Computational Radiomics System to Decode the Radiographic Phenotype
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Summary
PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images, is developed and its application in characterizing lung-lesions is demonstrated.
- Type
- article
- Published
- 2017-11-01
- Cited by
- 5,565
- References
- 18
- Access
- Open access
- OpenAlex
- https://openalex.org/W2767128594
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4859345
Keywords
Phenotype, Radiomics, Computational biology, Radiography, Medicine
References
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- Textural Features for Image Classification
- Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
- Radiomics: Extracting more information from medical images using advanced feature analysis
- Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis
- Applications and limitations of radiomics
- The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review.
- Texture indexes and gray level size zone matrix. Application to cell nuclei classification
- Deep Learning
- ANALYSIS AND INTERPRETATION OF DATA
Cited by
- Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
- Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening
- A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma
- CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy.
- Learning from scanners: Bias reduction and feature correction in radiomics
- Computer Analysis of Images and Patterns: CAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings
- Эффективен ли церебролизин при ишемическом инсульте
- Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data
- Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images
- ENVIRONMENTAL SOUND RECOGNITION USING SPECTROGRAM IMAGE FEATURES
- Automatic Segmentation and Overall Survival Prediction in Gliomas Using Fully Convolutional Neural Network and Texture Analysis
- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
- Data Analysis Strategies in Medical Imaging
- Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma.
- CT texture analysis in colorectal liver metastases and the surrounding liver parenchyma and its potential as an imaging biomarker of disease aggressiveness, response and survival.
- Radiomics in radiooncology - Challenging the medical physicist.
- Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach
- Computation of reliable textural indices from multimodal brain MRI: suggestions based on a study of patients with diffuse intrinsic pontine glioma
- A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram.
- Applying Precision Oncology Principles in Radiation Oncology.
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