Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Quantification Of Heterogeneity As A Biomarker In Tumor Imaging: A Systematic Review

L. Alic, W. Niessen, J. Veenland
Published 2014 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
Share
Background Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice. Methodology The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared. Principal Findings Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description. Conclusions In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.
This paper references
10.1155/2011/732848
Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review
Xiangyu Yang (2011)
10.2214/AJR.11.7336
Nasopharyngeal carcinoma: investigation of intratumoral heterogeneity with FDG PET/CT.
Bingsheng Huang (2012)
10.1007/s10278-009-9179-7
Temporal Analysis of Tumor Heterogeneity and Volume for Cervical Cancer Treatment Outcome Prediction: Preliminary Evaluation
J. Prescott (2009)
Dose-escalation by boosting radiation dose within the primary tumor on the basis of a pre-treatment FDG-PET-CT scan in stage II and III NSCLC: A randomized phase II trial
J. Belderbos (2010)
10.1016/j.artmed.2010.04.004
Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns
D. Iakovidis (2010)
10.1259/dmfr/83345935
Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa.
J. Raja (2012)
10.1055/s-0029-1245992
[Image analysis in the differential diagnosis of renal parenchyma lesions].
J. Tůma (2011)
10.1007/s11307-010-0441-7
Monitoring the Longitudinal Intra-tumor Physiological Impulse Response to VEGFR2 Blockade in Breast Tumors Using DCE-CT
K. Stantz (2010)
10.1007/s10278-009-9185-9
Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography
Y. Zhu (2009)
10.3109/0284186X.2012.731525
Distinguishing radiation fibrosis from tumour recurrence after stereotactic ablative radiotherapy (SABR) for lung cancer: A quantitative analysis of CT density changes
Sarah A. Mattonen (2013)
10.1016/S0929-8266(98)00070-6
Follow-up of Wilms' tumour during pre-operative chemotherapy by qualitative and quantitative sonography.
M. Engelbrecht (1998)
10.1148/RADIOLOGY.177.1.2399318
Lymphomas: MR imaging contrast characteristics with clinical-pathologic correlations.
W. Negendank (1990)
10.1016/j.cmpb.2009.07.003
A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images
D. Glotsos (2010)
10.1007/s00330-002-1785-4
Dynamic contrast-enhanced MR imaging in monitoring response to isolated limb perfusion in high-grade soft tissue sarcoma: initial results
C. S. P. V. Rijswijk (2003)
10.1109/TPAMI.1984.4767557
Multiple Resolution Texture Analysis and Classification
Shmuel Peleg (1984)
10.1102/1470-7330.2011.0022
Volume and attenuation computed tomography measurements for interim evaluation of Hodgkin and follicular lymphoma as an additional surrogate parameter for more confident response monitoring: a pilot study
D. Spira (2011)
10.1016/J.IJROBP.2005.04.052
Dynamic contrast-enhanced magnetic resonance imaging of radiation therapy-induced microcirculation changes in rectal cancer.
Q. D. de Lussanet (2005)
10.1016/J.ULTRASMEDBIO.2005.07.009
Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound.
S. Tsantis (2005)
10.2967/jnumed.110.082404
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer
F. Tixier (2011)
10.1111/j.1755-3768.1992.tb04920.x
Echographic differentation of intraocular melanomas by computer analysis
J. M. Thijssen (1992)
10.1016/j.neuroimage.2009.09.049
An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging
S. Drabycz (2010)
10.1093/BIOSTATISTICS/4.3.433
A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data.
F. O’Sullivan (2003)
10.1016/j.rx.2012.01.011
[The heterogeneity of blood flow on magnetic resonance imaging: a biomarker for grading cerebral astrocytomas].
A. R. Revert Ventura (2014)
10.1016/S0360-3016(97)00101-6
Tumor hypoxia adversely affects the prognosis of carcinoma of the head and neck.
D. Brizel (1997)
10.3233/978-1-61499-276-9-169
MRI Texture Analysis in Paediatric Oncology: A Preliminary Study
Ahmed E. Fetit (2013)
10.1097/00004728-200207000-00017
Fractal Analysis of Small Peripheral Pulmonary Nodules in Thin-section CT: Evaluation of the Lung-nodule Interfaces
S. Kido (2002)
10.1016/j.compbiomed.2009.10.005
Pleural nodule identification in low-dose and thin-slice lung computed tomography
A. Retico (2009)
10.1055/s-0031-1299331
Automatic texture-based analysis in ultrasound imaging of ovarian masses.
