Guo Zhenjiang, Wang Ning, Zhao Guangyuan, Du Liqiang, Cui Zhaobo, Liu Fangzhen
Department of Gastrointestinal, Hengshui People's Hospital, Hengshui, Hebei, China.
Department of Respiratory and Critical Care Medicine, Hengshui People's Hospital, Hengshui, Hebei, China.
Front Oncol. 2024 Dec 10;14:1503728. doi: 10.3389/fonc.2024.1503728. eCollection 2024.
To develop and validate a model for preoperative prediction of positive proximal margins for adenocarcinoma of the esophagogastric junction (AEG) by transabdominal approach, and to analyze the safe margin distances for patients with different risks of positive proximal margins.
A retrospective analysis was performed on 284 AEG patients who underwent surgery via the transabdominal approach at Hengshui People's Hospital between January 2017 and December 2023. Patients were divided into a training set (=201, first five years) and a test set (=83, last two years). Clinicopathologic factors potentially influencing margin status were collected. The synthetic minority oversampling technique (SMOTE) was applied to address class imbalance in the training set. Two nomogram models were developed: one based on the original training set and the other using the SMOTE dataset. The model's performance was compared using the test set, with the area under the curve (AUC) used to evaluate discrimination and the Hosmer-Lemeshow test used for model fit. The best-performing model was used to calculate total scores for the entire cohort, and the optimal cutoff value was determined via the ROC curve. Patients were classified into low- and high-risk groups based on the total score, and optimal margin distances were determined using Youden's index.
The model developed using the SMOTE dataset showed superior AUC for predicting positive proximal margins in the test set compared to the model based on the original training set (0.814 0.780). Independent predictors of positive proximal margins included Borrmann classification, Lauren classification, cT stage, tumor differentiation, and Siewert classification. The Hosmer-Lemeshow test showed a good model fit (χ² = 5.397, = 0.612). Using a cutoff total score of 206.811, patients were divided into low-risk (score < 206.811) and high-risk (score ≥ 206.811) groups, with an AUC of 0.788. For the low-risk group, a proximal margin distance of 2.75 cm yielded an AUC of 0.824, with a sensitivity of 54.5%, specificity of 97.9%, and a Youden's index of 0.524. For the high-risk group, a margin distance of 3.85 cm provided an AUC of 0.813, sensitivity of 73.1%, specificity of 80.0%, and a Youden's index of 0.531.
The nomogram may offer a valuable preoperative tool for assessing the risk of positive proximal margins in AEG patients. While it holds the potential to inform surgical decision-making and help determine appropriate margin distances, further validation in larger and more diverse cohorts is needed to confirm its clinical utility.
建立并验证一种经腹入路术前预测食管胃交界腺癌（AEG）近端切缘阳性的模型，并分析不同近端切缘阳性风险患者的安全切缘距离。
对2017年1月至2023年12月在衡水市人民医院经腹入路手术的284例AEG患者进行回顾性分析。患者分为训练集（=201例，前五年）和测试集（=83例，后两年）。收集可能影响切缘状态的临床病理因素。应用合成少数过采样技术（SMOTE）解决训练集中的类别不平衡问题。建立了两个列线图模型：一个基于原始训练集，另一个使用SMOTE数据集。使用测试集比较模型的性能，曲线下面积（AUC）用于评估鉴别能力，Hosmer-Lemeshow检验用于评估模型拟合度。使用表现最佳的模型计算整个队列的总分，并通过ROC曲线确定最佳截断值。根据总分将患者分为低风险和高风险组，并使用约登指数确定最佳切缘距离。
与基于原始训练集的模型相比，使用SMOTE数据集开发的模型在测试集中预测近端切缘阳性的AUC更高（0.814对0.780）。近端切缘阳性的独立预测因素包括Borrmann分类、Lauren分类、cT分期、肿瘤分化和Siewert分类。Hosmer-Lemeshow检验显示模型拟合良好（χ² = 5.397，P = 0.612）。使用截断总分206.811，患者分为低风险（得分<206.811）和高风险（得分≥206.811）组，AUC为0.788。对于低风险组，近端切缘距离2.75 cm时AUC为0.824，敏感性为54.5%，特异性为97.9%，约登指数为0.524。对于高风险组，切缘距离3.85 cm时AUC为0.813，敏感性为73.1%，特异性为80.0%，约登指数为0.531。
列线图可为评估AEG患者近端切缘阳性风险提供有价值的术前工具虽然它有可能为手术决策提供信息并帮助确定合适的切缘距离，但需要在更大和更多样化的队列中进行进一步验证以确认其临床实用性。
