La Radiologia medica · 2026
Intratumoral and peritumoral CT radiomics combined with clinical and hematologic inflammatory markers for predicting lymph node metastasis in esophageal squamous cell carcinoma: a retrospective single-center study.
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Yang Xinmiao, Niu Hongfeng, Yang Ziqing, Ren Jingjing, Wang Xinrong, Deng Kan, Wu Qingxia, Kang Xiaohong, Zhao Junqiang, Liang Changhua
Аннотация
OBJECTIVE: To develop and validate a hybrid model integrating intratumoral (TR) and peritumoral (PTR) CT radiomics with clinical and hematologic inflammatory markers for predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC).
METHODS: This retrospective single-center cohort study included 304 patients with pathologically confirmed ESCC. Patients were randomly divided into a training cohort (n = 243) and a test cohort (n = 61) in an 8:2 ratio. Radiomics features were extracted from manually segmented three-dimensional volumes of interest (VOIs) on venous-phase thin-slice contrast-enhanced CT images. The PTR VOIs were defined as 1-, 2-, and 3-mm ring-shaped regions surrounding the TR VOI. Clinical data, hematologic inflammatory markers, and TR and PTR CT radiomics features were used to construct six categories of models: a clinical model, a hematologic model, TR + PTR radiomics models, a clinical + TR + PTR radiomics model, a hematologic + TR + PTR radiomics model, and a clinical + hematologic + TR + PTR hybrid model. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: The overall LNM prevalence was 37.5% (114/304), including 37.9% (92/243) in the training cohort and 36.1% (22/61) in the test cohort. Multivariate analysis identified cN stage as an independent predictor for LNM (p < 0.05). Among the TR + PTR radiomics models, the TR + PTR1mm model showed the strong performance. The hybrid model exhibited strong performance, with AUC values of 0.898 (95% CI 0.859-0.937) and 0.867 (95% CI 0.766-0.968) for the training and test cohorts, respectively. At the optimal threshold, the hybrid model achieved a sensitivity of 0.818 and a specificity of 0.769 in the test cohort. The hybrid model demonstrated favorable net benefit in DCA compared with all other models.
CONCLUSION: The hybrid model based on TR and 1-mm PTR CT radiomics combined with clinical and hematologic inflammatory markers may serve as a promising noninvasive tool for preoperative individualized risk assessment of LNM in ESCC. However, this retrospective single-center study had a relatively small internal validation cohort and lacked external validation. Therefore, further multicenter studies with external validation are warranted to confirm the robustness and generalizability of the proposed model.
METHODS: This retrospective single-center cohort study included 304 patients with pathologically confirmed ESCC. Patients were randomly divided into a training cohort (n = 243) and a test cohort (n = 61) in an 8:2 ratio. Radiomics features were extracted from manually segmented three-dimensional volumes of interest (VOIs) on venous-phase thin-slice contrast-enhanced CT images. The PTR VOIs were defined as 1-, 2-, and 3-mm ring-shaped regions surrounding the TR VOI. Clinical data, hematologic inflammatory markers, and TR and PTR CT radiomics features were used to construct six categories of models: a clinical model, a hematologic model, TR + PTR radiomics models, a clinical + TR + PTR radiomics model, a hematologic + TR + PTR radiomics model, and a clinical + hematologic + TR + PTR hybrid model. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: The overall LNM prevalence was 37.5% (114/304), including 37.9% (92/243) in the training cohort and 36.1% (22/61) in the test cohort. Multivariate analysis identified cN stage as an independent predictor for LNM (p < 0.05). Among the TR + PTR radiomics models, the TR + PTR1mm model showed the strong performance. The hybrid model exhibited strong performance, with AUC values of 0.898 (95% CI 0.859-0.937) and 0.867 (95% CI 0.766-0.968) for the training and test cohorts, respectively. At the optimal threshold, the hybrid model achieved a sensitivity of 0.818 and a specificity of 0.769 in the test cohort. The hybrid model demonstrated favorable net benefit in DCA compared with all other models.
CONCLUSION: The hybrid model based on TR and 1-mm PTR CT radiomics combined with clinical and hematologic inflammatory markers may serve as a promising noninvasive tool for preoperative individualized risk assessment of LNM in ESCC. However, this retrospective single-center study had a relatively small internal validation cohort and lacked external validation. Therefore, further multicenter studies with external validation are warranted to confirm the robustness and generalizability of the proposed model.