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 PDFAnalyze on Scholarcy
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
Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review
Xiangyu Yang (2011)
Nasopharyngeal carcinoma: investigation of intratumoral heterogeneity with FDG PET/CT.
Bingsheng Huang (2012)
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)
Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns
D. Iakovidis (2010)
Texture analysis of CT images in the characterization of oral cancers involving buccal mucosa.
J. Raja (2012)
[Image analysis in the differential diagnosis of renal parenchyma lesions].
J. Tůma (2011)
Monitoring the Longitudinal Intra-tumor Physiological Impulse Response to VEGFR2 Blockade in Breast Tumors Using DCE-CT
K. Stantz (2010)
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)
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)
Follow-up of Wilms' tumour during pre-operative chemotherapy by qualitative and quantitative sonography.
M. Engelbrecht (1998)
Lymphomas: MR imaging contrast characteristics with clinical-pathologic correlations.
W. Negendank (1990)
A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images
D. Glotsos (2010)
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)
Multiple Resolution Texture Analysis and Classification
Shmuel Peleg (1984)
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)
Dynamic contrast-enhanced magnetic resonance imaging of radiation therapy-induced microcirculation changes in rectal cancer.
Q. D. de Lussanet (2005)
Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound.
S. Tsantis (2005)
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer
F. Tixier (2011)
Echographic differentation of intraocular melanomas by computer analysis
J. M. Thijssen (1992)
An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging
S. Drabycz (2010)
A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data.
F. O’Sullivan (2003)
[The heterogeneity of blood flow on magnetic resonance imaging: a biomarker for grading cerebral astrocytomas].
A. R. Revert Ventura (2014)
Tumor hypoxia adversely affects the prognosis of carcinoma of the head and neck.
D. Brizel (1997)
MRI Texture Analysis in Paediatric Oncology: A Preliminary Study
Ahmed E. Fetit (2013)
Fractal Analysis of Small Peripheral Pulmonary Nodules in Thin-section CT: Evaluation of the Lung-nodule Interfaces
S. Kido (2002)
Pleural nodule identification in low-dose and thin-slice lung computed tomography
A. Retico (2009)
Automatic texture-based analysis in ultrasound imaging of ovarian masses.
F. Faschingbauer (2013)
Image Analysis of Benign and Malignant Neck Masses
R. Plant (1998)
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)
Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography
Haifeng Wu (2012)
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)
LEARNING PATTERNS OF LIVER MASSES USING IMPROVED RBF NETWORKS
Chien-Cheng Lee (2010)
The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography.
M. McNitt-Gray (1999)
Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images
D. Mitrea (2012)
Computed tomographic characteristics of intrapelvic masses in dogs.
Daniel I Spector (2011)
Quantifying heterogeneity in human tumours using MRI and PET.
M. Asselin (2012)
Textural analysis of contrast‐enhanced MR images of the breast
P. Gibbs (2003)
Quantitatively Characterizing the Textural Features of Sonographic Images for Breast Cancer With Histopathologic Correlation
S. Chen (2005)
Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images.
H. Yoshida (2003)
Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images
W. Chen (2007)
Computer-aided diagnosis scheme for detection of polyps at CT colonography.
H. Yoshida (2002)
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)
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)
Spatial Heterogeneity in Sarcoma 18F-FDG Uptake as a Predictor of Patient Outcome
J. Eary (2008)
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)
Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment
T. Larkin (2014)
MR classification of brain gliomas: value of magnetization transfer and conventional imaging.
T. Kurki (1995)
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)
Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography.
J. Willaime (2013)
A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound
Jitendra Virmani (2013)
A wavelet-based optimal texture feature set for classification of brain tumours
M. Sasikala (2008)
Quantitative assessment Ki-67 score for prediction of response to neoadjuvant chemotherapy in breast cancer
J. R. Brown (2014)
Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images
Megan Rakoczy (2012)
Microcirculatory fraction (MCF(I)) as a potential imaging marker for tumor heterogeneity in breast cancer.
Xiangyu Yang (2012)
Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features
P. Georgiadis (2008)
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)
Computer-aided Diagnosis of the Solitary Pulmonary Nodule1
Sumit K. Shah (2005)
Inhibition of tyrosine kinase receptors by SU6668 promotes abnormal stromal development at the periphery of carcinomas
P. Farace (2009)
Ultrasonographic texture characterization of salivary and neck masses using two-dimensional gray-scale clustering.
