Zhu Liwen, Zhao Ben, Xia Tianyi, Chang Di, Xia Cong, Liu Mengqiu, Li Ridong, Cao Buyue, Qiu Yue, Yu Yaoyao, Zhou Shuwei, Chen Huayu, Cai Wu, Ding Zhimin, Lu Chunqiang, Tang Tianyu, Song Yang, Wang Yuancheng, Ye Jing, Liu Ying, Ju Shenghong
Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China.
Insights Imaging. 2025 Jun 27;16(1):141. doi: 10.1186/s13244-025-02025-2.
To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management.
Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split into four cohorts: training (n = 192), validation (n = 82), testing (n = 100), and clinical utilization (n = 163). A radiomics model was constructed based on contrast-enhanced CT (CECT) to predict LNM, and its performance was evaluated using the areas under the curve (AUC). Kaplan-Meier analysis was used to assess the prognostic and therapeutic decision-assisting value of the radiomics model.
A total of 437 patients (mean age: 63.1 years ± 9.2 standard deviation; 253 men) were included. The radiomics model outperformed other models with AUCs of 0.84, 0.82, and 0.78 in the training, validation, and testing cohorts (all p < 0.05), respectively. LNM predicted by the radiomics model was significantly associated with overall survival (p < 0.001). Kaplan-Meier analysis revealed that patients with a higher risk of LNM also had worse outcomes (all p < 0.05). Additionally, among the high-risk subgroup identified by the radiomics model in the clinical utilization cohort, patients who underwent dissection of ≥ 15 lymph nodes exhibited better overall survival compared to those with fewer lymph nodes dissected (p = 0.002).
The radiomics model we constructed demonstrated impressive performance in predicting LNM and prognosis, suggesting its potential for optimizing the clinical management of PDAC.
This radiomics model can predict the risk of lymph nodes metastasis and prognosis of patients in pancreatic ductal adenocarcinoma and has potential value in selecting patients who can benefit from different extents of lymph nodes dissection.
Thorough lymph node dissection is important for achieving the best prognosis in pancreatic ductal adenocarcinoma (PDAC). The radiomics model can accurately predict lymph node status and stratify patients' prognosis. This radiomics model enhances the clinical management of PDAC.
建立一种放射组学模型,以预测胰腺导管腺癌(PDAC)患者的淋巴结转移(LNM)情况,并评估其在临床管理中的价值。
回顾性纳入来自四个中心的病理确诊为PDAC的患者,并将其分为四个队列:训练队列(n = 192)、验证队列(n = 82)、测试队列(n = 100)和临床应用队列(n = 163)。基于对比增强CT(CECT)构建放射组学模型以预测LNM,并使用曲线下面积(AUC)评估其性能。采用Kaplan-Meier分析评估放射组学模型的预后和治疗决策辅助价值。
共纳入437例患者(平均年龄:63.1岁±9.2标准差;男性253例)。放射组学模型在训练、验证和测试队列中的AUC分别为0.84、0.82和0.78,优于其他模型(所有p < 0.05)。放射组学模型预测的LNM与总生存期显著相关(p < 0.001)。Kaplan-Meier分析显示,LNM风险较高的患者预后也较差(所有p < 0.05)。此外,在临床应用队列中由放射组学模型确定的高危亚组中,接受≥15个淋巴结清扫的患者与清扫淋巴结较少的患者相比,总生存期更好(p = 0.002)。
我们构建的放射组学模型在预测LNM和预后方面表现出色,表明其在优化PDAC临床管理方面的潜力。
该放射组学模型可预测胰腺导管腺癌患者的淋巴结转移风险和预后,在选择可从不同程度淋巴结清扫中获益的患者方面具有潜在价值。
彻底的淋巴结清扫对于实现胰腺导管腺癌(PDAC)的最佳预后很重要。放射组学模型可准确预测淋巴结状态并对患者预后进行分层。该放射组学模型增强了PDAC的临床管理。