| P50 | author | Dimitris Visvikis | Q91680418 |
| | Catherine Cheze Le Rest | Q47460846 |
| P2093 | author name string | Paul E Kinahan | |
| | Larry Pierce | |
| | Mathieu Hatt | |
| | Catherine Cheze Le Rest | |
| | Florent Tixier | |
| P2860 | cites work | Quantification of Intratumoral Metabolic Macroheterogeneity on 18F-FDG PET/CT and Its Prognostic Significance in Pathologic N0 Squamous Cell Lung Carcinoma. | Q52991547 |
| | Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. | Q53063662 |
| | Textural Parameters of Tumor Heterogeneity in ¹⁸F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer. | Q53068643 |
| | Novel spatial analysis method for PET images using 3D moment invariants: Applications to Parkinson's disease | Q60767139 |
| | Texture analysis of CT-images for early detection of liver malignancy | Q72009739 |
| | Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer | Q82586040 |
| | Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT | Q83082145 |
| | Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders | Q83752916 |
| | Intratumor Textural Heterogeneity on Pretreatment (18)F-FDG PET Images Predicts Response and Survival After Chemoradiotherapy for Hypopharyngeal Cancer | Q86211155 |
| | The promise and limits of PET texture analysis | Q87250885 |
| | Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis | Q87319243 |
| | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach | Q23760721 |
| | Radiomics: extracting more information from medical images using advanced feature analysis | Q23760733 |
| | False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review | Q24288664 |
| | How to make more published research true | Q24288766 |
| | Systematic review of the empirical evidence of study publication bias and outcome reporting bias - an updated review | Q26851424 |
| | State-Of-The-Art and Recent Advances in Quantification for Therapeutic Follow-Up in Oncology Using PET | Q27694674 |
| | Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing | Q27860948 |
| | Quantifying tumour heterogeneity with CT | Q28709424 |
| | Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients | Q30458538 |
| | Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review | Q30663295 |
| | A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data | Q30819319 |
| | Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features | Q30820677 |
| | Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? | Q33576007 |
| | The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake | Q33597565 |
| | Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III | Q33617538 |
| | Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology. | Q33669828 |
| | Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer | Q33753620 |
| | Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. | Q51783790 |
| | FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. | Q52886599 |
| | Quantitative assessment of tumor metabolism using FDG-PET imaging | Q34088981 |
| | Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer | Q34164634 |
| | 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort | Q34453471 |
| | Radiomics: Images Are More than Pictures, They Are Data | Q34502075 |
| | The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer. | Q34510278 |
| | A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores | Q34514621 |
| | Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients. | Q34521659 |
| | Decoding global gene expression programs in liver cancer by noninvasive imaging | Q34630291 |
| | Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer | Q34724752 |
| | Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome | Q35089180 |
| | DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. | Q35108363 |
| | Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies | Q35151263 |
| | Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. | Q35734154 |
| | Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models | Q35737796 |
| | Heterogeneity of (18)F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma | Q35756750 |
| | Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. | Q35768828 |
| | Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study | Q35850871 |
| | Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards | Q35915904 |
| | The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. | Q35916957 |
| | Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients | Q35940876 |
| | 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 |
| | Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach | Q36195557 |
| | Metrology Standards for Quantitative Imaging Biomarkers | Q36336865 |
| | Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. | Q36343075 |
| | Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation | Q36350466 |
| | Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? | Q36421023 |
| | Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors | Q36425348 |
| | Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer | Q36427534 |
| | Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer | Q36763115 |
| | Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study | Q36804482 |
| | Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non-Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235. | Q37180546 |
| | Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation | Q37230607 |
| | FDG uptake heterogeneity in FIGO IIb cervical carcinoma does not predict pelvic lymph node involvement | Q37522745 |
| | Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems. | Q37633048 |
| | Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images | Q37686674 |
| | Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. | Q37697013 |
| | Quantifying heterogeneity in human tumours using MRI and PET. | Q37977699 |
| | Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis | Q38052001 |
| | Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. | Q38271116 |
| | An update on novel quantitative techniques in the context of evolving whole-body PET imaging. | Q38279627 |
| | IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. | Q38367229 |
| | Non-Small Cell Lung Cancer Treated with Erlotinib: Heterogeneity of (18)F-FDG Uptake at PET-Association with Treatment Response and Prognosis. | Q38432601 |
| | Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications | Q38446606 |
| | Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [(18)F]FDG PET/CT scans in locally advanced rectal cancer | Q38580483 |
| | Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging | Q38794635 |
| | Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis | Q38798562 |
| | MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection | Q38956083 |
| | Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas | Q38980180 |
| | The age of reason for FDG PET image-derived indices | Q39065889 |
| | Tumor Treatment Response Based on Visual and Quantitative Changes in Global Tumor Glycolysis Using PET-FDG Imaging. The Visual Response Score and the Change in Total Lesion Glycolysis. | Q39666021 |
| | Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer | Q39929954 |
| | Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma | Q40000698 |
| | Exploring the use of shape and texture descriptors of positron emission tomography tracer distribution in imaging studies of neurodegenerative disease | Q40218040 |
| | Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET. | Q40674955 |
| | The precision of textural analysis in (18)F-FDG-PET scans of oesophageal cancer | Q40918671 |
| | Robust feature selection to predict tumor treatment outcome | Q41456435 |
| | The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from (18)F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer | Q41714333 |
| | Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. | Q41790286 |
| | Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. | Q42035081 |
| | Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments | Q42353561 |
| | Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. | Q42648185 |
| | Robustness of intratumour ¹⁸F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma | Q43753631 |
| | MR tissue characterization of intracranial tumors by means of texture analysis. | Q43755407 |
| | Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography | Q43952149 |
| | Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. | Q45035123 |
| | Intratumoral metabolic heterogeneity of cervical cancer | Q46433358 |
| | The International EQUATOR Network: enhancing the quality and transparencyof health care research | Q46466135 |
| | Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. | Q47429122 |
| | 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 |
| | Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images. | Q50897746 |
| | Intra-tumour 18F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. | Q50955765 |
| | A gradient-based method for segmenting FDG-PET images: methodology and validation. | Q51026384 |
| | Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. | Q51059595 |
| | Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. | Q51158529 |
| | An automatic method for accurate volume delineation of heterogeneous tumors in PET. | Q51175504 |
| | Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification. | Q51669590 |
| P433 | issue | 1 | |
| P304 | page(s) | 151-165 | |
| P577 | publication date | 2016-06-06 | |
| P1433 | published in | European Journal of Nuclear Medicine and Molecular Imaging | Q15708890 |
| P1476 | title | Characterization of PET/CT images using texture analysis: the past, the present… any future? | |
| P478 | volume | 44 | |