Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods

Xiang, Yangwei; Sun, Yifeng; Liu, Yuan; Han, Baohui; Chen, Qunhui; Ye, Xiaodan; Zhu, Li; Gao, Wen; Fang, Wentao

doi:10.21037/jtd.2019.01.90

Cited by 18 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Models for 20–30 mm solid SPNs may outperform those used for 8–20 mm counterparts in terms of predicting the probability of malignancy because such a probability increases among larger SPNs 13. Third, our study focused on indeterminate solid SPNs diagnosed on imaging without identifying their types, such as hamartomas and arteriovenous malformations, as had been performed in previous studies 15–17…”

Section: Discussionmentioning

confidence: 97%

“…However, previous models for subsolid SPNs may outperform those for solid SPNs in terms of predicting the probability of malignancy because subsolid SPNs are significantly more likely to be malignant 3. Second, some previous models focused on solid SPNs, including those 20–30 mm or 8–20 mm in size 15–17. Models for 20–30 mm solid SPNs may outperform those used for 8–20 mm counterparts in terms of predicting the probability of malignancy because such a probability increases among larger SPNs 13.…”

Section: Discussionmentioning

confidence: 99%

“…The present study analyzed the following variables: Patient characteristics including age, sex, body mass index, smoking history (yes or on), chronic interstitial or obstructive lung disease (yes or no), family history of lung cancer (yes or no), and history of extra-thoracic malignant neoplasm (>5 years ago, yes or no).Image-based features consisted of emphysema (yes or no) and nodule parameters including diameter, upper lobe location (yes or no), marginal spiculation (yes or no), significant lung nodule enhancement (yes [>15 HU] or no [≤15 HU]), and pleural indentation (yes or no) based on previous studies 5,13,15Tumor biomarkers including carcinoembryonic antigen (CEA, normal [≤5.0 μg/L] or abnormal [>5.0 μg/L]), carbohydrate antigen 125 (normal [≤35.0 kU/L] or abnormal [>35.0 kU/L]), carbohydrate antigen 199 (normal [≤27.0 kU/L] or abnormal [>27.0 kU/L]), carbohydrate antigen 724 (normal [≤6.9 kU/L] or abnormal [>6.9 kU/L]), neuron-specific enolase (normal [≤16.3 μg/L] or abnormal [>16.3 μg/L]), squamous cell carcinoma antigen (normal [≤1.5 μg/L] or abnormal [>1.5 μg/L]), and ferritin (normal [13–150 μg/L] or abnormal [<13 or >150 μg/L]).…”

Section: Methodsmentioning

confidence: 99%

“…Image-based features consisted of emphysema (yes or no) and nodule parameters including diameter, upper lobe location (yes or no), marginal spiculation (yes or no), significant lung nodule enhancement (yes [>15 HU] or no [≤15 HU]), and pleural indentation (yes or no) based on previous studies 5,13,15…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

<p>Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations</p>

Chen¹,

Yan²,

Zhao³

et al. 2019

CMAR

View full text Add to dashboard Cite

PurposeThis study aimed to develop and validate a nomogram for predicting the malignancy of small (8–20 mm) solid indeterminate solitary pulmonary nodules (SPNs) in a Chinese population by using routine clinical and computed tomography data.MethodsThe prediction model was developed using a retrospective cohort that comprised 493 consecutive patients with small indeterminate SPNs who were treated between December 2012 and December 2016. The model was independently validated using a second retrospective cohort comprising 216 consecutive patients treated between January 2017 and May 2018. The investigated variables included patient characteristics (e.g., age and smoking history), nodule parameters (e.g., marginal spiculation and significant enhancement), and tumor biomarker levels (e.g., carcinoembryonic antigen). A prediction model was developed by using multivariable logistic regression analysis, and the model’s performance was presented as a nomogram. The model was evaluated based on its discriminative ability, calibration, and clinical usefulness.ResultsThe developed nomogram was ultimately based on age, marginal spiculation, significant enhancement, and pleural indentation. The Harrell concordance index values were 0.869 in the training cohort (95% confidence interval: 0.837–0.901) and 0.847 in the validation cohort (95% confidence interval: 0.792–0.902). The Hosmer-Lemeshow test revealed good calibration in each of the training and validation cohorts. Decision curve analysis confirmed that the nomogram was clinically useful (risk threshold from 0.10 to 0.85).ConclusionPatient age, marginal spiculation, significant enhancement, and pleural indentation are independent predictors of malignancy in small indeterminate solid SPNs. The developed nomogram is easy-to-use and may allow the accurate prediction of malignancy in small indeterminate solid SPNs among Chinese patients.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

<p>Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations</p>

Chen¹,

Yan²,

Zhao³

et al. 2019

CMAR

View full text Add to dashboard Cite

show abstract

“…The main advantage of ANN is able to approximate any nonlinear mathematical function [31]. SVMs are based on the principle of structural risk minimization and put the data into a multidimensional space to achieve classification with a hyperplane, which have distinct advantages in solving problems such as the small sample size, nonlinear, or high dimensional pattern types [3,31]. Every approach has its advantages and disadvantages, and it is necessary to try different methods to seek a suitable model for the diagnosis of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based multimode diagnosis system for lung cancer

Duan

Cao

Hong

et al. 2020

Aging

View full text Add to dashboard Cite

As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broadspectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

show abstract

Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke

et al. 2020

View full text Add to dashboard Cite

show abstract

Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods

Cited by 18 publications

References 31 publications

<p>Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations</p>

<p>Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations</p>

Development of a machine learning-based multimode diagnosis system for lung cancer

Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke

Contact Info

Product

Resources

About