Radiomics in radiooncology - Challenging the medical physicist.
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Summary
The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue.
- Type
- review
- Published
- 2018-04-01
- Cited by
- 90
- References
- 107
- OpenAlex
- https://openalex.org/W2794993152
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20391944
Keywords
Radiomics, Physicist, Medical physicist, Medical physics, Medicine
References
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- A Leisurely Look at the Bootstrap, the Jackknife, and
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- Impact of repeat computerized tomography replans in the radiation therapy of head and neck cancers
- Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?
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- Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): An Introduction to the Scientific Issues
Cited by
- A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers.
- JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.
- Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis
- Radiomics: the facts and the challenges of image analysis
- Human Glioma Migration and Infiltration Properties as a Target for Personalized Radiation Medicine
- An overview of deep learning in medical imaging focusing on MRI
- Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images
- Radiogenomics: bridging imaging and genomics
- Integrating imaging and omics data: A review
- Reliability of tumor segmentation in glioblastoma: impact on the robustness of MRI-radiomic features
- Tumor grading of soft tissue sarcomas using MRI-based radiomics
- Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction
- Optimal Mass Transport for Robust Texture Analysis
- Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis
- Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients.
- Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.
- Radiographic Image Radiomics Feature Reproducibility: A Preliminary Study on the Impact of Field Size.
- 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma
- Glioma Grade Classification via Omics Imaging
- Radiogenomics Based on PET Imaging
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