Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.