K. Yoshiura (1997)
Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?
G. Cook (2013)
Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response
N. Szerlip (2012)
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)
Application of an artificial neural network to the computer-aided differentiation of focal liver disease in MR imaging
X. Zhang (2009)
Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images.
Y. Liao (2011)
Pulmonary nodule detection using chest CT images.
D-Y. Kim (2003)
Apparent diffusion coefficient histograms may predict low‐grade glioma subtype
D. Tozer (2007)
Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images.
W. K. Moon (2011)
Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging
Chunyan Cui (2011)
Texture Analysis in Quantitative MR Imaging
L. Kjær (1995)
Computer aided diagnosis of parotid gland lesions using ultrasonic multi-feature tissue characterization.
S. Siebers (2010)
Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis.
B. Garra (1993)
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)
Texture‐based and diffusion‐weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla
J. Fruehwald-Pallamar (2013)
Computerized diagnosis of breast lesions on ultrasound.
K. Horsch (2002)
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)
Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps
C. J. Rose (2009)
Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images.
Y. Huang (2006)
Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy.
Shan Tan (2013)
Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques.
C. McLaren (2009)
A Statistical Modeling Approach to the Analysis of Spatial Patterns of FDG-PET Uptake in Human Sarcoma
F. O'Sullivan (2011)
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)
Textural Features for Image Classification
R. Haralick (1973)
Distinguishing between Germinomas and Pineal Cell Tumors on MR Imaging
N. Dumrongpisutikul (2012)
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)
Computerized analysis of lesions in US images of the breast.
M. Giger (1999)
Relevance of sonographic B-mode criteria and computer-aided ultrasonic tissue characterization in differential/diagnosis of solid breast masses.
S. Huber (2000)
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival.
B. Ganeshan (2012)
DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6
J. O'Connor (2011)
Can Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined with Texture Analysis Differentiate Malignant Glioneuronal Tumors from Other Glioblastoma?
P. Eliat (2012)
SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors
Jitendra Virmani (2012)
Textural features corresponding to textural properties
Moses Amadasun (1989)
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)
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Fergus Davnall (2012)
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)
Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation.
S. Chen (2012)
Support vector machines for diagnosis of breast tumors on US images.
R. Chang (2003)
A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier
M. Gletsos (2003)
Comparative Analysis of Texture Characteristics of Malignant and Benign Tumors in Breast Ultrasonograms
K. Kim (2010)
Computer-aided diagnosis with textural features for breast lesions in sonograms
D. Chen (2011)
Fractal Geometry of Nature
B. Mandelbrot (1977)
Erratum to “Progress in Therapy Development for Amyotrophic Lateral Sclerosis”
Kalina Venkova-Hristova (2012)
Pixel analysis of MR perfusion imaging in predicting radiation therapy outcome in cervical cancer
N. Mayr (2000)
Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems
Y. Huang (2005)
A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results.
M. McNitt-Gray (1999)
Computerized characterization of breast masses on three-dimensional ultrasound volumes.
B. Sahiner (2004)
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)
Diffusion-weighted imaging of breast masses: comparison of diagnostic performance using various apparent diffusion coefficient parameters.
M. Hirano (2012)
Texture analysis — A new method of differentiating prostatic carcinoma from prostatic hypertrophy
C. Kratzik (2004)
Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management.
S. Chen (2009)
Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.
Mahesh B. Nagarajan (2013)
Analysis of parametric histogram from dynamic contrast‐enhanced MRI: application in evaluating brain tumor response to radiotherapy
S. Peng (2013)
Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.
K. Nie (2008)
Computational methods of feature
H Liu (2008)
Computer-assisted analysis of lymph nodes detected by EUS in patients with esophageal carcinoma.
D. Loren (2002)
Analysis of the spatial characteristics of metabolic abnormalities in newly diagnosed glioma patients
X. Li (2002)
Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound
Walter Gómez (2012)
Diagnostik fokaler Leberläsionen durch Texturanalyse von dynamischen Computertomogrammen
Klein Hm (1993)
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)
A dual neural network ensemble approach for multiclass brain tumor classification.
J. Sachdeva (2012)
Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features.
Ted Way (2009)
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
T. Ojala (2002)
Sonohistology for the computerized differentiation of parotid gland tumors.
