Model integrating CT-based radiomics and genomics for survival prediction in esophageal cancer patients receiving definitive chemoradiotherapy

Abstract

Background: Definitive chemoradiotherapy (dCRT) is a standard treatment option for locally advanced stage inoperable esophageal squamous cell carcinoma (ESCC). Evaluating clinical outcome prior to dCRT remains challenging. This study aimed to investigate the predictive power of computed tomography (CT)-based radiomics combined with genomics for the treatment efficacy of dCRT in ESCC patients.

Methods: This retrospective study included 118 ESCC patients who received dCRT. These patients were randomly divided into training (n = 82) and validation (n = 36) groups. Radiomic features were derived from the region of the primary tumor on CT images. Least absolute shrinkage and selection operator (LASSO) regression was conducted to select optimal radiomic features, and Rad-score was calculated to predict progression-free survival (PFS) in training group. Genomic DNA was extracted from formalin-fixed and paraffin-embedded pre-treatment biopsy tissue. Univariate and multivariate Cox analyses were undertaken to identify predictors of survival for model development. The area under the receiver operating characteristic curve (AUC) and C-index were used to evaluate the predictive performance and discriminatory ability of the prediction models, respectively.

Results: The Rad-score was constructed from six radiomic features to predict PFS. Multivariate analysis demonstrated that the Rad-score and homologous recombination repair (HRR) pathway alterations were independent prognostic factors correlating with PFS. The C-index for the integrated model combining radiomics and genomics was better than that of the radiomics or genomics models in the training group (0.616 vs. 0.587 or 0.557) and the validation group (0.649 vs. 0.625 or 0.586).

Conclusion: The Rad-score and HRR pathway alterations could predict PFS after dCRT for patients with ESCC, with the combined radiomics and genomics model demonstrating the best predictive efficacy.

Keywords: Contrast-enhanced computed tomography; Esophageal squamous cell carcinoma; Genomics; Prognosis; Radiomics.

Fig. 1

Recruitment and selection process of patients

Fig. 2

Workflow of the current study

Fig. 3

Evaluation of the stability of radiomics features based on ICC. Features with ICC < 0.8 were removed, and the remaining 638 radiomics features were included in the data analysis as stable feature parameters

Fig. 4

Selection of radiomic features associated with PFS based on LASSO regression models. (a): The crossvalidation curve. The vertical axis is mean square error, and the horizontal axis is lambda (λ). (b): Coefficient curves for radiomic features. The vertical axis represents the radiomic features’ coefficients, and the horizontal axis is λ

Fig. 5

Kaplan–Meier survival curves constructed based on the three models Applying the radiomics model, PFS curves for patients with Rad-score ≥ 0.36 (Rad-score = 1) and Rad-score < 0.36 (Rad-score = 0) in the training (a) and validation (b) groups. Applying the genomics model, PFS curves for patients with HRR pathway mutations (HRR = 1) and without (wild-type, HRR = 0) in the training (c) and validation (d) groups. Applying the integrated model, PFS curves for patients with high (Rad_HRR = 2), intermediate (Rad_HRR = 1), and low (Rad_HRR = 0) progression risk in the training (e) and validation (f) groups

Fig. 6

Evaluation of the predictive performances for the three models for PFS in ESCC patients after dCRT. Receiver operating characteristic curves showing the predictive performances of the radiomics model (a, b), genomics model (c, d), and integrated model (e, f) in the training and validation groups, respectively