Computed Tomography-Based Intratumor Heterogeneity Predicts Response to Immunotherapy Plus Chemotherapy in Esophageal Squamous Cell Carcinoma.

Rationale and Objectives: This study explored the intratumor heterogeneity (ITH) of esophageal squamous cell carcinoma (ESCC) using computed tomography (CT) and investigated the value of CT-based ITH in predicting the response to immune checkpoint inhibitor (ICI) plus chemotherapy in patients with ESCC.
Materials and Methods: This retrospective study included 416 patients with ESCC who received ICI plus chemotherapy at two independent hospitals between January 2019 and July 2022. Multiparametric CT features were extracted from ESCC lesions and screened using hierarchical clustering and dimensionality reduction algorithms. Logistic regression and machine learning models based on selected features were developed to predict treatment response and validated in separate datasets. ITH was quantified using the score calculated by the best-performing model and visualized through feature clustering and feature contribution heatmaps. A gene set enrichment analysis (GSEA) was performed to identify the biological pathways underlying the CT-based ITH.
Results: The extreme gradient boosting model based on CT-derived ITH had higher discriminative power, with areas under the receiver operating characteristic curve of 0.864 (95% confidence interval [CI]: 0.774-0.954) and 0.796 (95% CI: 0.698-0.893) in the internal and external validation sets. The CT-based ITH pattern differed significantly between responding and non-responding patients. The GSEA indicated that CT-based ITH was associated with immunity-, keratinization-, and epidermal cell differentiation-related pathways.
Conclusion: CT-based ITH is an effective biomarker for identifying patients with ESCC who could benefit from ICI plus chemotherapy. Immunity-, keratinization-, and epidermal cell differentiation-related pathways may influence the patient's response to ICI plus chemotherapy.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)