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. 2024 Jun 14:15:1405146.
doi: 10.3389/fimmu.2024.1405146. eCollection 2024.

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

Jia-Ling Wang #  1   2 , Lian-Sha Tang #  1   2 , Xia Zhong #  3 , Yi Wang  2 , Yu-Jie Feng  2 , Yun Zhang  3 , Ji-Yan Liu  1
Affiliations

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

Jia-Ling Wang et al. Front Immunol. .

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.

Keywords: computed tomography; esophageal squamous cell cancer; major pathological response; neoadjuvant immunotherapy; radiomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of patient selection. CT, computed tomography.
Figure 2
Figure 2
Selection of radiomic features and comparison of radiomics score. (A) Selection of the regulation weight parameter (λ) for the least absolute shrinkage and selection operator. (B) coefficient curves for 10 radiomic features. (C) There were significant differences in the radiomics score between the MPR group and non-MPR group in both training cohort and validation cohort. MPR, major pathological response.
Figure 3
Figure 3
The receiver operating characteristic curve of the three models. (A) In the training group; (B) in the validation group.
Figure 4
Figure 4
Nomogram, calibration curve, and decision curve analysis of the combined model. (A) Nomogram of the combined model; (B) calibration curve for the major pathological response in the training group; (C) calibration curve for the major pathological response in the validation group; (D) decision curve analysis of the three models.
See this image and copyright information in PMC

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