AI Article Synopsis

  • A study was conducted on patients with resectable esophageal squamous cell carcinoma (ESCC) to create a predictive model that uses radiomics from enhanced CT images and clinical data to determine treatment response to neoadjuvant immunotherapy (NIT).
  • The research involved 82 patients split into training and validation groups, utilizing logistic regression and a nomogram to develop the predictive model based on selected clinical and radiomic features.
  • Results showed high predictive accuracy with AUC values of 0.93 for the training group and 0.85 for the validation group, indicating that this model could effectively guide personalized treatment decisions for ESCC patients before starting therapy.

Article Abstract

Background: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable 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 to NIT in ESCC patients.

Methods: This retrospective study included 82 ESCC patients who were randomly divided 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 conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models.

Results: One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with 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.

Conclusion: 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 providing individualized treatment regimens for patients.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211602PMC
http://dx.doi.org/10.3389/fimmu.2024.1405146DOI Listing

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