AUTHOR=Wang Jia-Ling , Tang Lian-Sha , Zhong Xia , Wang Yi , Feng Yu-Jie , Zhang Yun , Liu Ji-Yan 

TITLE=A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma

JOURNAL=Frontiers in Immunology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1405146

DOI=10.3389/fimmu.2024.1405146

ISSN=1664-3224

ABSTRACT=Background: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display varibalevarying treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response toof NIT infor ESCC patients.Methods: This retrospective study included 82 ESCC patients and randomly divided these patients into the training group (n=57) and the validation group (n=25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was applied to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. The aArea under curve (AUC) was applied to evaluate the predictive ability of the models, andwhile decision curve analysis (DCA) and calibration curves were performed to test the application of the models.Results: One clinical data (radiotherapy) and ten radiomics features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve an excellent  predictive performance, with the AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model.Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT, with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providinge an individualized treatment regimens for patients.