人工智能辅助诊断系统影像学微特征与磨玻璃结节样肺腺癌预后的关系

魏宁; 蔺瑞江; 马敏杰; 陈昶; 韩彪

doi:10.3971/j.issn.1000-8578.2021.21.0255

人工智能辅助诊断系统影像学微特征与磨玻璃结节样肺腺癌预后的关系

Relation Between Imaging Microfeatures of Artificial Intelligence-assisted Diagnosis System and Prognosis of Lung Adenocarcinomas Presented as Ground-glass Nodules

摘要

摘要:
目的探讨人工智能(AI)辅助诊断系统影像学微特征与磨玻璃结节样肺腺癌预后的关系。
方法回顾性纳入162例肺部影像为磨玻璃结节(GGN)的腺癌患者的CT资料，依据影像学特征分为纯磨玻璃结节(PGGN)组及混合型磨玻璃结节(MGGN)组，利用AI辅助诊断系统分别提取其影像学微特征，并分析其与患者预后的关系。
结果 PGGN术后5年OS、RFS分别为89.7%、88.5%; MGGN组则分别为81.0%、79.0%，PGGN组术后5年OS及RFS均优于MGGN组(χ²=6.289/7.255, 均P < 0.05)。多因素Cox回归显示，微血管集束(P < 0.001)、结节标准体积(P=0.013)及结节长径(P < 0.001)等影像学微特征为术后OS的独立危险因素; 微血管集束(P < 0.001)、结节标准体积(P=0.017)、结节长径(P=0.005)、结节中心密度(P=0.038)等影像学微特征及淋巴结转移(P < 0.001)为术后RFS的独立危险因素。
结论 AI辅助诊断系统可有效预测GGN型肺腺癌的预后，并对GGN的临床精准诊疗及早期肺癌防治有一定的参考价值。

Abstract:
Objective To investigate the relation between the imaging microfeatures of AI-assisted diagnosis system and the prognosis of lung adenocarcinomas presented as ground-glass nodules (GGN).
Methods We retrospectively analyzed CT data of 162 patients with lung adenocarcinomas presented as GGN. According to different imaging characteristics, the patients were divided into pure ground glass nodules (PGGN) group and mixed ground glass nodules (MGGN) group. The AI-assisted diagnosis system was used to extract their imaging microfeatures, and their relation with the prognosis of the patients was analyzed.
Results The five-year OS and RFS were 89.7% and 88.5% in PGGN group, and 81.0% and 79.0% in MGGN group (χ²=6.289/7.255, P < 0.05). Multivariate Cox regression showed that imaging microfeatures such as microvascular cluster (P < 0.001), standard nodule volume (P=0.013) and nodule length (P < 0.001) were independent risk factors for OS, meanwhile, imaging microfeatures such as microvascular cluster (P < 0.001), standard nodule volume (P=0.017), nodule length (P=0.005), nodule central density (P=0.038) and lymph node metastasis (P < 0.001) were independent risk factors for RFS.
Conclusion The AI-assisted diagnosis system can effectively predict the prognosis of lung adenocarcinomas presented as GGN, and it also has a certain reference value for the clinical precision diagnosis and treatment of GGN and the prevention and treatment of early lung cancer.

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