Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

Abstract

Purpose: To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.

Methods: This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility.

Results: The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram.

Conclusion: A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

Keywords: CT image; Identification; Nomogram; Pulmonary nodule; Radiomic.

Fig. 1

Patient screening flowchart

Fig. 2

Radiomics analysis process

Fig. 3

ROC curves of five models in the training set (A)and validation set (B). T1 model: T1 radiomics model; T0 model: T0 radiomics model; Delta model: Delta radiomics model; CR model: clinicoradiological model

Fig. 4

The integrated nomogram incorporating the CR-signature, T0-signature, T1-signature, and Delta-signature

Fig. 5

Calibration curves of the nomogram in the training and validation sets (A, B). The x-axis indicates the predicted probability estimated by the nomogram, while the y-axis indicates the actual probability. Apparent probabilities and bias-corrected probabilities are indicated by dotted and solid lines, respectively

Fig. 6

Decision curves of 5 models in all data set. The net income is shown on the y-axis, and the probability threshold is shown on the x-axis