The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma

Abstract

Background: Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN.

Methods: This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated.

Results: The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735-0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707-0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723-0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power.

Conclusion: The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.

Keywords: Computed tomography; Diagnosis; Lung adenocarcinoma; Nomogram; Radiomics.

Fig. 1

Inclusion pathway of pulmonary nodules

Fig. 2

Radiomic workflow. Contrast-enhanced chest CT images are retrieved for radiomic feature extraction. ROIs of pulmonary nodules are segmented and the corresponding ROIs are stacked up to construct VOI of the nodules. Six categories of radiomic features are extracted from within the defined VOI, including histogram features, form factor features, and texture features

Fig. 3

Radiomic feature selection using LASSO logistic regression. a Selection of the tuning parameter (λ). The LASSO regression model was used with penalty parameter tuning that was conducted by 10-fold cross-validation based on minimum criteria. The binomial deviance was plotted versus log (λ). The dotted vertical lines were plotted at the optimal λ values based on the minimum criteria and 1 standard error of the minimum criteria. The optimal λ value of 0.0809 with log (λ) = −2.515 was selected. b LASSO coefficient profiles of the 385 radiomic features. The dotted vertical line was plotted at the λ value of 0.0809, resulting in five nonzero coefficients

Fig. 4

Violin plots present the boxplots of the five radiomic features in the training and validation datasets, respectively

Fig. 5

Violin plots present the boxplots of the radiomic score in the training and validation datasets, respectively

Fig. 6

The radiomic nomogram for lung adenocarcinoma prediction. a Radiomic nomogram developed for the prediction of lung adenocarcinoma, which incorporates radiomic signature, internal composition and margins of nodule. Plots (b) and (c) present the calibration curves of the nomogram in the training and validation datasets, respectively. The calibration curve illustrates the calibration of the nomogram in terms of the agreement between the predicted risk of malignancy and the observed outcomes of malignancy. The 45°diagonal line represents a perfect prediction, and the red line represents the predictive performance of the nomogram. The red line has a closer fit to the diagonal line, which indicates better predictive accuracy of the nomogram