Key Points Question Are peritumoral radiomics features extracted from pretreatment computed tomography images predictive of pathological complete response following neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma? Findings In this diagnostic study of 231 patients, the developed model integrating intratumoral and peritumoral radiomics features achieved improvement of predictive performance (area under the receiver operating characteristic curve, 0.852) compared with the conventionally constructed model merely using intratumoral radiomics features (area under the receiver operating characteristic curve, 0.730). Meaning Peritumoral radiomics may provide additional predictive value for treatment response estimation in esophageal squamous cell carcinoma and thus benefit individualized therapeutic strategies.

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The tumor microenvironment (TME) of esophageal squamous cell carcinoma (ESCC) impacts tumor progression but is poorly understood. We obtained tumor tissues from 279 patients after esophagectomy and characterized the TME in intraepithelial and stromal regions using multiplex fluorescent immunohistochemistry (mfIHC). A heterogeneous immune population infiltrating tumor and the uninvolved esophageal tissue were observed. The infiltration of intraepithelial programmed death ligand 1 (PD-L1)positive tumor-associated macrophages (TAMs) and stromal granzyme B + activated cytotoxic T cells (aCTLs) correlated with both prolonged overall survival (OS) and disease-free survival (DFS). The intraepithelial memory T cell infiltration predicted longer OS, while intraepithelial and stromal regulatory T cell (Treg) infiltration was associated with shortened OS and DFS, respectively. Multivariate models combining immune infiltrates and clinicopathological factors outperformed tumor-node-metastasis (TNM) stage in predicting OS and DFS at 3 and 5 years. The infiltration of Treg inversely correlated with that of the antitumor effectors including CTLs, aCTLs, and natural killer (NK) cells. Intraepithelial memory T cell infiltration also negatively correlated with PD-L1 expression. In spatial analysis, intraepithelial dendritic cell (DC)-memory T cell engagement increased in high PD-L1 + TAM infiltration group. The characterization of the TME revealed a complex interplay between immune populations and may be employed to stratify patient for prognosis prediction and immunotherapy.

Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.