@article{QIMS154899,
author = {Menglong Zheng and Qi Chen and Lijun Dong and Jianlin Wei and Peng Wang and Yonghong Yu and Ying Liu},
title = {Development and validation of intratumoral and peritumoral CT-based radiomics nomograms for predicting malignancy and invasiveness of pulmonary ground-glass nodules: a retrospective study},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {16},
number = {7},
year = {2026},
keywords = {},
abstract = {Background: Currently, it is still very challenging for radiologists to visually distinguish between benign and malignant ground-glass nodules (GGNs) and predict the degree of invasiveness of GGNs directly from computed tomography (CT) images. Radiomics is a newly emerging technique that can transform digital medical images into numerous quantitative features revealing pathophysiology. The present study aimed to evaluate the predictive value of intratumoral and peritumoral CT-based radiomics and the radiomics nomogram for determining the benign or malignant status of pulmonary GGNs and the invasiveness of malignant GGNs.Methods: A total of 1,372 GGNs from 1,280 patients with preoperative CT scans were included in this retrospective study. Among these, 212 GGNs were diagnosed with inflamed or infected benign lesions, 31 with atypical adenomatous hyperplasia (AAH), 257 with adenocarcinoma in situ (AIS), 559 with minimally invasive adenocarcinoma (MIA), and 313 with invasive adenocarcinoma (IAC). Malignant GGNs were classified according to clinical management strategies, with AAH, AIS, and MIA categorized as non-invasive lesions, and IAC as an invasive lesion. Two types of regions of interest (ROIs) were annotated from the CT images: gross tumor volume (GTV) and peritumoral volume (5 and 10 mm around the tumor, PTV1 and PTV2). Radiomics models, a clinical factor model, and a combined radiomics nomogram integrating the radiomics score with independent clinical predictors were constructed and compared.Results: For malignancy predictions, the GTV + PTV1 + PTV2 radiomics model and the PTV1 radiomics model achieved the highest performance in the training set and testing set, with areas under the curve (AUCs) of 0.840 and 0.796, respectively. The combined radiomics nomogram demonstrated excellent discrimination in both the training set and testing set, with AUCs of 0.870 and 0.817, respectively. Moreover, the DeLong test showed that in both the training and validation cohorts, the AUC values of the comprehensive nomogram were significantly different from those of the clinical factor model, GTV radiomics model, PTV2 radiomics model, and GTV + PTV1 + PTV2 radiomics models (P},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/154899}
}