Differential value of CT quantitative analysis for different subtypes of invasive lung adenocarcinoma presenting as subsolid nodule / 实用放射学杂志

ABSTRACT

Objective To explore the value of CT quantitative analysis in differentiating lepidicGpredominant adenocarcinoma (LPA)from invasive nonGlepidicGpredominant adenocarcinoma (INV)manifested as subsolid nodules (SSN).Methods A total of patients with lung adenocarcinoma manifested as SSNs on CT images were divided into LPA group and INV group according to their pathological results.Total volume,solid volume,solid volume percentage,total mass,solid mass,solid mass percentage and threeGdimensional mean CT value of the nodules were calculated after segmenting pulmonary nodules by a 3DGCT segmentation software,meanwhile the oneGdimensional mean CT value and maximal diameter were manually measured.SPSS 22.0 software was used for statistical analysis. Results Solid volume (0.1 6 ±0.3 6 cm3 vs 1.2 6 ±2.1 7 cm3 ,P＜0.00 1),solid volume percentage (4.7 9 ±5.40% vs 2 6.3 3 ± 1 5.6 3%, P＜0.001),total mass (1 180.64±1 751.46 mg vs 2 386.59±3 224.54 mg,P＝0.010),solid mass (151.64±337.53 mg vs 1 257.34± 2 220.9 2 mg,P＜0.00 1 ),solid mass percentage (7.9 8 ± 8.5 8% vs 3 7.2 3 ± 1 8.83%,P＜0.00 1 ),threeGdimensional mean CT value (－492.26±71.21 HU vs －350.73±94.52 HU,P＜0.001 )and oneGdimensional mean CT value (－472.29 ± 1 12.46 HU vs －282.02 ± 1 5 9.13 HU,P＜0.001)in LPA group were found significantly lower than those in INV group.There were no significant differences in the maximal diameter and total volume between LPA group and INV group.Solid volume percentage and solid mass percentage were selected according to stepwise discriminant analysis.The accuracy of Bayes modes by using substitution method and cross validation method were 84.2% and 83.3%,respectively.Conclusion Solid volume percentage and solid mass percentage were important parameters for differentiating LPA from INV.Quantitative analysis of SSN was very helpful to preoperatively evaluate the subtypes and prognosis of lung adenocarcinoma by using 3DGCT segmentation technique.