F. Faschingbauer (2013)
10.1177/000348949810700811
Image Analysis of Benign and Malignant Neck Masses
R. Plant (1998)
10.1007/s10916-012-9869-4
Computer Aided Diagnosis System for Breast Cancer Based on Color Doppler Flow Imaging
Y. Liu (2012)
Quantification and classification of echographic findings in the thyroid gland by computerized B-mode texture analysis.
T. Hirning (1989)
Computeraided diagnosis for the classification of breast masses in automated whole breast ultrasound images
Wk Moon (2011)
10.1007/s10278-012-9547-6
Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography
Haifeng Wu (2012)
10.1007/s00330-013-2965-0
Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy
M. Ravanelli (2013)
10.4015/S1016237210001852
LEARNING PATTERNS OF LIVER MASSES USING IMPROVED RBF NETWORKS
Chien-Cheng Lee (2010)
10.1016/S0895-6111(99)00033-6
The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography.
M. McNitt-Gray (1999)
10.1155/2012/348135
Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images
D. Mitrea (2012)
10.1111/J.1740-8261.2010.01748.X
Computed tomographic characteristics of intrapelvic masses in dogs.
Daniel I Spector (2011)
10.1016/j.ejca.2011.12.025
Quantifying heterogeneity in human tumours using MRI and PET.
M. Asselin (2012)
10.1002/mrm.10496
Textural analysis of contrast‐enhanced MR images of the breast
P. Gibbs (2003)
10.7863/jum.2005.24.5.651
Quantitatively Characterizing the Textural Features of Sonographic Images for Breast Cancer With Histopathologic Correlation
S. Chen (2005)
10.1088/0031-9155/48/22/008
Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images.
H. Yoshida (2003)
10.1002/mrm.21347
Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images
W. Chen (2007)
10.1148/RADIOGRAPHICS.22.4.G02JL16963
Computer-aided diagnosis scheme for detection of polyps at CT colonography.
H. Yoshida (2002)
10.1148/radiol.12120254
Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival.
F. Ng (2013)
10.1586/era.12.85
Breast cancer intratumor genetic heterogeneity: causes and implications
C. Ng (2012)
From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer
P Lambin (2010)
10.2967/jnumed.108.053397
Spatial Heterogeneity in Sarcoma 18F-FDG Uptake as a Predictor of Patient Outcome
J. Eary (2008)
10.1016/j.ejrad.2012.10.023
Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?
F. Ng (2013)
10.1002/mrm.24644
Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment
T. Larkin (2014)
10.1016/0730-725X(95)00006-3
MR classification of brain gliomas: value of magnetization transfer and conventional imaging.
T. Kurki (1995)
10.1016/j.ejrad.2011.04.045
Computer-aided diagnosis in breast DCE-MRI--quantification of the heterogeneity of breast lesions.
U. Preim (2012)
Optimization of time-topeak analysis for differentiating malignant and benign breast lesions with dynamic contrast-enhanced MRI
F Liu (2011)
10.1088/0031-9155/58/2/187
Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography.
J. Willaime (2013)
10.3109/03091902.2013.794869
A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound
Jitendra Virmani (2013)
10.1080/03091900701455524
A wavelet-based optimal texture feature set for classification of brain tumours
M. Sasikala (2008)
10.1038/labinvest.2013.128
Quantitative assessment Ki-67 score for prediction of response to neoadjuvant chemotherapy in breast cancer
J. R. Brown (2014)
10.1007/s10278-012-9506-2
Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images
Megan Rakoczy (2012)
10.1016/j.mri.2012.04.026
Microcirculatory fraction (MCF(I)) as a potential imaging marker for tumor heterogeneity in breast cancer.
