| scholarly article | Q13442814 |
| P2093 | author name string | Arthur Jochems | |
| Henry C Woodruff | |||
| Ralph Th Leijenaar | |||
| Simon A Keek | |||
| P2860 | cites work | The imperative to share clinical study reports: recommendations from the Tamiflu experience | Q21563018 |
| Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach | Q23760721 | ||
| Radiomics: extracting more information from medical images using advanced feature analysis | Q23760733 | ||
| Radiomics: the process and the challenges | Q26865378 | ||
| Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing | Q27860948 | ||
| The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups | Q29614700 | ||
| A new initiative on precision medicine | Q29615654 | ||
| Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features | Q30820677 | ||
| Big Data in radiation therapy: challenges and opportunities | Q31139104 | ||
| Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept | Q31151742 | ||
| Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT | Q31152462 | ||
| Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer | Q33674304 | ||
| Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer | Q33799553 | ||
| Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma | Q33906012 | ||
| Rapid-learning system for cancer care | Q34193994 | ||
| Volumetric CT-based segmentation of NSCLC using 3D-Slicer | Q34392512 | ||
| CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma | Q34466023 | ||
| Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer | Q34488635 | ||
| Radiomics: Images Are More than Pictures, They Are Data | Q34502075 | ||
| Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement | Q34837497 | ||
| Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. | Q35146660 | ||
| Robust Radiomics feature quantification using semiautomatic volumetric segmentation | Q35206996 | ||
| Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. | Q35251578 | ||
| Clinical implications of heterogeneity of tumor response to radiation therapy | Q35655983 | ||
| The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. | Q35916957 | ||
| Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability | Q35945509 | ||
| Machine Learning methods for Quantitative Radiomic Biomarkers | Q35960448 | ||
| Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. | Q36173696 | ||
| Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data | Q36282168 | ||
| Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier | Q36304217 | ||
| Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. | Q36343075 | ||
| Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis. | Q36350647 | ||
| Assessing the effect of CT slice interval on unidimensional, bidimensional and volumetric measurements of solid tumours | Q36361598 | ||
| Identification of residual metabolic-active areas within individual NSCLC tumours using a pre-radiotherapy (18)Fluorodeoxyglucose-PET-CT scan | Q36410337 | ||
| Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer | Q36427534 | ||
| Assessing the performance of prediction models: a framework for traditional and novel measures | Q36617895 | ||
| Reproducibility of radiomics for deciphering tumor phenotype with imaging | Q36724769 | ||
| Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology | Q36740376 | ||
| Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer | Q36763115 | ||
| Radiomic phenotype features predict pathological response in non-small cell lung cancer. | Q37060249 | ||
| A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen | Q37111842 | ||
| Respiratory motion in positron emission tomography/computed tomography: a review | Q37132031 | ||
| Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis | Q37476049 | ||
| Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma | Q37490196 | ||
| Adaptive radiotherapy for lung cancer | Q37706791 | ||
| Reproducibility of dynamic contrast-enhanced MR imaging. Part II. Comparison of intra- and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. | Q38065703 | ||
| Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. | Q38437513 | ||
| MRI features predict survival and molecular markers in diffuse lower-grade gliomas | Q38438803 | ||
| Quality assessment of positron emission tomography scans: recommendations for future multicentre trials | Q38638684 | ||
| Feature selection methodology for longitudinal cone-beam CT radiomics | Q38639487 | ||
| Defining the biological basis of radiomic phenotypes in lung cancer. | Q38668238 | ||
| Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status. | Q38688356 | ||
| Decision support systems for personalized and participative radiation oncology. | Q38698843 | ||
| Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures | Q38784854 | ||
| Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy | Q38924381 | ||
| Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography | Q38944502 | ||
| Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study | Q39042948 | ||
| Internal and external validation of predictive models: a simulation study of bias and precision in small samples | Q40556623 | ||
| Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning | Q41458682 | ||
| External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma | Q41492192 | ||
| Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy | Q41529914 | ||
| Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. | Q41550760 | ||
| A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. | Q41606953 | ||
| Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. | Q41790286 | ||
| Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? | Q41994050 | ||
| Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. | Q42648185 | ||
| Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation | Q44702085 | ||
| Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. | Q45947136 | ||
| A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme | Q46103186 | ||
| Variability in response assessment in solid tumors: effect of number of lesions chosen for measurement. | Q47446816 | ||
| Clinical Cancer Advances 2017: Annual Report on Progress Against Cancer From the American Society of Clinical Oncology. | Q47588606 | ||
| 4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers | Q47805273 | ||
| Radiomics: the bridge between medical imaging and personalized medicine | Q47869134 | ||
| Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. | Q48054500 | ||
| Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. | Q48604409 | ||
| Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models. | Q50086515 | ||
| A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. | Q50891696 | ||
| Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment. | Q52984730 | ||
| Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response. | Q53648862 | ||
| Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT. | Q55111384 | ||
| Prediction Models: Revolutionary in Principle, But Do They Do More Good Than Harm? | Q57238864 | ||
| Precision Oncology: An Overview | Q61661587 | ||
| Test-Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific? | Q64977929 | ||
| Interstitial fluid pressure in cervical carcinoma: within tumor heterogeneity, and relation to oxygen tension | Q74681606 | ||
| Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells | Q85572144 | ||
| P433 | issue | 1091 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | 20170926 | |
| P577 | publication date | 2018-07-05 | |
| P1433 | published in | British Journal of Radiology | Q15758529 |
| P1476 | title | A review on radiomics and the future of theranostics for patient selection in precision medicine | |
| P478 | volume | 91 |
| Q89665433 | Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine |
| Q90569829 | Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases |
| Q89513721 | CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging |
| Q92896253 | Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma |
| Q97533983 | FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients |
| Q64057427 | The Challenges of Diagnostic Imaging in the Era of Big Data |
Search more.