U. Scheipers (2005)
Computer-Aided Diagnosis for Early-Stage Lung Cancer Based on Longitudinal and Balanced Data
T. Sun (2013)
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)
Computer-aided diagnosis for 3-d power Doppler breast ultrasound.
Yi-Chen Lai (2013)
Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers
S. Mougiakakou (2007)
Analysis and classification of tissue with scatterer structure templates
K. Donohue (1999)
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)
Quantification of Inhomogeneities in Malignancy Grading of Non-Hodgkin Lymphoma with Mr Imaging
S. Rehn (1993)
Tumour inhomogeneities on magnetic resonance imaging, a new factor with prognostic information in non-Hodgkin's lymphomas.
S. Rehn (1997)
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)
Peripheral enhancement and spatial contrast uptake heterogeneity of primary breast tumours: quantitative assessment with dynamic MRI.
S. Mussurakis (1998)
Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis.
A. Karahaliou (2010)
Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade
K. Skogen (2012)
Heterogeneity analysis of Gd-DTPA uptake: improvement in breast lesion differentiation.
B. Issa (1999)
Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer.
M. Vaidya (2012)
Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence.
V. Goldberg (1992)
Neural network based focal liver lesion diagnosis using ultrasound images
Deepti Mittal (2011)
Sonographic texture characterization of salivary gland tumors by fractal analyses.
T. Chikui (2005)
Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker.
V. Goh (2011)
Assessing changes in tumour vascular function using dynamic contrast‐enhanced magnetic resonance imaging
C. Hayes (2002)
Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival.
K. Miles (2009)
Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis.
K. Yan (2012)
Cancer heterogeneity: implications for targeted therapeutics
R. Fisher (2013)
Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.
D. Chen (2002)
Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images.
Rebekah H. Gensure (2012)
Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features
Yanni Su (2011)
USING A FUZZY ENGINE AND COMPLETE SET OF FEATURES FOR HEPATIC DISEASES DIAGNOSIS: INTEGRATING CONTRAST AND NON-CONTRAST CT IMAGES
E-Liang Chen (2001)
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)
Characterizing at-Risk Voxels by Using Perfusion Magnetic Resonance Imaging for Cervical Cancer during Radiotherapy.
Zhibin Huang (2012)
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)
Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image
Shichong Zhou (2013)
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)
Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment
P. Fasching (2011)
Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps.
K. Emblem (2008)
Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images
Konstantinos Sidiropoulos (2013)
Texture analysis of lesions in breast ultrasound images.
R. Sivaramakrishna (2002)
Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation.
H. Aerts (2012)
A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer.
D M L Tse (2012)
A comparative study of texture measures with classification based on featured distributions
T. Ojala (1996)
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)
Computational Methods of Feature Selection
H. Liu (2007)
Computer-aided diagnosis of the solitary pulmonary nodule.
Sumit K. Shah (2005)
Computer-aided diagnosis of breast tumors with different US systems.
Wen-Jia Kuo (2002)
Breast cancer diagnosis using self-organizing map for sonography.
D. Chen (2000)
Echographic differentiation of histological types of intraocular melanoma.
J. Thijssen (1991)
Magnetic resonance diffusion imaging of ovarian masses: a first experience with 12 cases
G. Sarty (2003)
Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices
A. Padma (2013)
Characterization of breast cancer types by texture analysis of magnetic resonance images.
K. Holli (2010)
Fuzzy-Based Classification of Breast Lesions Using Ultrasound Echography and Elastography
S. Selvan (2012)
[Classification of solid soft tissue tumours by ultrasonography].
M. Schulte (2010)
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)
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)
Fractal methods and results in cellular morphology — dimensions, lacunarity and multifractals
T. G. Smith (1996)
Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image.
H. Wang (2010)
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)
Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.
D. Chen (1999)
Traditional Chinese Medicine in Cancer Care: A Review of Controlled Clinical Studies Published in Chinese
X. Li (2013)
A new automated method for the segmentation and characterization of breast masses on ultrasound images.
J. Cui (2009)
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)
Digital Image Analysis Is a Useful Adjunct to Endoscopic Ultrasonographic Diagnosis of Subepithelial Lesions of the Gastrointestinal Tract
V. X. Nguyen (2010)
Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.
R. Chang (2003)
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)
Heterogeneity of microbubble accumulation: a novel approach to discriminate between well-differentiated hepatocellular carcinomas and regenerative nodules.
