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已发表论文
局部晚期食管鳞状细胞癌新辅助化疗免疫治疗疗效的三角洲影像组学特征与炎症生物标志物整合研究
Authors Xu B, Zhu J, Fan T, Lian J, Ma J, Zhou Y, Lu H
Received 3 July 2025
Accepted for publication 6 November 2025
Published 3 December 2025 Volume 2025:18 Pages 16907—16919
DOI https://doi.org/10.2147/JIR.S548314
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Felix Marsh-Wakefield
Benjie Xu,1,* Jiahao Zhu,1,* Tiantian Fan,2,* Jie Lian,1 Jianqun Ma,3 Yang Zhou,2 Haibo Lu1
1Department of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China; 2Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China; 3Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yang Zhou, Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China, Tel +86-045186298000, Fax +86-045186663760, Email zhouyang@hrbmu.edu.cn Haibo Lu, Department of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China, Tel +86-045186298000, Fax +86-045185718309, Email luhaibo@hrbmu.edu.cn
Purpose: Neoadjuvant chemo-immunotherapy (NACI) significantly increases the pathological complete response (pCR) rate in locally advanced esophageal squamous cell carcinoma (LA-ESCC). However, not all patients benefit from this approach. This study aims to construct a multi-omics model for predicting the efficacy of NACI in LA-ESCC patients by integrating delta-radiomics features (delta-RFs) and the dynamic changes in inflammatory biomarkers.
Patients and Methods: Our study was the first to develop a multi-omics model based on dynamic CT imaging features and inflammatory indices in patients with LA-ESCC. A total of 217 patients were divided into training (n = 152) and validation (n = 65) cohorts. Following the completion of the data standardization process, delta-RFs were extracted using the PyRadiomics, representing the relative imaging changes between pre- and post-treatment. The algorithm known as the least absolute shrinkage and selection operator (LASSO) was selected to pinpoint the most significant RFs for predicting pCR. The imaging signatures and clinical characteristics were integrated by the logistic regression analysis. Finally, a nomogram was constructed based on the identified independent predictors.
Results: The pCR rates were approximately 30% among the enrolled patients. A total of 1834 delta-RFs were extracted. The 12 delta-RFs signature was determined by the optimal regularization parameter using the LASSO algorithm. Furthermore, multivariate regression analyses confirmed that tumor length, delta-RFs signature, and delta-systemic immune-inflammation index (SII) served as independent predictors for pCR prediction. Finally, a nomogram was constructed and the multi-omics model exhibited impressive predictive performance, with area under curve (AUC) values of 0.853 and 0.796 for the training and validation cohorts, respectively. This method aims to support informed treatment decisions and facilitate the development of personalized treatment strategies.
Conclusion: The multi-omics model, which is based on the delta-RFs signature and delta-SII indicators prior to surgery, effectively predicts the NACI treatment response in patients with LA-ESCC.
Keywords: locally advanced esophageal squamous cell carcinoma, neoadjuvant chemo-immunotherapy, delta-radiomics features, systemic immune-inflammation index, Pathological complete response
1Department of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China; 2Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China; 3Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yang Zhou, Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China, Tel +86-045186298000, Fax +86-045186663760, Email zhouyang@hrbmu.edu.cn Haibo Lu, Department of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150000, People’s Republic of China, Tel +86-045186298000, Fax +86-045185718309, Email luhaibo@hrbmu.edu.cn
Purpose: Neoadjuvant chemo-immunotherapy (NACI) significantly increases the pathological complete response (pCR) rate in locally advanced esophageal squamous cell carcinoma (LA-ESCC). However, not all patients benefit from this approach. This study aims to construct a multi-omics model for predicting the efficacy of NACI in LA-ESCC patients by integrating delta-radiomics features (delta-RFs) and the dynamic changes in inflammatory biomarkers.
Patients and Methods: Our study was the first to develop a multi-omics model based on dynamic CT imaging features and inflammatory indices in patients with LA-ESCC. A total of 217 patients were divided into training (n = 152) and validation (n = 65) cohorts. Following the completion of the data standardization process, delta-RFs were extracted using the PyRadiomics, representing the relative imaging changes between pre- and post-treatment. The algorithm known as the least absolute shrinkage and selection operator (LASSO) was selected to pinpoint the most significant RFs for predicting pCR. The imaging signatures and clinical characteristics were integrated by the logistic regression analysis. Finally, a nomogram was constructed based on the identified independent predictors.
Results: The pCR rates were approximately 30% among the enrolled patients. A total of 1834 delta-RFs were extracted. The 12 delta-RFs signature was determined by the optimal regularization parameter using the LASSO algorithm. Furthermore, multivariate regression analyses confirmed that tumor length, delta-RFs signature, and delta-systemic immune-inflammation index (SII) served as independent predictors for pCR prediction. Finally, a nomogram was constructed and the multi-omics model exhibited impressive predictive performance, with area under curve (AUC) values of 0.853 and 0.796 for the training and validation cohorts, respectively. This method aims to support informed treatment decisions and facilitate the development of personalized treatment strategies.
Conclusion: The multi-omics model, which is based on the delta-RFs signature and delta-SII indicators prior to surgery, effectively predicts the NACI treatment response in patients with LA-ESCC.
Keywords: locally advanced esophageal squamous cell carcinoma, neoadjuvant chemo-immunotherapy, delta-radiomics features, systemic immune-inflammation index, Pathological complete response