国际医学放射学杂志2025，Vol.48Issue(3)：312-318,7.DOI:10.19300/j.2025.Z21857
影像组学及深度学习预测非小细胞肺癌新辅助免疫治疗疗效的研究进展
Research progress on radiomics and deep learning in predicting the efficacy of neoadjuvant immunotherapy for non-small cell lung cancer
摘要
Abstract
Preoperative prediction of the efficacy of neoadjuvant immunotherapy(NIT)in non-small cell lung cancer(NSCLC)helps identify patients who are likely to benefit,reduce the risk of postoperative recurrence and metastasis,and improve prognosis.Radiomics and deep learning can be used to explore imaging biomarkers for predicting NIT efficacy in NSCLC.Radiomics,through global feature analysis or habitat analysis methods,can effectively quantify the temporal and spatial heterogeneity of tumors,providing a quantitative basis for efficacy prediction.Deep learning,on the other hand,adaptively extracts deep imaging features to evaluate treatment response.This review summarizes recent research progress in radiomics and deep learning technologies for predicting NIT efficacy in NSCLC patients,and discusses the associated technical challenges and corresponding solutions.关键词
非小细胞肺癌/新辅助治疗/免疫治疗/影像组学/深度学习/生物标志物Key words
Non-small cell lung cancer/Neoadjuvant therapy/Immunotherapy/Radiomics/Deep learning/Biomarker分类
医药卫生引用本文复制引用
黄诗洋,石磊..影像组学及深度学习预测非小细胞肺癌新辅助免疫治疗疗效的研究进展[J].国际医学放射学杂志,2025,48(3):312-318,7.基金项目
国家自然科学基金面上项目(82272085) （82272085）
浙江省自然科学基金(LY22H220001) （LY22H220001）
