Prediction Models for Solitary Pulmonary Nodules Based on Curvelet Textural Features and Clinical Parameters

Wang, Jingjing; Wu, Huijun; Sun, Tao; Li, Xia; Wang, Wei; Tao, Lixin; Huo, Da; Lv, Pingxin; He, Wen; Guo, Xiuhua

doi:10.7314/apjcp.2013.14.10.6019

Cited by 11 publications

(7 citation statements)

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“…The reconstruction interval and reconstruction thickness for routine scanning were 0.625 mm. The kernel was a B31f/B70 type and the data were reconstructed using a 512×512 matrix [15].…”

Section: Methodsmentioning

confidence: 99%

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

et al. 2014

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ObjectiveTo determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer.Materials and MethodsA total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.ResultsUsing a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.ConclusionOur results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.

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“…The reconstruction interval and reconstruction thickness for routine scanning were 0.625 mm. The kernel was a B31f/B70 type and the data were reconstructed using a 512×512 matrix [15].…”

Section: Methodsmentioning

confidence: 99%

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

et al. 2014

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“…Being the most common varied factors in clinical settings on the imaging modality, whether the imaging acquisition parameters of contrast-enhancement, reconstruction slice thickness and convolution kernel could affect the diagnostic performance of radiomics features on the differential diagnosis of SPN is an interesting field that has been explorated 13 20 . Although individual CT texture feature is useful in the characterization of SPN 17 18 21 , integrating multiple features into a predictive panel as a radiomics signature may be a robust approach for quantifying tumor phenotype 22 23 . Thus, regarding the influence of scanning parameters on the individual feature performance in the previous studies 13 19 24 , radiomics signature could consequently make impact on the performance of differential diagnosis of SPN.…”

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confidence: 99%

Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule

Huang

et al. 2016

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The Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule (SPN) remains unclear. 240 patients with SPNs (malignant, n = 180; benign, n = 60) underwent non-contrast CT (NECT) and contrast-enhanced CT (CECT) which were reconstructed with different slice thickness and convolution kernel. 150 radiomics features were extracted separately from each set of CT and diagnostic performance of each feature were assessed. After feature selection and radiomics signature construction, diagnostic performance of radiomics signature for discriminating benign and malignant SPN was also assessed with respect to the discrimination and classification and compared with net reclassification improvement (NRI). Our results showed NECT-based radiomics signature demonstrated better discrimination and classification capability than CECT in both primary (AUC: 0.862 vs. 0.829, p = 0.032; NRI = 0.578) and validation cohort (AUC: 0.750 vs. 0.735, p = 0.014; NRI = 0.023). Thin-slice (1.25 mm) CT-based radiomics signature had better diagnostic performance than thick-slice CT (5 mm) in both primary (AUC: 0.862 vs. 0.785, p = 0.015; NRI = 0.867) and validation cohort (AUC: 0.750 vs. 0.725, p = 0.025; NRI = 0.467). Standard convolution kernel-based radiomics signature had better diagnostic performance than lung convolution kernel-based CT in both primary (AUC: 0.785 vs. 0.770, p = 0.015; NRI = 0.156) and validation cohort (AUC: 0.725 vs.0.686, p = 0.039; NRI = 0.467). Therefore, this study indicates that the contrast-enhancement, reconstruction slice thickness and convolution kernel can affect the diagnostic performance of radiomics signature in SPN, of which non-contrast, thin-slice and standard convolution kernel-based CT is more informative.

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“…The CT pixels or voxels that comprise the image are the result of X-ray beam attenuation as it passes through a small portion of living tissue [2, 10]. The behaviours of the various attenuations are used to construct the histogram, and its study is only another step in the process of differentiating SINRSBs from malignant nodules, without ignoring the initial appearance (spicules, lobules, MEN diameter, and visceral pleural retraction) that, when combined with the radiologist's experience and the support of clinical data, contributes to the definition of probable malignancy.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, fine-needle aspiration biopsies of suspected benign nodules are relatively low in sensitivity and specificity because this method often cannot reach an aetiological diagnosis of the benign process; furthermore, it generates technical difficulties and complications, especially with regard to smaller nodules located deep in the lung parenchyma [9]. Hence, the need exists to use three-dimensional imaging technology to establish new lung nodule evaluation methods for more in-depth analyses of the textural features of the nodule by analysing the histogram data obtained on CT imaging [10].…”

Section: Introductionmentioning

confidence: 99%

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

et al. 2019

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Background The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures. Objectives The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images. Methods A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules. SINRSB CT scans were performed using the same noncontrast enhanced CT protocol and equipment; the malignant nodule data were acquired from several databases. The kurtosis (KUR) and skewness (SKW) values of these tests were determined for the whole volume of each nodule, and the histograms were classified into two basic patterns: peaks or plateaus. Results The mean (MEN) KUR values of the SINRSBs and malignant nodules were 3.37 ± 3.88 and 5.88 ± 5.11, respectively. The receiver operating characteristic (ROC) curve showed that the sensitivity and specificity for distinguishing SINRSBs from malignant nodules were 65% and 66% for KUR values >6, respectively, with an area under the curve (AUC) of 0.709 (p < 0.0001). The MEN SKW values of the SINRSBs and malignant nodules were 1.73 ± 0.94 and 2.07 ± 1.01, respectively. The ROC curve showed that the sensitivity and specificity for distinguishing malignant nodules from SINRSBs were 65% and 66% for SKW values >3.1, respectively, with an AUC of 0.709 (p < 0.0001). An analysis of the peak and plateau histograms revealed sensitivity, specificity, and accuracy values of 84%, 74%, and 79%, respectively. Conclusions KUR, SKW, and histogram shape can help to noninvasively diagnose SINRSBs but should not be used alone or without considering clinical data.

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Prediction Models for Solitary Pulmonary Nodules Based on Curvelet Textural Features and Clinical Parameters

Cited by 11 publications

References 31 publications

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

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