Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation

Chen, Shao Jer; Lin, Chien‐Hung; Chang, Chuan Yu; Chang, Ku Yaw; Ho, Han‐Chen; Hsiao, Shih Hsuan; Lin, Cheng-Wei; Tzeng, Jeh En; Chen, Yen Ting; Tsai, Hann-Huei

doi:10.1016/j.clinimag.2011.10.018

Cited by 12 publications

(17 citation statements)

References 10 publications

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“…However, the superiority of this technique over visual CT image analysis in terms of specificity is debated. In addition, several studies evaluated the efficacies of quantitative analyses of CT and ultrasonography (US) images in the differentiation of metastatic and benign lymph node diseases [ 5 , 9 , 10 ]. In the present study, we quantitatively analyzed the CT images of mediastinal and hilar lymph nodes in patients with tuberculosis and sarcoidosis using image analysis software.…”

Section: Discussionmentioning

confidence: 99%

Quantitative image analysis using chest computed tomography in the evaluation of lymph node involvement in pulmonary sarcoidosis and tuberculosis

et al. 2018

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PurposeTo evaluate the feasibility of quantitative analysis of chest computed tomography (CT) scans for the assessment of lymph node (LN) involvement in patients with pulmonary tuberculosis and sarcoidosis.MethodsIn 47 patients with tuberculosis (n = 26) or sarcoidosis (n = 21), 115 lymph nodes (tuberculous, 55; sarcoid, 60) were visually analyzed on chest CT scans according to their size, location, attenuation and shape. Each node was manually segmented using image analysis tool, which was quantitatively analyzed using the following variables: Feret’s diameter, perimeter, area, circularity, mean grey value (Mean), standard deviation (SD) of grey value, minimum grey value (Min), maximum grey value (Max), median grey value (Median), skewness, kurtosis, and net enhancement. We statistically analyzed the visual and quantitative CT features of tuberculous and sarcoid LNs.ResultsIn visual CT analysis, the mean node size in sarcoidosis was significantly greater than that in tuberculosis. There were no statistical differences between tuberculous and sarcoid LNs in terms of location and shape. Central low attenuation and peripheral rim enhancement were more frequently observed in tuberculous LNs than in the sarcoid ones. In quantitative CT analysis, there were significant differences in the values of the Feret’s diameter, perimeter, area, circularity, mean grey value, SD, median, skewness, and kurtosis between tuberculous and sarcoid LNs.ConclusionsQuantitative CT analysis using CT parameters with pixel-by-pixel measurements can help to differentiate of tuberculous and sarcoid LNs.

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Section: Discussionmentioning

confidence: 99%

Quantitative image analysis using chest computed tomography in the evaluation of lymph node involvement in pulmonary sarcoidosis and tuberculosis

et al. 2018

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“…This means that about 99% of patients with uncertain malignant or benign LNs can avoid unnecessary FNA if the texture analysis method introduced here is used to diagnose these patients. In comparison with the other texture analysis studies, we can achieve a higher accuracy in classification of tumour-free and metastatic cervical LNs [ 13 - 15 ]. All of them focused predominantly on co-occurrence matrix features.…”

Section: Discussionmentioning

confidence: 90%

“…In texture analysis methods, Chen et al . [ 13 ] used a combination of texture features based on the co-occurrence matrix and neighbouring grey-level dependence matrix (NGLDM) to classify benign and malignant LNs on ultrasound images. They achieved a 100% classification accuracy using a support vector machine classifier.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, some computer-aided methods for characterising LN in ultrasound imaging have been proposed. Many quantitative features such as morphology [ 10 - 12 ], texture [ 13 - 15 ], contrast-enhanced based [ 16 , 17 ], and elastography [ 12 , 17 - 20 ] have been shown to be useful in differentiating between benign and malignant LNs. The present study provides additional information to support these claims.…”

Section: Introductionmentioning

confidence: 99%

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Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis

Ardakani¹,

Rasekhi²,

Mohammadi³

et al. 2018

Pol J Radiol

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PurposePapillary thyroid carcinoma (PTC) is the most common thyroid cancer, and cervical lymph nodes (LNs) are the most common extrathyroid metastatic involvement. Early detection and reliable diagnosis of LNs can lead to improved cure rates and management costs. This study explored the potential of texture analysis for texture-based classification of tumour-free and metastatic cervical LNs of PTC in ultrasound imaging.Material and methodsA total of 274 LNs (137 tumour-free and 137 metastatic) were explored using the texture analysis (TA) method. Up to 300 features were extracted for texture analysis in three normalisations (default, 3sigma, and 1-99%). Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features were classified by the first nearest neighbour classifier.ResultsNormalisation reflected improvement on the performance of the classifier; hence, the features under 3sigma normalisation schemes through FFPA (fusion Fisher plus the probability of classification error [POE] + average correlation coefficients [ACC]) features indicated high performance in classifying tumour-free and metastatic LNs with a sensitivity of 99.27%, specificity of 98.54%, accuracy of 98.90%, positive predictive value of 98.55%, and negative predictive value of 99.26%. The area under the receiver operating characteristic curve was 0.996.ConclusionsTA was determined to be a reliable method with the potential for characterisation. This method can be applied by physicians to differentiate between tumour-free and metastatic LNs in patients with PTC in conventional ultrasound imaging.

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“…For each node, gray-scale and power Doppler sonograms were obtained using a Sequoia 512 system (Siemens Medical Solutions, Issaquah, Washington) with a high-frequency transducer (8)(9)(10)(11)(12)(13)(14). All images were obtained in a clinical setting without being specially processed.…”

Section: Databasementioning

confidence: 99%

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes

Zhang

Wang

et al. 2015

Ultrason Imaging

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We initiated an observer study to evaluate a computerized system developed in our previous study for automatic extraction of 10 features and estimation of the malignancy probability of cervical nodes in sonograms. In the present study, five expert radiologists and five resident radiologists interpreted the sonograms of 178 nodes. The malignancy rating and patient management recommendation (biopsy or follow-up) were made without and then with the computer aid. Under these two reading conditions, the performances of radiologists and agreement among a group of radiologists were evaluated by using the receiver operating characteristic (ROC) analysis and the κ statistic, respectively. With the computer aid, the performances of radiologists improved significantly, as indicated by the increase in the area under the ROC curve (Az) from 0.843 to 0.896 (p = 0.031) and from 0.705 to 0.822 (p < 0.001), for the expert and resident groups, respectively. Agreement among all 10 radiologists improved from slight to moderate as indicated by an increase in the κ value from 0.195 to 0.421 (p < 0.001). The average performance of residents with aid (Az = 0.822) was close to that of experts without aid (Az = 0.843). Results indicate that computer-aided diagnosis is useful to improve radiologist performance (especially that of inexperienced radiologists) in the ultrasonographic evaluation of cervical nodes and to reduce variability among radiologists.

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Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation

Cited by 12 publications

References 10 publications

Quantitative image analysis using chest computed tomography in the evaluation of lymph node involvement in pulmonary sarcoidosis and tuberculosis

Quantitative image analysis using chest computed tomography in the evaluation of lymph node involvement in pulmonary sarcoidosis and tuberculosis

Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes

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