Wu Hao, Wu XiaoLi, Miao ShouLiang, Cao GuoQuan, Su Huang, Pan Jie, Xu YiLun, Zhou JianWei
Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jpn J Radiol. 2025 Apr 11. doi: 10.1007/s11604-025-01780-y.
Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improving patient outcomes. This study leverages the power of deep learning, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze arterial phase enhanced CT images and predict LNM in ESCC patients.
A retrospective study included 441 ESCC patients who underwent radical esophagectomy and regional lymphadenectomy. CT imaging was performed using contrast-enhanced CT scanners. Tumor region segmentation was conducted to determine the region of interest (ROI), where local tumor 3D volumes were extracted as input for the model. The novel deep learning model, LymphoReso-Net, combined CNN and LSTM networks to process and learn from medical imaging data. The model outputs a binary prediction for LNM. GRAD-CAM was integrated to enhance model interpretability. Performance was evaluated using fivefold cross-validation with metrics including accuracy, sensitivity, specificity, and AUC-ROC. The gold standard for LNM confirmation was pathologically confirmed LNM shortly after the CT.
LymphoReso-Net demonstrated promising performance with an average accuracy of 0.789, an AUC of 0.836, a sensitivity of 0.784, and a specificity of 0.797. GRAD-CAM provided visual explanations of the model's decision-making, aiding in identifying critical regions associated with LNM prediction.
This study introduces a novel deep learning framework, LymphoReso-Net, for predicting LNM in ESCC patients. The model's accuracy and interpretability offer valuable insights into lymphatic spread patterns, enabling more informed therapeutic decisions.
食管鳞状细胞癌（ESCC）是一项重大的全球健康挑战，预后尤其严峻。准确预测ESCC中的淋巴结转移（LNM）对于优化治疗策略和改善患者预后至关重要。本研究利用深度学习的力量，特别是卷积神经网络（CNN）和长短期记忆（LSTM）网络，来分析动脉期增强CT图像并预测ESCC患者的LNM。
一项回顾性研究纳入了441例行根治性食管切除术和区域淋巴结清扫术的ESCC患者。使用对比增强CT扫描仪进行CT成像。进行肿瘤区域分割以确定感兴趣区域（ROI），从中提取局部肿瘤三维体积作为模型的输入。新型深度学习模型LymphoReso-Net结合了CNN和LSTM网络，以处理医学影像数据并从中学习。该模型输出LNM的二元预测结果。集成了GRAD-CAM以增强模型的可解释性。使用五折交叉验证评估性能，指标包括准确率、灵敏度、特异性和AUC-ROC。LNM确认的金标准是CT检查后不久经病理证实的LNM。
LymphoReso-Net表现出良好的性能，平均准确率为0.789，AUC为0.836，灵敏度为0.784，特异性为0.797。GRAD-CAM为模型的决策提供了可视化解释，有助于识别与LNM预测相关的关键区域。
本研究引入了一种用于预测ESCC患者LNM的新型深度学习框架LymphoReso-Net。该模型的准确性和可解释性为淋巴扩散模式提供了有价值的见解，有助于做出更明智的治疗决策。