Xiangyu Yang (2012)
10.1016/j.cmpb.2007.10.007
Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features
P. Georgiadis (2008)
10.1097/MNM.0b013e32835ae50c
Three-dimensional positron emission tomography image texture analysis of esophageal squamous cell carcinoma: relationship between tumor 18F-fluorodeoxyglucose uptake heterogeneity, maximum standardized uptake value, and tumor stage
Xinzhe Dong (2013)
10.1016/J.ACRA.2005.01.018
Computer-aided Diagnosis of the Solitary Pulmonary Nodule1
Sumit K. Shah (2005)
10.1038/sj.bjc.6605041
Inhibition of tyrosine kinase receptors by SU6668 promotes abnormal stromal development at the periphery of carcinomas
P. Farace (2009)
10.1038/SJ.DMFR.4600295
Ultrasonographic texture characterization of salivary and neck masses using two-dimensional gray-scale clustering.
K. Yoshiura (1997)
10.2967/jnumed.112.107375
Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?
G. Cook (2013)
10.1073/pnas.1114033109
Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response
N. Szerlip (2012)
10.2214/AJR.12.9545
Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images.
K. Downey (2013)
10.1007/s12194-009-0062-5
Application of an artificial neural network to the computer-aided differentiation of focal liver disease in MR imaging
X. Zhang (2009)
10.1118/1.3566064
Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images.
Y. Liao (2011)
10.1034/J.1600-0455.2003.00061.X
Pulmonary nodule detection using chest CT images.
D-Y. Kim (2003)
10.1002/NBM.1091
Apparent diffusion coefficient histograms may predict low‐grade glioma subtype
D. Tozer (2007)
10.1016/j.ultrasmedbio.2011.01.006
Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images.
W. K. Moon (2011)
10.1007/s00330-011-2182-7
Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging
Chunyan Cui (2011)
10.1177/028418519503600204
Texture Analysis in Quantitative MR Imaging
L. Kjær (1995)
10.1016/j.ultrasmedbio.2010.06.009
Computer aided diagnosis of parotid gland lesions using ultrasonic multi-feature tissue characterization.
S. Siebers (2010)
10.1006/uimg.1993.1017
Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis.
B. Garra (1993)
10.1097/00004728-200301000-00011
Fractal Analysis of Internal and Peripheral Textures of Small Peripheral Bronchogenic Carcinomas in Thin-section Computed Tomography: Comparison of Bronchioloalveolar Cell Carcinomas With Nonbronchioloalveolar Cell Carcinomas
S. Kido (2003)
10.1002/nbm.2962
Texture‐based and diffusion‐weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla
J. Fruehwald-Pallamar (2013)
10.1118/1.1429239
Computerized diagnosis of breast lesions on ultrasound.
K. Horsch (2002)
10.1118/1.3151811
STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis
Y. Zheng (2009)
Quantification of heterogeneity as a biomarker in tumour imaging: a systematic review
L Alic (2011)
10.1002/mrm.22003
Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps
C. J. Rose (2009)
10.1016/J.ACRA.2005.07.014
Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images.
Y. Huang (2006)
10.1016/j.ijrobp.2012.10.017
Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy.
Shan Tan (2013)
10.1038/NCOMMS5644
Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1016/j.acra.2009.01.029
Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques.
C. McLaren (2009)
10.1109/TMI.2011.2160984
A Statistical Modeling Approach to the Analysis of Spatial Patterns of FDG-PET Uptake in Human Sarcoma
F. O'Sullivan (2011)
10.1258/ar.2011.110221
Subtype differentiation of small renal cell carcinomas on three-phase MDCT: usefulness of the measurement of degree and heterogeneity of enhancement
S. C. Jung (2012)
10.1109/TSMC.1973.4309314
Textural Features for Image Classification
R. Haralick (1973)
10.3174/ajnr.A2806
Distinguishing between Germinomas and Pineal Cell Tumors on MR Imaging
N. Dumrongpisutikul (2012)
10.1158/1078-0432.CCR-12-1307
Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer
T. Win (2013)
10.1016/S1076-6332(99)80115-9
Computerized analysis of lesions in US images of the breast.
M. Giger (1999)
10.1016/S0301-5629(00)00274-X
Relevance of sonographic B-mode criteria and computer-aided ultrasonic tissue characterization in differential/diagnosis of solid breast masses.
S. Huber (2000)
10.1007/s00330-011-2319-8
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
10.1016/j.crad.2011.08.012
Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival.
B. Ganeshan (2012)
10.1038/bjc.2011.191
DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6
J. O'Connor (2011)
10.1155/2012/195176
Can Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined with Texture Analysis Differentiate Malignant Glioneuronal Tumors from Other Glioblastoma?
