Cao Mengxuan, Xu Ruixin, You Yi, Huang Chencui, Tong Yahan, Zhang Ruolan, Zhang Yanqiang, Yu Pengcheng, Wang Yi, Chen Wujie, Cheng Xiangdong, Zhang Lei
Department of Medical Imaging, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
Department of Gastric surgery, Institutes of Basic Medicine and Cancer (IBMC), Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, 310022, China.
BMC Med Imaging. 2025 Jul 1;25(1):242. doi: 10.1186/s12880-025-01777-z.
In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph node metastasis is crucial.
We retrospectively collected 256 patients with AEG from two centres. The radiomics features were extracted from the preoperative diagnostic CT images, and the feature selection method and machine learning method were applied to reduce the feature size and establish the predictive imaging features. By comparing the three machine learning methods, the best radiomics nomogram was selected, and the average AUC was obtained by 20 repeats of fivefold cross-validation for comparison. The fusion model was constructed by logistic regression combined with clinical factors. On this basis, ROC curve, calibration curve and decision curve of the fusion model are constructed.
The predictive efficacy of fusion model for tumour invasion depth was higher than that of radiomics nomogram, with an AUC of 0.764 vs. 0.706 in the test set, P < 0.001, internal validation set 0.752 vs. 0.697, P < 0.001, and external validation set 0.756 vs. 0.687, P < 0.001, respectively. The predictive efficacy of the lymph node metastasis fusion model was higher than that of the radiomics nomogram, with an AUC of 0.809 vs. 0.732 in the test set, P < 0.001, internal validation set 0.841 vs. 0.718, P < 0.001, and external validation set 0.801 vs. 0.680, P < 0.001, respectively.
We have developed a fusion model combining radiomics and clinical risk factors, which is crucial for the accurate preoperative diagnosis and treatment of AEG, advancing precision medicine. It may also spark discussions on the imaging feature differences between AEG and GC (Gastric cancer).
在精准医学背景下，放射组学已成为解决医学问题的关键技术。对于食管胃交界腺癌（AEG），建立基于术前CT的AEG侵袭和淋巴结转移预测模型至关重要。
我们回顾性收集了来自两个中心的256例AEG患者。从术前诊断性CT图像中提取放射组学特征，并应用特征选择方法和机器学习方法来减小特征规模并建立预测性影像特征。通过比较三种机器学习方法，选择最佳的放射组学列线图，并通过20次五折交叉验证重复计算获得平均AUC进行比较。融合模型通过逻辑回归结合临床因素构建。在此基础上，构建融合模型的ROC曲线、校准曲线和决策曲线。
融合模型对肿瘤浸润深度的预测效能高于放射组学列线图，在测试集中AUC分别为0.764和0.706，P<0.001；内部验证集中分别为0.752和0.697，P<0.001；外部验证集中分别为0.756和0.687，P<0.001。淋巴结转移融合模型的预测效能高于放射组学列线图，在测试集中AUC分别为0.809和0.732，P<0.001；内部验证集中分别为0.841和0.718，P<0.001；外部验证集中分别为0.801和0.680，P<0.001。
我们开发了一种结合放射组学和临床危险因素的融合模型，这对于AEG的术前准确诊断和治疗、推进精准医学至关重要。它也可能引发关于AEG和GC（胃癌）之间影像特征差异的讨论。
