AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review

作者：Yishu Deng, Tailin Li, Yunze Wang, Silin Chen, Silin Chen, Feilong Tang, Taoyu Zhu, Jiayi Ran, Bo Yang, Xiaohan Zhang, Ruijie Xu, Manas K. Ray, Yimin Zhang, Shuifang Chen, Shuifang Chen, Jian Liu · 发表于：Cancer Cell International · 年份：2025 · DOI：10.1186/s12935-025-04063-8 · 被引用次数：5 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis

Neoadjuvant immunotherapy (NIT) has emerged as a transformative treatment strategy across various cancer types. However, due to the significant heterogeneity of tumors, patients exhibit highly variable responses to NIT, making the accurate preoperative identification of those who would benefit a pressing clinical challenge. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has opened new pathways for predicting treatment response. AI-driven approaches have the ability to extract latent features from high-dimensional, multimodal oncological data, facilitating the construction of efficient predictive models that can optimize individualized treatment strategies. In this review, we systematically summarize existing AI-driven computational approaches for NIT response prediction, categorizing them into indirect and direct predictive paradigms. The indirect paradigm predicts clinically validated surrogate biomarkers to infer therapeutic response to NIT. In contrast, the direct paradigm leverages AI to analyze high-throughput data and establish data-driven biomarkers that directly predict clinical endpoints of NIT. Additionally, we categorize existing AI predictive models based on data modalities, spanning radiomics, pathomics, genomics, and multi-omics approaches, each providing distinct insights into tumor characteristics and treatment response. Despite notable progress, current predictive models still face significant challen...