Surgical endoscopy · 2025
Endoscopic ultrasound-based radiomics for predicting pathologic upgrade in esophageal low-grade intraepithelial neoplasia.
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Chen Yajing, Sun Shuhan, Miao Shumei, Chen Han, Zhou Xiaoying, Yu Feihong
Аннотация
BACKGROUND: There is no consensus on managing patients with endoscopic suspicion of early esophageal squamous cell carcinoma (ESCC) but biopsy-confirmed low-grade intraepithelial neoplasia (LGIN). The aim of this study is to evaluate the utility of an endoscopic ultrasound (EUS)-based radiomics nomogram for predicting esophageal LGIN pathological progression before diagnostic endoscopic submucosal dissection (ESD).
METHODS: In the development phase, EUS images of 535 patients who had biopsy-confirmed LGIN and were undergoing ESD were retrospectively included. Concurrently, 251 patients were prospectively included for independent model validation. A radiomics signature (RS) was constructed using Pearson test and the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics nomogram was then developed with multivariate logistic regression to predict pathologic upgrade before ESD. Model performance was assessed with receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: Following stepwise multivariate logistic regression analysis, statistically significant clinical features were incorporated into the clinical predictive model. From EUS images, 105 radiomic features were extracted, with 11 key features selected for RS development. The RS showed strong predictive performance in identifying pathologic upgrade (AUC = 0.786). Moreover, when integrated with the clinical model (AUC = 0.648), the RS performance remarkably improved (AUC = 0.818). These results were subsequently validated in the prospective test cohort (RS: AUC = 0.792; Clinical model: AUC = 0.669; Combined model: AUC = 0.821). The combined model presented as a nomogram also excelled in calibration tests and DCA, underlining its potential for clinical application.
CONCLUSION: The EUS-based radiomics nomogram showed potential for predicting pathologic upgrade in esophageal LGIN, which helps to distinguish high-risk from low-risk cases and assists clinicians in assessing the necessity of diagnostic ESD.
METHODS: In the development phase, EUS images of 535 patients who had biopsy-confirmed LGIN and were undergoing ESD were retrospectively included. Concurrently, 251 patients were prospectively included for independent model validation. A radiomics signature (RS) was constructed using Pearson test and the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics nomogram was then developed with multivariate logistic regression to predict pathologic upgrade before ESD. Model performance was assessed with receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: Following stepwise multivariate logistic regression analysis, statistically significant clinical features were incorporated into the clinical predictive model. From EUS images, 105 radiomic features were extracted, with 11 key features selected for RS development. The RS showed strong predictive performance in identifying pathologic upgrade (AUC = 0.786). Moreover, when integrated with the clinical model (AUC = 0.648), the RS performance remarkably improved (AUC = 0.818). These results were subsequently validated in the prospective test cohort (RS: AUC = 0.792; Clinical model: AUC = 0.669; Combined model: AUC = 0.821). The combined model presented as a nomogram also excelled in calibration tests and DCA, underlining its potential for clinical application.
CONCLUSION: The EUS-based radiomics nomogram showed potential for predicting pathologic upgrade in esophageal LGIN, which helps to distinguish high-risk from low-risk cases and assists clinicians in assessing the necessity of diagnostic ESD.