P. Eliat (2012)
10.1007/s10278-012-9537-8
SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors
Jitendra Virmani (2012)
10.1109/21.44046
Textural features corresponding to textural properties
Moses Amadasun (1989)
10.1016/J.COMPMEDIMAG.2004.04.003
Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound.
N. Piliouras (2004)
10.1007/s13244-012-0196-6
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Fergus Davnall (2012)
10.1002/jmri.22836
Early (72‐Hour) detection of radiotherapy‐induced changes in an experimental tumor model using diffusion‐weighted imaging, diffusion tensor imaging, and Q‐space imaging parameters: A comparative study
F. Peeters (2012)
10.1016/j.clinimag.2011.10.018
Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation.
S. Chen (2012)
10.1016/S1076-6332(03)80044-2
Support vector machines for diagnosis of breast tumors on US images.
R. Chang (2003)
10.1109/TITB.2003.813793
A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier
M. Gletsos (2003)
10.1007/BF03190341
Comparative Analysis of Texture Characteristics of Malignant and Benign Tumors in Breast Ultrasonograms
K. Kim (2010)
10.1016/j.compmedimag.2010.11.003
Computer-aided diagnosis with textural features for breast lesions in sonograms
D. Chen (2011)
10.2307/2323761
Fractal Geometry of Nature
B. Mandelbrot (1977)
10.1155/2012/853030
Erratum to “Progress in Therapy Development for Amyotrophic Lateral Sclerosis”
Kalina Venkova-Hristova (2012)
10.1002/1522-2586(200012)12:6<1027::AID-JMRI31>3.0.CO;2-5
Pixel analysis of MR perfusion imaging in predicting radiation therapy outcome in cervical cancer
N. Mayr (2000)
10.1002/uog.1951
Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems
Y. Huang (2005)
10.1118/1.598603
A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results.
M. McNitt-Gray (1999)
10.1118/1.1649531
Computerized characterization of breast masses on three-dimensional ultrasound volumes.
B. Sahiner (2004)
10.1007/s00330-009-1616-y
Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement
D. Newell (2009)
10.2214/AJR.11.7093
Diffusion-weighted imaging of breast masses: comparison of diagnostic performance using various apparent diffusion coefficient parameters.
M. Hirano (2012)
10.1007/BF00256050
Texture analysis — A new method of differentiating prostatic carcinoma from prostatic hypertrophy
C. Kratzik (2004)
10.1016/j.ultrasmedbio.2008.08.017
Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management.
S. Chen (2009)
10.5405/jmbe.1183
Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.
Mahesh B. Nagarajan (2013)
10.1002/nbm.2882
Analysis of parametric histogram from dynamic contrast‐enhanced MRI: application in evaluating brain tumor response to radiotherapy
S. Peng (2013)
10.1016/j.acra.2008.06.005
Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.
K. Nie (2008)
Computational methods of feature
H Liu (2008)
10.1016/S0016-5107(02)70131-4
Computer-assisted analysis of lymph nodes detected by EUS in patients with esophageal carcinoma.
D. Loren (2002)
10.1002/jmri.10147
Analysis of the spatial characteristics of metabolic abnormalities in newly diagnosed glioma patients
X. Li (2002)
10.1109/TMI.2012.2206398
Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound
Walter Gómez (2012)
10.1055/S-2008-1032713
Diagnostik fokaler Leberläsionen durch Texturanalyse von dynamischen Computertomogrammen
Klein Hm (1993)
10.1016/j.radonc.2010.07.001
The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer.
P. Lambin (2010)
10.1002/cnm.2481
A dual neural network ensemble approach for multiclass brain tumor classification.
J. Sachdeva (2012)
10.1118/1.3140589
Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features.
Ted Way (2009)
10.1109/TPAMI.2002.1017623
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
T. Ojala (2002)
10.1016/J.ULTRASMEDBIO.2005.06.011
Sonohistology for the computerized differentiation of parotid gland tumors.
U. Scheipers (2005)
10.1371/journal.pone.0063559
Computer-Aided Diagnosis for Early-Stage Lung Cancer Based on Longitudinal and Balanced Data
T. Sun (2013)
10.1016/j.mri.2010.11.006
Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition.
P. Georgiadis (2011)
10.1016/j.ultrasmedbio.2012.09.020
Computer-aided diagnosis for 3-d power Doppler breast ultrasound.
