摘要
目的探讨基于深度学习(DL)的肺结节检测算法对不同大小肺结节的检出效果。方法回顾性分析344例肺结节患者的胸部CT图片,计算并比较基于DL的肺结节检出模型对不同大小肺结节的检出率(相对于医师诊断结果),分析模型检出假阳性结节的类别。结果344份CT图像中,医师共诊断710个0~30 mm肺结节。模型共检出2495个候选肺结节,其中真阳性675个(相对于医师诊断结果),模型对结节的检出率为95.07%(675/710)。模型对0~4 mm肺结节的检出率为82.80%(77/93),0~5 mm结节为90.15%(238/264),0~6 mm结节为92.94%(395/425),5~10 mm结节为97.94%(381/389),10~20 mm结节为98.21%(55/56),20~30 mm结节为100%(1/1),模型对不同大小肺结节的检出率差异无统计学意义(χ^2=21.72,P>0.05)。模型检出假阳性结节中,50.38%(917/1820)为医师最初诊断漏诊者,32.53%(592/1820)为血管断面。结论DL肺结节检出模型对肺结节的整体检出率较高(95.07%),且不受结节大小的影响。
Objective To explore the diagnostic effects of detection algorithm based on deep learning(DL)on pulmonary nodules with different sizes.Methods CT images of 344 patients with pulmonary nodules were retrospectively analyzed.The detection rates of the model based on DL for pulmonary nodules with different sizes(relative to the physician's diagnosis)were calculated and compared,and the false positive nodules detected by the model were analyzed.Results On 344 CT images,physicians diagnosed 710 pulmonary nodules of 0-30 mm.A total of 2495 candidate pulmonary nodules were detected by the model,among which 675 were true positive relative to the physician's diagnosis.The detection rate of nodules of the model was 95.07%(675/710),of 0-4 mm was 82.80%(77/93),of 0-5 mm was 90.15%(238/264),of 0-6 mm was 92.94%(395/425),of 5-10 mm was 97.94%(381/389),of 10-20 mm was 98.21%(55/56),and of 20-30 mm was 100%(1/1).There was no statistically significant difference of detection rate for pulmonary nodules with different sizes of the model(χ^2=21.72,P>0.05).Among the false positive nodules detected by the model,50.38%(917/1820)were missed by physicians,and 32.53%(592/1820)were vascular sections.Conclusion The overall detection rate of pulmonary nodules of DL model is high(95.07%),which is not affected by the size of nodules.
作者
王娟
唐丽丽
于明川
那曼丽
张滨
WANG Juan;TANG Lili;YU Mingchuan;NA Manli;ZHANG Bin(Department of Radiology,Peking University Shougang Hospital,Beijing 100144,China)
出处
《中国医学影像技术》
CSCD
北大核心
2019年第12期1771-1774,共4页
Chinese Journal of Medical Imaging Technology
