Abstract
The management of early-stage esophageal cancer remains suboptimal due to fragmented coordination between endoscopic and surgical modalities, resulting in diagnostic delays and inconsistent therapeutic decision-making. This study presents an integrated clinical framework that synergizes advanced endoscopic techniques (endoscopic submucosal dissection, endoscopic mucosal resection) with thoracic surgical interventions, supported by multimodal staging protocols to improve diagnostic accuracy by approximately 20%. A key innovation is the implementation of a streamlined 72-h clinical pathway, which reduces treatment delays through real-time multidisciplinary collaboration and intraoperative risk stratification using machine learning-based predictive models. To address systemic barriers, the framework incorporates competency-based cross-disciplinary training programs and value-based reimbursement structures, targeting a 28.7% reduction in treatment discrepancies. Future advancements focus on molecular stratification and health economic evaluations to further refine precision oncology approaches. This paradigm shift from sequential to integrated care demonstrates potential to enhance both oncologic outcomes and healthcare resource utilization in early esophageal cancer management.