Yi-Chen Lai (2013)
10.1016/j.artmed.2007.05.002
Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers
S. Mougiakakou (2007)
10.1109/58.753018
Analysis and classification of tissue with scatterer structure templates
K. Donohue (1999)
10.1016/j.ijrobp.2010.11.022
Tumor metabolism and perfusion in head and neck squamous cell carcinoma: pretreatment multimodality imaging with 1H magnetic resonance spectroscopy, dynamic contrast-enhanced MRI, and [18F]FDG-PET.
Jacobus F.A. Jansen (2012)
10.1177/028418519303400102
Quantification of Inhomogeneities in Malignancy Grading of Non-Hodgkin Lymphoma with Mr Imaging
S. Rehn (1993)
10.3109/10428199709055587
Tumour inhomogeneities on magnetic resonance imaging, a new factor with prognostic information in non-Hodgkin's lymphomas.
S. Rehn (1997)
10.7785/tcrt.2012.500255
Monitoring Anti-Angiogenic Therapy in Colorectal Cancer Murine Model using Dynamic Contrast-Enhanced MRI — Comparing Pixel-by-Pixel with Region of Interest Analysis
C. Haney (2013)
Classification, Estimation and Pattern Recognition
T. Young (1974)
10.1097/00004728-199801000-00007
Peripheral enhancement and spatial contrast uptake heterogeneity of primary breast tumours: quantitative assessment with dynamic MRI.
S. Mussurakis (1998)
10.1259/bjr/50743919
Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis.
A. Karahaliou (2010)
10.1007/s11060-012-1010-5
Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade
K. Skogen (2012)
10.1097/00004728-199907000-00024
Heterogeneity analysis of Gd-DTPA uptake: improvement in breast lesion differentiation.
B. Issa (1999)
10.1016/j.radonc.2011.10.014
Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer.
M. Vaidya (2012)
10.1118/1.596804
Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence.
V. Goldberg (1992)
10.1016/j.compmedimag.2011.01.007
Neural network based focal liver lesion diagnosis using ultrasound images
Deepti Mittal (2011)
10.1016/J.ULTRASMEDBIO.2005.05.012
Sonographic texture characterization of salivary gland tumors by fractal analyses.
T. Chikui (2005)
10.1148/radiol.11110264
Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker.
V. Goh (2011)
10.1002/NBM.756
Assessing changes in tumour vascular function using dynamic contrast‐enhanced magnetic resonance imaging
C. Hayes (2002)
10.1148/radiol.2502071879
Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival.
K. Miles (2009)
10.1118/1.3689811
Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis.
K. Yan (2012)
10.1038/bjc.2012.581
Cancer heterogeneity: implications for targeted therapeutics
R. Fisher (2013)
10.1016/S0301-5629(02)00528-8
Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.
D. Chen (2002)
10.1016/j.acra.2012.04.015
Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images.
Rebekah H. Gensure (2012)
10.2174/1874431101105010026
Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features
Yanni Su (2011)
10.4015/S1016237201000200
USING A FUZZY ENGINE AND COMPLETE SET OF FEATURES FOR HEPATIC DISEASES DIAGNOSIS: INTEGRATING CONTRAST AND NON-CONTRAST CT IMAGES
E-Liang Chen (2001)
10.1016/j.cmpb.2013.04.016
Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set
T. Sun (2013)
10.4172/1948-5956.1000151
Characterizing at-Risk Voxels by Using Perfusion Magnetic Resonance Imaging for Cervical Cancer during Radiotherapy.
Zhibin Huang (2012)
10.1102/1470-7330.2010.0021
Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage
B. Ganeshan (2010)
10.1016/j.bspc.2013.06.011
Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image
Shichong Zhou (2013)
10.1002/jmri.22095
Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images
Jaber Juntu (2010)
10.1186/1471-2407-11-486
Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment
P. Fasching (2011)
10.1148/radiol.2473070571
Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps.
K. Emblem (2008)
10.1007/s11548-013-0813-y
Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images
Konstantinos Sidiropoulos (2013)
10.1016/S0895-6111(02)00027-7
Texture analysis of lesions in breast ultrasound images.
R. Sivaramakrishna (2002)
10.1016/j.lungcan.2011.06.003
Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation.
