Comprehensive Summary
The following research discusses how artificial intelligence (AI) can be utilized to improve precision neoadjuvant immunochemotherapy (nICT) in patients suffering from locally advanced esophageal squamous cell carcinoma (ESCC). Paper overviews recent advances integrating radiomics and deep learning for the prediction of pathological complete response (pCR following nICT from voxel-level image features derived from clinical CT scans. The research authors highlight a recent multicenter study encompassing 741 patients, with the AI model showing superb generalizability with AUC values ranging from 0.83-0.92 and validation accuracy up to 91%. Among the technical innovations were voxel-level radiomic maps combined with Vision-Mamba architectures, capturing global spatial dependencies. Notably, interpretability tools such as SHAP unveiled tumor regions most predictive of prognosis, in concordance with pathological patterns.
Outcomes and Implications
The paper highlights how AI-powered imaging can personalize patient stratification, inform treatment adaptation, and prevent unnecessary surgery by identifying those who have the highest likelihood of pathological complete response (pCR). Although encouraging, writers do note that pCR alone is not a validated surrogate for long-term survival, and prospective trials with overall survival as the endpoint are required. AI can facilitate risk-adaptive strategies, dynamic surveillance, and multimodal integration. Challenges remain in standardizing imaging protocols, ensuring real-world deployment, and improving interpretability. However, federated learning and cloud-based systems may enable broader clinical integration. Together, these advances signal AI’s growing role in precision oncology and perioperative management of ESCC.