Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma
Background: Lung cancer is one of the most serious cancers in the world. Subtypes of lung adenocarcinoma can be quickly distinguished by analyzing 3D radiomic signatures and radiological features.Methods: This study included 493 patients from 3 hospitals with a total of 506 lesions confirmed as minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), or invasive adenocarcinoma (IAC). After segmenting the lesion area, 3D radiomic signatures were extracted using the PyRadiomics package v. 3.0.1 implemented in Python (https://pyradiomics.readthedocs.io/en/latest/index.html), and the corresponding radiological features were collected. Subsequently, the top 100 features were identified by feature screening methods, including the Spearman rank correlation and minimum redundancy maximum relevance (mRMR) feature selection, and the top 10 features were determined by the least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating 3D radiomic signatures and radiological features in the prediction system. The nomogram was evaluated from multiple perspectives and tested on the validation cohort.Results: The model combined 3 radiological features and seven 3D radiomic signatures. The area under the curve (AUC) of the model was 0.877 (95% CI: 0.829-0.925) in the training cohort, 0.864 (95% CI: 0.789-0.940) in the testing cohort, and 0.836 (95% CI: 0.749-0.924) in the validation cohort. The nomogram applied in all 3 cohorts showed reliable accuracy and calibration. The decision curve also demonstrated the clinical effectiveness of the nomogram.Conclusions: In this study, a nomogram-based model combining 3D radiomic signatures and radiological features was developed. Its performance in identifying IAC and MIA/AIS was satisfactory and had clinical value.
基金：
ChineseStomatological Association, Clinical Research Fund for Youth; [CSA-A2021-03]
第一作者机构：[1]Shanghai Univ Med & Hlth Sci, Fac Med Instrumentat, 279 Zhouzhu Rd, Shanghai 201318, Peoples R China
共同第一作者：
通讯作者：
通讯机构：[1]Shanghai Univ Med & Hlth Sci, Fac Med Instrumentat, 279 Zhouzhu Rd, Shanghai 201318, Peoples R China[*1]Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, 279 Zhouzhu Road, Pudong New Area, Shanghai 201318, China
推荐引用方式(GB/T 7714)：
Ren He,Xiao Zhengguang,Ling Chen,et al.Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma[J].QUANTITATIVE IMAGING IN MEDICINE AND SURGERY.2023,13(1):doi:10.21037/qims-22-491.
APA：
Ren, He,Xiao, Zhengguang,Ling, Chen,Wang, Jiayi,Wu, Shiyu...&Li, Ping.(2023).Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,13,(1)
MLA：
Ren, He,et al."Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma".QUANTITATIVE IMAGING IN MEDICINE AND SURGERY 13..1(2023)