H. Aerts (2012)
10.1259/bjr/13374146
A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer.
D M L Tse (2012)
10.1016/0031-3203(95)00067-4
A comparative study of texture measures with classification based on featured distributions
T. Ojala (1996)
10.1142/5965
The Dissimilarity Representation for Pattern Recognition - Foundations and Applications
E. Pekalska (2005)
The Representations of Sonographic Image Texture for Breast Cancer Using Co-occurrence Matrix
Shao Jer Chen (2005)
10.1201/9781584888796
Computational Methods of Feature Selection
H. Liu (2007)
Computer-aided diagnosis of the solitary pulmonary nodule.
Sumit K. Shah (2005)
10.1016/S1076-6332(03)80349-5
Computer-aided diagnosis of breast tumors with different US systems.
Wen-Jia Kuo (2002)
10.1016/S0301-5629(99)00156-8
Breast cancer diagnosis using self-organizing map for sonography.
D. Chen (2000)
10.1016/0301-5629(91)90120-L
Echographic differentiation of histological types of intraocular melanoma.
J. Thijssen (1991)
10.1007/s10334-003-0027-3
Magnetic resonance diffusion imaging of ovarian masses: a first experience with 12 cases
G. Sarty (2003)
10.3109/03091902.2012.712199
Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices
A. Padma (2013)
10.1016/j.acra.2009.08.012
Characterization of breast cancer types by texture analysis of magnetic resonance images.
K. Holli (2010)
10.1097/RUQ.0b013e318262594a
Fuzzy-Based Classification of Breast Lesions Using Ultrasound Echography and Elastography
S. Selvan (2012)
10.1055/s-0028-1109917
[Classification of solid soft tissue tumours by ultrasonography].
M. Schulte (2010)
10.1016/j.acra.2008.01.010
Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features.
W. Wu (2008)
Classificatioo of MR Tumor Images Based on Gabor Wavelet Analysis
Yihui Liu (2012)
Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods
Παντελής Γεωργιάδης (2015)
10.1016/j.mri.2008.02.013
Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images? Objective evaluation by means of texture analysis.
M. Mayerhoefer (2008)
10.1016/S0165-0270(96)00080-5
Fractal methods and results in cellular morphology — dimensions, lacunarity and multifractals
T. G. Smith (1996)
10.1016/j.ejrad.2009.01.024
Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image.
H. Wang (2010)
10.1016/j.ultrasmedbio.2010.08.019
Classification of the thyroid nodules based on characteristic sonographic textural feature and correlated histopathology using hierarchical support vector machines.
S. Chen (2010)
Quantitative analysis of 3D US images in the relationship with liver lesion diagnosis
E Rokita (2009)
Breast cancer diagnosis using selforganizing map for sonography
D Chen (2000)
10.1148/RADIOLOGY.213.2.R99NV13407
Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.
D. Chen (1999)
10.1371/journal.pone.0060338
Traditional Chinese Medicine in Cancer Care: A Review of Controlled Clinical Studies Published in Chinese
X. Li (2013)
10.1118/1.3110069
A new automated method for the segmentation and characterization of breast masses on ultrasound images.
J. Cui (2009)
10.1118/1.2210568
Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
W. Chen (2006)
Is magnetic resonance imaging texture analysis a useful tool for cell therapy in vivo monitoring?
P. Eliat (2001)
10.7863/jum.2010.29.9.1345
Digital Image Analysis Is a Useful Adjunct to Endoscopic Ultrasonographic Diagnosis of Subepithelial Lesions of the Gastrointestinal Tract
V. X. Nguyen (2010)
10.1016/S0301-5629(02)00788-3
Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.
R. Chang (2003)
10.3174/ajnr.A2161
Differentiation among Glioblastoma Multiforme, Solitary Metastatic Tumor, and Lymphoma Using Whole-Tumor Histogram Analysis of the Normalized Cerebral Blood Volume in Enhancing and Perienhancing Lesions
J. Ma (2010)
10.1016/j.ultrasmedbio.2011.12.006
Heterogeneity of microbubble accumulation: a novel approach to discriminate between well-differentiated hepatocellular carcinomas and regenerative nodules.
H. Maruyama (2012)
10.1118/1.2207129
Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
Ted Way (2006)
10.1016/S0301-5629(02)00620-8
Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.
