Comparing Robot-Assisted, Minimally Invasive, Transcervical, and Transhiatal Esophagectomy for Esophageal Cancer: A Causal Deep Learning Meta-Analysis with Neural Architecture

This meta-analysis evaluates the effectiveness of four esophagectomy procedures—Robot-Assisted Minimally Invasive Esophagectomy (RAMIE), Minimally Invasive Esophagectomy (MIE), Transcervical Esophagectomy (TCE), and Transhiatal Esophagectomy (THE)—for treating esophageal cancer, utilizing causal deep learning techniques to assess key clinical outcomes. Data from 70,102 patients were analyzed, focusing on operative time, postoperative complications, mortality, hospital stay, and lymph node retrieval. Unlike traditional statistical methods, deep learning models capture non-linear relationships and adjust for multiple confounders, providing more accurate and reliable predictions. The results show RAMIE to be the most effective procedure, with an average operative time of 350 minutes, reduced blood loss (250 mL), and fewer complications (24%). MIE follows closely with 300 minutes of operative time, 200 mL of blood loss, and a 30% complication rate. TCE and THE have higher complication rates (up to 40% and 42%, respectively), alongside longer recovery times. THE, although less effective in clinical outcomes, proved to be more cost-efficient. SUCRA rankings confirmed RAMIE’s superiority (88%), compared to MIE (83%), TCE (76%), and THE (66%). Additionally, decision tree analysis with 95.63% accuracy and 96.17% cross-validation performance supported RAMIE as the optimal choice, highlighting its precision, fewer complications, and faster recovery, despite higher costs. This study underscores the significance of deep learning, enhancing surgical decision-making and optimizing patient outcomes, with machine learning offering a more robust and nuanced approach compared to traditional methods.