磁共振成像2026，Vol.17Issue(3)：201-205,234,6.DOI:10.12015/issn.1674-8034.2026.03.029
磁共振成像结合人工智能在宫颈癌精准诊疗中的研究进展
Research progress of magnetic resonance imaging combined with artificial intelligence in the precision diagnosis and treatment of cervical cancer
摘要
Abstract
Precision diagnosis and therapy for cervical cancer,a major global public health challenge,are hindered by tumor heterogeneity and the limitations of conventional assessment methods.The integration of artificial intelligence(AI)with multi-parametric MRI(mp-MRI)provides a new paradigm for non-invasively assessing tumor pathophysiology.Research in this field has established an AI-driven,hierarchical technical framework spanning from anatomical localization to molecular characterization:At the anatomical level,the introduction of novel architectures such as Transformer and state space models(SSM)has overcome the receptive field limitations of convolutional neural networks(CNN),achieving precise lesion segmentation within complex pelvic anatomical backgrounds.At the functional level,AI optimizes the parameter fitting models of intravoxel incoherent motion(IVIM),diffusion kurtosis imaging(DKI),and dynamic contrast-enhanced MRI(DCE-MRI)via deep neural networks(DNN),significantly enhancing the robustness of quantitative parameters.Furthermore,it utilizes habitat analysis techniques to quantify intra-tumoral microscopic heterogeneity for predicting lymph node metastasis(LNM)and lymphovascular space invasion(LVSI).At the molecular level,radiomics and radiogenomics leverage machine learning to deeply mine high-dimensional imaging features,establishing non-linear mappings between imaging phenotypes and molecular characteristics such as gene mutations and the immune microenvironment.Additionally,the integration of circulating tumor DNA(ctDNA)data facilitates the formation of a multi-modal"imaging biopsy"paradigm.This AI-empowered three-stage system(segmentation-functional analysis-molecular decoding)connects the entire chain of precision diagnosis and treatment for cervical cancer.However,the clinical translation of this system is still limited by systemic challenges such as inadequate data standardization,limited model generalizability,and poor interpretability.This article systematically reviews these advancements,deeply analyzes technical principles,clinical values,and practical dilemmas,aiming to provide a forward-looking perspective for promoting this technology towards clinically-oriented individualized precision medicine.关键词
宫颈癌/深度学习/分子标志物/磁共振成像/影像组学/精准医学/综述Key words
cervical cancer/deep learning/molecular biomarkers/magnetic resonance imaging/radiomics/precision medicine/review分类
医药卫生引用本文复制引用
孔琦琪,冯宇泽,班允清..磁共振成像结合人工智能在宫颈癌精准诊疗中的研究进展[J].磁共振成像,2026,17(3):201-205,234,6.基金项目
Xinjiang Uygur Autonomous Region"Tianshan Yingcai"Healthcare Talent Cultivation Program(No.TSYC202301B083) （No.TSYC202301B083）
Xinjiang Medical University Innovation and Entrepreneurship Project(No.CXCY2025014). 新疆维吾尔自治区"天山英才"医药卫生培养计划项目(编号:TSYC202301B083) （No.CXCY2025014）
新疆医科大学创新创业项目(编号:CXCY2025014) （编号:CXCY2025014）