H. Maruyama (2012)
Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
Ted Way (2006)
Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.
D. Chen (2002)
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)
Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms
M. Strzelecki (2006)
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)
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)
Quantitative Measurement for Thyroid Cancer Characterization Based on Elastography
J. Ding (2011)
Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme
E. I. Zacharaki (2009)
3-D ultrasound texture classification using run difference matrix.
Wei-Ming Chen (2005)
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.
D. Moher (2010)
Pattern analysis accounts for heterogeneity observed in MRI studies of tumor angiogenesis
M. Dominietto (2013)
Texture analysis using gray level run lengths
M. Galloway (1974)
Texture analysis of breast tumors on sonograms.
D. Chen (2000)
Enhancement characteristics of papillary renal neoplasms revealed on triphasic helical CT of the kidneys.
B. Herts (2002)
Intratumoral Metabolic Heterogeneity of Cervical Cancer
E. Kidd (2008)
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)
A Simple Sequentially Rejective Multiple Test Procedure
S. Holm (1979)
Heterogeneity as Biomarker in Tumour Imaging: Systematic Review PLOS ONE | www.plosone
Texture‐based classification of focal liver lesions on MRI at 3.0 Tesla: A feasibility study in cysts and hemangiomas
M. Mayerhoefer (2010)
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)
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)
Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm
Yachun Pang (2012)
Multifeature analysis of Gd‐enhanced MR images of breast lesions
S. Sinha (1997)
Computerized ultrasound B-scan characterization of breast nodules.
F. Lefebvre (2000)
Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases.
N. Mouthuy (2012)
Texture analysis on MRI images of non-Hodgkin lymphoma
Lara Harrison (2008)
Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images
W. Wu (2012)
Using Multivariate Statistics
D. Adler (2016)
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)
Application of artificial neural networks for the classification of liver lesions by image texture parameters.
H. Sujana (1996)
Tissue classification with generalized spectrum parameters.
K. D. Donohue (2001)
Retrieval technique for the diagnosis of solid breast tumors on sonogram.
Wen-Jia Kuo (2002)
Pattern recognition system for focal liver lesions using "crisp" and "fuzzy" classifiers.
H. M. Klein (1996)
Optimization of time-to-peak analysis for differentiating malignant and benign breast lesions with dynamic contrast-enhanced MRI.
F. Liu (2011)
Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system
L. A. Meinel (2007)
Optical differentiation between malignant and benign lymphadenopathy by grey scale texture analysis of endobronchial ultrasound convex probe images.
P. Nguyen (2012)
Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma
M. Hatt (2013)
This paper is referenced by
Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?
R. De Robertis (2017)
Increased heterogeneity of brain perfusion is an early marker of central nervous system involvement in antiphospholipid antibody carriers
Ting-Syuan Lin (2017)
From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health
E. Capobianco (2020)
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)
Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma
E. Choi (2016)
Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy.
A. Hirata (2019)
Texture Analysis of Non-Small Cell Lung Cancer on Unenhanced CT and Blood Flow Maps: a Potential Prognostic Tool
Serena Baiocco (2019)
A biomarker basing on radiomics for the prediction of overall survival in non–small cell lung cancer patients
B. He (2018)
Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer
D. Cusumano (2017)
Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer
A. García-Vicente (2017)
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)
Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions.
X. Ma (2016)
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)
KIT exon 11 and PDGFRA exon 18 gene mutations in gastric GIST: proposal of a short panel for predicting therapeutic response
D. Barcelos (2018)
Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis.
Yuri S Velichko (2020)
Quantitative radiomics: Validating image textural features for oncological PET in lung cancer.
F. Yang (2018)
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)
Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach
G. Di Leo (2020)
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar (2015)
Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET
G. Cook (2017)
Decision support systems for personalized and participative radiation oncology☆
P. Lambin (2017)
Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications
A. Larroza (2016)
Saliency Prediction for Mobile User Interfaces
Prakhar Gupta (2018)
Algorithm programming for 3D fractal dimension evaluation
A. Bartrés (2016)
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)
Radiomics: Principles and radiotherapy applications.
I. Gardin (2019)
Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer
A. Syed (2019)
Tumor Texture Analysis in PET: Where Do We Stand?
I. Buvat (2015)
Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy
G. Lin (2017)See more