D. Chen (2002)
10.1102/1470-7330.2013.0033
Does volume perfusion computed tomography enable differentiation of metastatic and non-metastatic mediastinal lymph nodes in lung cancer patients? A feasibility study
D. Spira (2013)
[The diagnosis of focal liver lesions by the texture analysis of dynamic computed tomograms].
H. M. Klein (1993)
10.1016/J.COMPMEDIMAG.2005.11.004
Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms
M. Strzelecki (2006)
10.1593/TLO.12385
Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo.
Ali Sadeghi-Naini (2013)
Classification, estimation, and pattern recognition: American Elsevier Pub. Co
TY Young (1974)
Computer-aided diagnosis applied to US of solid breast nodules
DR Chen (1999)
10.7785/tcrt.2012.500214
Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms
U. Acharya (2011)
10.7863/jum.2011.30.9.1259
Quantitative Measurement for Thyroid Cancer Characterization Based on Elastography
J. Ding (2011)
10.1002/mrm.22147
Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme
E. I. Zacharaki (2009)
10.1016/J.ULTRASMEDBIO.2005.01.014
3-D ultrasound texture classification using run difference matrix.
Wei-Ming Chen (2005)
10.1016/j.ijsu.2010.02.007
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.
D. Moher (2010)
10.1002/mrm.24590
Pattern analysis accounts for heterogeneity observed in MRI studies of tumor angiogenesis
M. Dominietto (2013)
10.1016/S0146-664X(75)80008-6
Texture analysis using gray level run lengths
M. Galloway (1974)
10.1016/S0887-2171(00)90025-8
Texture analysis of breast tumors on sonograms.
D. Chen (2000)
10.2214/AJR.178.2.1780367
Enhancement characteristics of papillary renal neoplasms revealed on triphasic helical CT of the kidneys.
B. Herts (2002)
10.1158/1078-0432.CCR-07-5252
Intratumoral Metabolic Heterogeneity of Cervical Cancer
E. Kidd (2008)
10.1016/J.ACRA.2004.11.010
Computerized scheme for assessing ultrasonographic features of breast masses.
K. Kim (2005)
Endorectal ultrasound and computerized B-scan texture analysis to assess sessile adenoma and small rectal carcinoma
C Kuntz (1994)
10.2307/4615733
A Simple Sequentially Rejective Multiple Test Procedure
S. Holm (1979)
Heterogeneity as Biomarker in Tumour Imaging: Systematic Review PLOS ONE | www.plosone
10.1002/jmri.22268
Texture‐based classification of focal liver lesions on MRI at 3.0 Tesla: A feasibility study in cysts and hemangiomas
M. Mayerhoefer (2010)
10.2967/jnumed.110.078261
Monitoring Response to Antiangiogenic Therapy in Non–Small Cell Lung Cancer Using Imaging Markers Derived from PET and Dynamic Contrast-Enhanced MRI
A. D. de Langen (2011)
10.1016/j.ejrad.2008.05.007
Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy.
M. Pickles (2009)
10.1155/2012/634907
Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm
Yachun Pang (2012)
10.1002/JMRI.1880070613
Multifeature analysis of Gd‐enhanced MR images of breast lesions
S. Sinha (1997)
10.1016/S0301-5629(00)00302-1
Computerized ultrasound B-scan characterization of breast nodules.
F. Lefebvre (2000)
10.1016/j.neurad.2011.11.002
Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases.
N. Mouthuy (2012)
10.1016/j.compbiomed.2008.01.016
Texture analysis on MRI images of non-Hodgkin lymphoma
Lara Harrison (2008)
10.1016/j.compmedimag.2012.07.004
Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images
W. Wu (2012)
Using Multivariate Statistics
D. Adler (2016)
10.1016/j.cmpb.2011.10.001
ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform
U. Acharya (2012)
10.1016/S0301-5629(96)00144-5
Application of artificial neural networks for the classification of liver lesions by image texture parameters.
H. Sujana (1996)
10.1016/S0301-5629(01)00468-9
Tissue classification with generalized spectrum parameters.
K. D. Donohue (2001)
10.1016/S0301-5629(02)00541-0
Retrieval technique for the diagnosis of solid breast tumors on sonogram.
Wen-Jia Kuo (2002)
10.1097/00004424-199601000-00002
Pattern recognition system for focal liver lesions using "crisp" and "fuzzy" classifiers.
