Objective： To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas （MIAs）and invasive pulmonary adenocarcinomas （IPAs） based on radiomic feature analysis with computed tomography...Objective： To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas （MIAs）and invasive pulmonary adenocarcinomas （IPAs） based on radiomic feature analysis with computed tomography（CT）.Methods： A total of 109 patients with ground-glass opacity lesions （GGOs） in the lungs determined by CTexaminations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation andsegmentation of the GGOs as regions of interest （ROIs）, the patients were subdivided into three groups based onpathologic analyses： the preinvasive lesions （including atypical adenomatous hyperplasia and adenocarcinoma insitu） subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. Thedata analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguishany two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic （ROC） curve wasapplied to accurately evaluate the performances of the regression models.Results： We found that the voxel count feature （P〈0.001） could be used as a predictor for distinguishing IPAsfrom preinvasive lesions. However, the surface area feature （P=0.040） and the extruded surface area feature（P=0.013） could be predictors of IPAs compared with MIAs. In addition, the correlation feature （P=0.046） coulddistinguish preinvasive lesions from MIAs better.Conclusions： Preinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CTimages, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseasesare very important for clinical surgery.展开更多
基金supported by the Special Fund of Pharmacy, Radiology and Ecsomatics of Tianjin Medical University Cancer Institute & Hospital (No. Y1507)
文摘Objective： To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas （MIAs）and invasive pulmonary adenocarcinomas （IPAs） based on radiomic feature analysis with computed tomography（CT）.Methods： A total of 109 patients with ground-glass opacity lesions （GGOs） in the lungs determined by CTexaminations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation andsegmentation of the GGOs as regions of interest （ROIs）, the patients were subdivided into three groups based onpathologic analyses： the preinvasive lesions （including atypical adenomatous hyperplasia and adenocarcinoma insitu） subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. Thedata analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguishany two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic （ROC） curve wasapplied to accurately evaluate the performances of the regression models.Results： We found that the voxel count feature （P〈0.001） could be used as a predictor for distinguishing IPAsfrom preinvasive lesions. However, the surface area feature （P=0.040） and the extruded surface area feature（P=0.013） could be predictors of IPAs compared with MIAs. In addition, the correlation feature （P=0.046） coulddistinguish preinvasive lesions from MIAs better.Conclusions： Preinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CTimages, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseasesare very important for clinical surgery.
