影像组学与人工智能在肺磨玻璃结节生长预测中的研究进展  

Research Progress of Radiomics and Artificial Intelligence in the Growth Prediction of Pulmonary Ground-glass Nodules

作　　者：吴馨雨 华媚琦 夏雨霏 万杉田 王晟涵 种浩宇 高晨 WU Xinyu;HUA Meiqi;XIA Yufei;WAN Shantian;WANG Shenghan;ZHONG Haoyu;GAO Chen(The First Affiliated Hospital,Zhejiang Chinese Medical University(Zhejiang Provincial Hospital of Chinese Medicine),Hangzhou 310006,China;The First Clinical Medical School,Zhejiang Chinese Medical University,Hangzhou 310006,China)

机构地区：[1]浙江中医药大学附属第一医院(浙江省中医院),杭州310006 [2]浙江中医药大学第一临床医学院,杭州310006

出　　处：《中国医学计算机成像杂志》2025年第1期141-145,共5页Chinese Computed Medical Imaging

基　　金：浙江省医药卫生科技计划项目(2024KY129)。

摘　　要：肺磨玻璃结节(GGN)的生长在一定程度上反映病灶的恶性潜能。既往研究将GGN的直径、体积、质量、质量倍增时间、体积倍增时间作为衡量结节生长的重要指标,但这些指标的获得均需要长期随访。近年来,随着影像组学和人工智能技术的快速发展,有关两者在磨玻璃结节生长预测中的作用取得了一定研究进展。本文就GGN生长特征和影像组学及人工智能技术在GGN生长预测中的研究进展进行综述,旨在为GGN管理提供一定参考依据。The growth of ground glass nodules(GGN)in the lungs reflects to some extent the malignant potential of the lesion.Previous studies have taken the diameter,volume,mass,mass doubling time and volume doubling time of GGN as important indicators to measure the growth of nodules,but the acquisition of these indicators requires long-term follow-up.In recent years,with the rapid development of radiomics and artificial intelligence technology,researches on radiomics and artificial intelligence in the growth prediction of GGN have made some progress.This article reviews the growth characteristics of GGN in lung and the research progress of radiomics and artificial intelligence technology in the growth prediction of GGN,aiming to provide a reference for the management of GGN.

关 键 词：影像组学 人工智能 深度学习 磨玻璃结节 生长 

分 类 号：R734.2[医药卫生—肿瘤] R730.44[医药卫生—临床医学]