A radiomics nomogram based on contrast-enhanced CT for preoperative prediction of Lymphovascular invasion in esophageal squamous cell carcinoma

Abstract

Background and purpose: To develop a radiomics nomogram based on contrast-enhanced computed tomography (CECT) for preoperative prediction of lymphovascular invasion (LVI) status of esophageal squamous cell carcinoma (ESCC).

Materials and methods: The clinical and imaging data of 258 patients with ESCC who underwent surgical resection and were confirmed by pathology from June 2017 to December 2021 were retrospectively analyzed.The clinical imaging features and radiomic features were extracted from arterial-phase CECT. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature selection and signature construction. Multivariate logistic regression analysis was used to develop a radiomics nomogram prediction model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance and clinical effectiveness of the model in preoperative prediction of LVI status.

Results: We constructed a radiomics signature based on eight radiomics features after dimensionality reduction. In the training cohort, the area under the curve (AUC) of radiomics signature was 0.805 (95% CI: 0.740-0.860), and in the validation cohort it was 0.836 (95% CI: 0.735-0.911). There were four predictive factors that made up the individualized nomogram prediction model: radiomic signatures, TNRs, tumor lengths, and tumor thicknesses.The accuracy of the nomogram for LVI prediction in the training and validation cohorts was 0.790 and 0.768, respectively, the specificity was 0.800 and 0.618, and the sensitivity was 0.786 and 0.917, respectively. The Delong test results showed that the AUC value of the nomogram model was significantly higher than that of the clinical model and radiomics model in the training and validation cohort(P<0.05). DCA results showed that the radiomics nomogram model had higher overall benefits than the clinical model and the radiomics model.

Conclusions: This study proposes a radiomics nomogram based on CECT radiomics signature and clinical image features, which is helpful for preoperative individualized prediction of LVI status in ESCC.

Keywords: computed tomography; decision curve analysis; esophageal squamous cell carcinoma; lymphovascular invasion; nomogram.

Figure 1

Workflow for image preprocessing,image segmentation, radiomics feature extraction, feature reduction, and model building and validation for this study.

Figure 2

Texture feature selection using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model.

Figure 3

The most predictive subset of radiomics features for predicting LVI in ESCC.

Figure 4

Developed radiomics nomogram. The radiomics nomogram was developed in the primary cohort, with the rad-score, TNR, length, and thickness incorporated.

Figure 5

ROC curves of the radiomics, clinical and nomogram models for predicting LVI in the training cohort (A) and validation cohort (B).

Figure 6

Decision curve analysis (DCA) of the training cohort (A) and validation cohort (B). DCA indicated that using the nomogram model to predict LVI would be more beneficial than a "treat-all" or "treat-none" regimen.