A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules: An Asia Cohort Study

Zheng, Bin; Zhou, Xiwen; Chen, Jianhua; Zheng, Wei; Duan, Qing; Chen, Chun

doi:10.1016/j.athoracsur.2015.03.071

Cited by 26 publications

(66 citation statements)

References 14 publications

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“…Computed tomography (CT) as a widely available noninvasive modality for detecting lung cancer in clinical practice, has an important role in the differential diagnosis of SPN 8 9 10 11 . However, conventional visual assessment of SPN on CT, such as tumor size, density and margins, has an lower diagnostic accuracy of approximately 60% in differentiating benign from malignant SPN 12 .…”

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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

Sci Rep

207

122

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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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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

Sci Rep

207

122

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“…Unconditional logistic regression and Fisher's linear discriminant analysis are very important tasks in machine learning, which can be used to automatically derive the generalized description of a given dataset from known historical data, in order to predict future events [21][22].The results of the two models are relatively satisfactory, and consequently afford greater con dence in the assessment of COVID-19. In addition, the above CT features indicate that the lesion is in the critical period, and this trend change is helpful for clinicians to judge the therapeutic effect and predict the outcome of the disease.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 Disease Progression with Chest CT Images

Liang¹,

Qiu

Zhu

et al. 2020

Preprint

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Background: Some mild patients can deteriorate to moderate or severe within a week with the natural progression of COVID-19.it has been crucial to early identify those mild cases and give timely treatment . The chest computed tomography (CT) has shown to be useful to assist clinical diagnosis of COVID-19.In this study, machine learning was used to develop an early-warning CT feature model for predicting mild patients with potential malignant progression.Methods：The total of 140 COVID-19 mild patients were collected. All patients at admission were divided into groups (alleviation group and exacerbation group) with or without malignant progression.The clinical and laboratory data at admission, the first CT, and the follow-up CT at critical stage of the two groups were compared with Chi-square test,.The CT features data (distribution, morphology,etc) were used to establish the prediction model by Fisher's linear discriminant method and Unconditional logistic regression algorithm. And the model was validated with 40 exception data.and the Area Under ROC curve (AUC) was used to evaluate the models.Results：The model filtered out three variables of CT features including distal air bronchogram, fibrosis,and reversed halo sign. Notably, the distal air bronchograms was less common in alleviation group, while the fibrosis and reversed halo sign were more common.The sensitivity, specificity and Youden index of unconditional logistic regression were 86.1%, 92.6% and 78.7%, For the analysis of Fisher's linear discriminant, the sensitivity, specificity and Youden index were 83.3%, 94.1% and 77.4%. The generalization ability of both models were consistent with sensitivity of 95.89%, specificity of 100%, and Youden index of 83.33%.Conclusions: The CT imaging features-based machine learning model has a high sensitivity for finding out the mild patients who are easy to deteriorate into severe/critical cases efficiently so that timely treatments came true for those patients,while largely help to relieve the medical pressure.

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“…Other changes include the transmigration stenosis of the lumen and irregular stenosis. Malignant SPNs can cause changes to bronchi including sudden truncation, normality, translation and cone stenosis (21). Calcification is a powerful index in CT for the identification of benign SPNs.…”

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

Shi

Wang

2016

Experimental and Therapeutic Medicine

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The aim of the present study was to analyze the value of applying dual-source 64-layer spiral computed tomography (CT) in the differential diagnosis of solitary pulmonary nodules (SPNs). Mediastinal windows from 45 cases were selected to study SPNs (maximum diameter, ≤3 cm), and the pathological nature of lesions was determined by clinical and pathological diagnosis. Conventional 64-layer spiral CT scanning, local enhancement and 3D recombination technologies were used to determine the occurrence rate, lesion diameter, degree of enhancement, lobular sign, spicule sign, pleural indentation sign, vessel convergence sign and bronchus sign. The final diagnoses indicated 34 cases of malignant SPNs (75.6%) and 11 benign cases (24.4%). When the nodule diameter in the malignant group was compared with that of the benign group, the difference was not statistically significant (P>0.05). Nodules in the malignant group showed inhomogeneous enhancement while nodules in the benign group showed homogeneous enhancement. The enhanced CT values in the malignant group were higher than those in the benign group, and the difference was statistically significant (P<0.05). The proportion of nodules with lobular sign in the malignant group was significantly higher than that in the benign group (P<0.05). The proportion of nodules with calcification, vessel convergence sign and bronchus sign in the malignant group were significantly higher than those in the benign group, and the differences were statistically significant (P<0.05). A comparison of vacuole sign, pleural indentation sign, spiculate protuberance and fat occurrence between the two groups yielded no statistically significant differences (P>0.05). The sensitivity of CT enhancement was 85.6%, specificity was 79.6%, positive predicated value was 92.3%, and the negative predicted value was 85.2%. In conclusion, SPNs diagnosed by CT enhancement manifested with enhancement degree, lobular sign, calcification, vessel convergence sign and bronchus sign with high diagnostic accuracy.

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A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules: An Asia Cohort Study

Cited by 26 publications

References 14 publications

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

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

Predicting COVID-19 Disease Progression with Chest CT Images

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

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A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules: An Asia Cohort Study

Cited by 26 publications

References 14 publications

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

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

Predicting&nbsp;COVID-19 Disease Progression with Chest CT Images

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

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Product

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Predicting COVID-19 Disease Progression with Chest CT Images