Radiomics model based on CT images for distinguishing invasive lung adenocarcinoma with micropapillary or solid structure
- VernacularTitle:基于CT影像组学鉴别伴微乳头及实体型结构浸润性肺腺癌
- Author:
Fen WANG
1
;
Teng ZHANG
2
;
Mei YUAN
2
;
Genji BO
1
Author Information
1. Department of Medical Imaging, Huai an First People s Hospital Affiliated to Nanjing Medical University, Huai an, 223300, Jiangsu, P. R. China
2. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China
- Publication Type:Journal Article
- Keywords:
Radiomics;
micropapillary;
solid;
adenocarcinoma;
computed tomography
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2024;31(01):65-70
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the radiomics features to distinguish invasive lung adenocarcinoma with micropapillary or solid structure. Methods A retrospective analysis was conducted on patients who received surgeries and pathologically confirmed invasive lung adenocarcinoma in our hospital from April 2016 to August 2019. The dataset was randomly divided into a training set [including a micropapillary/solid structure positive group (positive group) and a micropapillary/solid structure negative group (negative group)] and a testing set (including a positive group and a negative group) with a ratio of 7∶3. Two radiologists drew regions of interest on preoperative high-resolution CT images to extract radiomics features. Before analysis, the intraclass correlation coefficient was used to determine the stable features, and the training set data were balanced using synthetic minority oversampling technique. After mean normalization processing, further radiomics features selection was conducted using the least absolute shrinkage and selection operator algorithm, and a 5-fold cross validation was performed. Receiver operating characteristic (ROC) curves were depicted on the training and testing sets to evaluate the diagnostic performance of the radiomics model. Results A total of 340 patients were enrolled, including 178 males and 162 females with an average age of 60.31±6.69 years. There were 238 patients in the training set, including 120 patients in the positive group and 118 patients in the negative group. There were 102 patients in the testing set, including 52 patients in the positive group and 50 patients in the negative group. The radiomics model contained 107 features, with the final 2 features selected for the radiomics model, that is, Original_ glszm_ SizeZoneNonUniformityNormalized and Original_ shape_ SurfaceVolumeRatio. The areas under the ROC curve of the training and the testing sets of the radiomics model were 0.863 (95%CI 0.815-0.912) and 0.857 (95%CI 0.783-0.932), respectively. The sensitivity was 91.7% and 73.7%, the specificity was 78.8% and 84.0%, and the accuracy was 85.3% and 78.4%, respectively. Conclusion There are differences in radiomics features between invasive pulmonary adenocarcinoma with or without micropapillary and solid structures, and the radiomics model is demonstrated to be with good diagnostic value.
