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

He Ren; Zhengguang Xiao; Chen Ling; Jiayi Wang; Shiyu Wu; Yanan Zeng; Ping Li

doi:10.21037/qims-22-491

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

Quant Imaging Med Surg. 2023 Jan 1;13(1):237-248. doi: 10.21037/qims-22-491. Epub 2022 Nov 2.

Authors

He Ren^#¹, Zhengguang Xiao^#², Chen Ling^#¹, Jiayi Wang^#³, Shiyu Wu¹, Yanan Zeng¹, Ping Li¹

Affiliations

¹ Faculty of Medical Instrumentation, Shanghai University of Medicine & Health Sciences, Shanghai, China.
² Department of Radiology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Anesthesiology Department of Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

^# Contributed equally.

Abstract

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.

Keywords: 3D radiomics; Lung adenocarcinoma; computed tomography; invasive adenocarcinoma (IAC); radiological.