Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma

Abstract

Purpose: We aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma.

Patients and methods: A total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis.

Results: The radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82).

Conclusion: The CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.

Keywords: biomarker; computed tomography; lung adenocarcinoma; radiomics.

Figure 1

Radiomic method. Original images from patients with non-small-cell lung cancer. Experienced radiologists segmented the tumor region of interest (ROI) on all computed tomography slices to extract the radiomic features. Features such as tumor shape, intensity, and texture features were extracted from the ROI to discriminate invasive lung adenocarcinoma from non-invasive lung adenocarcinoma. Abbreviations: AUC, the area under the ROC curve; ROC, receiver operating characteristic.

Figure 2

(A and B) The least absolute shrinkage and selection operator (LASSO) binary logistic regression model for feature selection. The features retained in the previous step were introduced into the LASSO regression model. First, a 10-fold cross-validation method was used to screen the LASSO regression model hyperparameter (λ) and select the model with the smallest error (λ). The retention (not equal to 0) was used to calculate the rad-score, which represents the sum of the product of the feature and the corresponding coefficient. Receiver operating characteristic analysis was used to discriminate the ability of the rad-score to identify invasive and non-invasive adenocarcinoma in the training and validation sets.

Figure 3

(A and B) Features used in the model and a description of the rad-score calculation. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis identified 14 suitable radiomic features for building the prediction model (A). On the left is the training cohort, on the right is the validation cohort, and red and blue represent the real group. The middle black line represents the cut-off value. If the black line can separate the red point from the blue point, it means that the model identification ability is better (B).

Figure 4

(A and B) Receiver operating characteristic (ROC) curves for the training and validation cohorts. Radiomic features had the potential ability to predict the preoperative discrimination of invasive and non-invasive lung adenocarcinoma. (The area under the ROC curve [AUC] for the training cohort was 0.83. The AUC for the validation cohort was 0.77).

Figure 5

Calibration curves of radiomics model in training cohort (A) and validation cohort (B). Calibration curve evaluated the correspondence between the predicted probabilities and observed probabilities. The closer the dot line to the grey solid line, the better prediction of the model was. Besides, according to Hosmer-Lemeshow test, the predicted probabilities have no significantly difference with observed probabilities with p>0.05.

Figure 6

Decision curve analysis for radiomic signature. The radiomic signature has higher standard net benefit at the threshold from 0.1 to 0.9 than the all positive prediction (Line Labeled All) and all negative prediction (Line labeled None).