Scholarlink

Objective <p>Development and validation of a radiomics model based on pretreatment deoxy-2-[fluorine-18]-fluoro-D-glucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) imaging for predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC).</p> Methods <p>A retrospective analysis was performed on 145 patients with ESCC, using pretreatment <sup>18</sup>F-FDG PET/CT imaging data and clinical information. Patients were randomly divided into training and validation cohorts in a 7:3 ratio. In the training cohort, independent risk factors for LNM in ESCC were identified through univariate and multivariate logistic regression analyses. Radiomic features were extracted from the PET images, and the least absolute shrinkage and selection operator (LASSO) regression was used for dimensionality reduction. Features highly correlated with LNM in ESCC were selected. The weighted radiomics score (Radscore) was then calculated based on these selected features. The diagnostic performance of each factor was evaluated using receiver operating characteristic (ROC) curves, and a prediction model nomogram was established. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the model. Finally, the model was validated using the validation cohort.</p> Results <p>Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and gender were significantly associated with LNM in ESCC (all <i>P</i> &lt; 0.05). SUVmax was found to be an independent risk factor for predicting LNM in ESCC. In the training and validation cohorts, the areas under the curve (AUC) for SUVmax combined with Radscore were 0.809 (95% CI: 0.723–0.894) and 0.801 (95% CI: 0.661–0.941), respectively, both of which were higher than those for SUVmax and Radscore alone. A nomogram, a comprehensive predictive model based on SUVmax and Radscore, may improve the net clinical benefit for patients.</p> Conclusion <p>The nomogram, a predictive model developed using <sup>18</sup>F-FDG PET/CT-based radiomics, offers reliable predictive value for LNM in ESCC and is expected to serve as a reference tool for therapeutic decision making in patients with ESCC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predictive value of 18F-FDG PET/CT-based radiomics model for lymph node metastasis in esophageal squamous cell carcinoma

  • 期刊论文

  • 发表日期：

    2025年10月15日

查看全文
  • Jianlin Wang,
  • Shufang Wu,
  • Aiqi Shi,
  • Hanlin Zhang,
  • Meng Niu,
  • Xiaoxue Tian,
  • Jiangyan Liu

摘要

Objective

Development and validation of a radiomics model based on pretreatment deoxy-2-[fluorine-18]-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging for predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC).

Methods

A retrospective analysis was performed on 145 patients with ESCC, using pretreatment 18F-FDG PET/CT imaging data and clinical information. Patients were randomly divided into training and validation cohorts in a 7:3 ratio. In the training cohort, independent risk factors for LNM in ESCC were identified through univariate and multivariate logistic regression analyses. Radiomic features were extracted from the PET images, and the least absolute shrinkage and selection operator (LASSO) regression was used for dimensionality reduction. Features highly correlated with LNM in ESCC were selected. The weighted radiomics score (Radscore) was then calculated based on these selected features. The diagnostic performance of each factor was evaluated using receiver operating characteristic (ROC) curves, and a prediction model nomogram was established. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the model. Finally, the model was validated using the validation cohort.

Results

Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and gender were significantly associated with LNM in ESCC (all P < 0.05). SUVmax was found to be an independent risk factor for predicting LNM in ESCC. In the training and validation cohorts, the areas under the curve (AUC) for SUVmax combined with Radscore were 0.809 (95% CI: 0.723–0.894) and 0.801 (95% CI: 0.661–0.941), respectively, both of which were higher than those for SUVmax and Radscore alone. A nomogram, a comprehensive predictive model based on SUVmax and Radscore, may improve the net clinical benefit for patients.

Conclusion

The nomogram, a predictive model developed using 18F-FDG PET/CT-based radiomics, offers reliable predictive value for LNM in ESCC and is expected to serve as a reference tool for therapeutic decision making in patients with ESCC.