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Quantitative CT Texture And Shape Analysis: Can It Differentiate Benign And Malignant Mediastinal Lymph Nodes In Patients With Primary Lung Cancer?

H. Bayanati, R. Thornhill, C. Souza, Vineeta Sethi-Virmani, Ashish Gupta, D. Maziak, K. Amjadi, C. Dennie
Published 2014 · Medicine

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AbstractObjectiveTo assess the accuracy of CT texture and shape analysis in the differentiation of benign and malignant mediastinal nodes in lung cancer.MethodsForty-three patients with biopsy-proven primary lung malignancy with pathological mediastinal nodal staging and unenhanced CT of the thorax were studied retrospectively. Grey-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from 72 nodes. Differences between benign and malignant features were assessed using Mann-Whitney U tests. Receiver operating characteristic (ROC) curves for each were constructed and the area under the curve (AUC) calculated with histopathology diagnosis as outcome. Combinations of features were also entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.ResultsUsing optimum-threshold criteria, the combined textural and shape features identified malignant mediastinal nodes with 81 % sensitivity and 80 % specificity (AUC = 0.87, P < 0.0001). Using this combination, 84 % malignant and 71 % benign nodes were correctly classified.ConclusionsQuantitative CT texture and shape analysis has the potential to accurately differentiate malignant and benign mediastinal nodes in lung cancer.Key Points• Mediastinal nodal staging is crucial in the management of lung cancer • Mediastinal nodal metastasis affects prognosis and suitability for surgical treatment • Computed tomography (CT) is limited for mediastinal nodal staging • Texture analysis measures tissue heterogeneity not perceptible to human vision • CT texture analysis may accurately differentiate malignant and benign mediastinal nodes
This paper references
Texture analysis using gray level run lengths
M. Galloway (1974)
Data mining: practical machine learning tools and techniques, 3rd Edition
I. Witten (1999)
Data Mining Practical Machine Learning Tools and Techniques
อนิรุธ สืบสิงห์ (2014)
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)
Non-small cell lung cancer: histopathologic correlates for texture parameters at CT.
B. Ganeshan (2013)
Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison.
G. Brown (2003)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
S. Raudys (1991)
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)
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
LIBSVM: A library for support vector machines
Chih-Chung Chang (2011)
Bronchogenic carcinoma: analysis of staging in the mediastinum with CT by correlative lymph node mapping and sampling.
T. Mcloud (1992)
Comparative efficacy of positron emission tomography with fluorodeoxyglucose in evaluation of small (<1 cm), intermediate (1 to 3 cm), and large (>3 cm) lymph node lesions.
N. Gupta (2000)
MRI texture analysis on texture test objects, normal brain and intracranial tumors.
S. Herlidou-Même (2003)
Textural Features for Image Classification
R. Haralick (1973)
Comparative efficacy of positron emission tomography with fluorodeoxyglucose in evaluation of small ( 3 cm) lymph node lesions.
N. Gupta (2000)
Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation.
S. Chen (2012)
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
J. Hanley (1982)
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Fergus Davnall (2012)
GohV (2013)Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5year survival
F Ng (2013)
CT and MR imaging in staging non-small cell bronchogenic carcinoma: report of the Radiologic Diagnostic Oncology Group.
W. Webb (1991)
Diameter-independent Computer-Aided Classification of Mesorectal Lymph Nodes in Rectal cancer : Preliminary Results
W. Shabana (2011)
CT and MR imaging in staging non-small cell bronchogenic carcinoma: report of the Radiologic Diagnostic Oncology
WR Webb (1991)
The tumor microenvironment and metastatic disease
S. J. Lunt (2008)
Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival.
K. Miles (2009)
Influence of MRI acquisition protocols and image intensity normalization methods on texture classification.
G. Collewet (2004)
Optical differentiation between malignant and benign lymphadenopathy by grey scale texture analysis of endobronchial ultrasound convex probe images.
P. Nguyen (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)
Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.
D. Chen (1999)
Texture analysis for tissue discrimination on T1‐weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers
M. Mayerhoefer (2005)
Journey toward computer-aided diagnosis: role of image texture analysis.
G. Tourassi (1999)
Thoracic nodal staging with PET imaging with 18FDG in patients with bronchogenic carcinoma.
E. Patz (1995)
The utility of sonographic features during endobronchial ultrasound-guided transbronchial needle aspiration for lymph node staging in patients with lung cancer: a standard endobronchial ultrasound image classification system.
T. Fujiwara (2010)
recommendations for practioners
Sarunas (2015)
Texture Analysis: A Review of Neurologic MR Imaging Applications
A. Kassner (2010)

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José (2020)
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X. Fave (2015)
Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.
Usman Bashir (2016)
Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis.
Y. Ueno (2017)
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A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma.
Yawei Gu (2018)
Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes
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Preliminary investigation into sources of uncertainty in quantitative imaging features
X. Fave (2015)
Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values.
Yu-xi Ge (2020)
Quantitative computed tomography texture analysis: can it improve diagnostic accuracy to differentiate malignant lymph nodes?
S. Y. Shin (2019)
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M-W You (2019)
CT texture analysis in histological classification of epithelial ovarian carcinoma
H. An (2021)
Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis
Z. Wang (2019)
2-[18F]FDG PET/CT radiomics in lung cancer: an overview of the technical aspect and its emerging role in management of the disease.
Reyhaneh Manafi-Farid (2020)
Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.
Yao Shen (2020)
Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI
X. Xu (2017)
Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging.
S. Liu (2018)
CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer
M. B. Andersen (2016)
CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation.
F. Xu (2018)
Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma
Xuejin Ou (2019)
Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features
N. Dinapoli (2015)
Distinguishing metastases from benign adrenal masses: what can CT texture analysis do?
B. Shi (2019)
Liver tissue classification of en face images by fractal dimension-based support vector machine.
Y. Zhu (2020)
Mediastinal lymph node malignancy detection in computed tomography images using fully convolutional network
Hitesh Tekchandani (2020)
Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study
L. Filograna (2018)
Quantitative image analysis using chest computed tomography in the evaluation of lymph node involvement in pulmonary sarcoidosis and tuberculosis
C. U. Lee (2018)
Complementary features for radiomic analysis of malignant and benign mediastinal lymph nodes
T. Pham (2017)
Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma
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