGPT.Frontiers in oncology · 2026Article
- Review
- 3D-NASE: A Novel 3D CT Nasal Attention-Based Segmentation Ensemble.Journal of imaging · 2025Article
- Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.Tomography (Ann Arbor, Mich.) · 2025Article
- 3D whole body preclinical micro-CT database of subcutaneous tumors in mice with annotations from 3 annotators.Scientific data · 2024Article
- Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review.Journal of personalized medicine · 2023Review
- Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture.Diagnostics (Basel, Switzerland) · 2023Article
- Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy.Cancers · 2023Article
- Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs.Cancers · 2023Article
- The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings.Tomography (Ann Arbor, Mich.) · 2023Review
- Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research.Physics and imaging in radiation oncology · 2023Article
- Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning.Tomography (Ann Arbor, Mich.) · 2023Article
- Radiomics analysis in medical imaging research.Journal of medical radiation sciences · 2023Article
- AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study.Acta neuropathologica communications · 2023Article
- Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models.PloS one · 2023Article
- Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.European radiology · 2022Article
- Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.Frontiers in bioscience (Landmark edition) · 2022Article
- A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.BMC medical imaging · 2022Article
- Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images.Frontiers in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.
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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.