H. M. Klein (1996)
10.1016/j.acra.2011.01.005
Optimization of time-to-peak analysis for differentiating malignant and benign breast lesions with dynamic contrast-enhanced MRI.
F. Liu (2011)
10.1002/jmri.20794
Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system
L. A. Meinel (2007)
10.1378/chest.11-1016
Optical differentiation between malignant and benign lymphadenopathy by grey scale texture analysis of endobronchial ultrasound convex probe images.
P. Nguyen (2012)
10.1007/s00259-013-2486-8
Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma
M. Hatt (2013)



This paper is referenced by
10.1007/s00330-017-5236-7
Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?
R. De Robertis (2017)
10.1371/journal.pone.0182344
Increased heterogeneity of brain perfusion is an early marker of central nervous system involvement in antiphospholipid antibody carriers
Ting-Syuan Lin (2017)
10.3390/jpm10010015
From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health
E. Capobianco (2020)
10.1038/srep43356
Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell–Renal-Cell-Carcinoma: Proof-of-Concept Study
Qingbo Yin (2017)
Detecting the Evolution Phases of Hepatocellular Carcinoma from Ultrasound Images , Using Generalized Co-Occurrence Matrices
D. Mitrea (2015)
10.18632/oncotarget.11693
Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma
E. Choi (2016)
10.1016/j.amjsurg.2019.07.040
Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy.
A. Hirata (2019)
10.23919/FRUCT.2019.8711918
Texture Analysis of Non-Small Cell Lung Cancer on Unenhanced CT and Blood Flow Maps: a Potential Prognostic Tool
Serena Baiocco (2019)
10.1186/s12931-018-0887-8
A biomarker basing on radiomics for the prediction of overall survival in non–small cell lung cancer patients
B. He (2018)
10.1007/s11547-017-0838-3
Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer
D. Cusumano (2017)
10.1007/s12149-017-1203-2
Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer
A. García-Vicente (2017)
10.1371/journal.pone.0217536
Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics
J. E. van Timmeren (2019)
10.1016/j.acra.2016.04.003
Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions.
X. Ma (2016)
10.1016/j.compbiomed.2016.09.011
Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images
D. Molina-García (2016)
Textural Analysis to Assess Heterogeneity in Breast Cancer
A. Moscoso (2016)
10.1186/s42047-018-0021-8
KIT exon 11 and PDGFRA exon 18 gene mutations in gastric GIST: proposal of a short panel for predicting therapeutic response
D. Barcelos (2018)
10.1016/j.acra.2020.03.004
Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis.
Yuri S Velichko (2020)
10.1016/j.radonc.2018.09.009
Quantitative radiomics: Validating image textural features for oncological PET in lung cancer.
F. Yang (2018)
10.1016/J.REMN.2019.02.004
Medidas de heterogeneidad global y esfericidad con 18F-FDG PET/TC en el cáncer de mama: relación con la biología tumoral, valor predictivo y pronóstico
M. Galán (2019)
10.1186/s41747-020-0145-y
Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach
G. Di Leo (2020)
10.3389/fonc.2015.00272
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar (2015)
10.1007/s00259-017-3733-1
Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET
G. Cook (2017)
10.1016/j.addr.2016.01.006
Decision support systems for personalized and participative radiation oncology☆
P. Lambin (2017)
10.5772/64641
Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications
A. Larroza (2016)
10.1109/WACV.2018.00171
Saliency Prediction for Mobile User Interfaces
Prakhar Gupta (2018)
10.1109/GMEPE-PAHCE.2016.7504637
Algorithm programming for 3D fractal dimension evaluation
A. Bartrés (2016)
10.2967/jnumed.117.199935
A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET
F. Orlhac (2018)
Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer
Sarah A. Mattonen (2016)
10.1016/J.CRITREVONC.2019.03.015
Radiomics: Principles and radiotherapy applications.
I. Gardin (2019)
10.1016/j.neo.2018.10.008
Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer
A. Syed (2019)
10.2967/jnumed.115.163469
Tumor Texture Analysis in PET: Where Do We Stand?
I. Buvat (2015)
10.1155/2017/6053879
Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy
G. Lin (2017)
See more
Semantic Scholar Logo Some data provided by SemanticScholar