Evaluation of Endoscopic Response Using Deep Neural Network in Esophageal Cancer Patients Who Received Neoadjuvant Chemotherapy

Background: We previously reported that endoscopic response evaluation can preoperatively predict the prognosis and distribution of residual tumors after neoadjuvant chemotherapy (NAC). In this study, we developed artificial intelligence (AI)-guided endoscopic response evaluation using a deep neural network to discriminate endoscopic responders (ERs) in patients with esophageal squamous cell carcinoma (ESCC) after NAC. Method: Surgically resectable ESCC patients who underwent esophagectomy following NAC were retrospectively analyzed in this study. Endoscopic images of the tumors were analyzed using a deep neural network. The model was validated with a test data set using 10 newly collected ERs and 10 newly collected non-ER images. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the endoscopic response evaluation by AI and endoscopists were calculated and compared. Results: Of 193 patients, 40 (21%) were diagnosed as ERs. The median sensitivity, specificity, PPV, and NPV values for ER detection in 10 models were 60%, 100%, 100%, and 71%, respectively. Similarly, the median values by the endoscopist were 80%, 80%, 81%, and 81%, respectively. Conclusion: This proof-of-concept study using a deep learning algorithm demonstrated that the constructed AI-guided endoscopic response evaluation after NAC could identify ER with high specificity and PPV. It would appropriately guide an individualized treatment strategy that includes an organ preservation approach in ESCC patients.