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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 challenges, which we broadly classify into biomarker-based and AI-based limitations. We further discuss potential strategies to address these challenges. This review systematically summarizes recent AI-based predictive models for NIT response across cancer types. By offering a structured analysis of current methodologies and challenges, we aim to guide future research and accelerate the integration of AI into precision immunotherapy.
Deng et al. (Wed,) studied this question.