Journal of Shandong University (Health Sciences) ›› 2022, Vol. 60 ›› Issue (5): 87-97.doi: 10.6040/j.issn.1671-7554.0.2022.0500

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Predictive value of CT-based radiomics nomogram for the invasiveness of lung pure ground-glass nodules

GAO Lin1, YU Xinxin2, KANG Bing2, ZHANG Shuai1, WANG Ximing1   

  1. 1. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China;
    2. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan 250021, Shandong, China
  • Published:2022-06-01

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Abstract: Objective To assess whether radiomic characteristics extracted from non-contrast computed tomography(NCCT)and contrast enhanced computed tomography(CECT)images could improve the ability to distinguish the histological invasive lesions manifesting as pure ground-glass nodules(pGGNs), and to develop a nomogram to improve the prediction ability to differentiate atypical adenomatous hyperplasia(AAH)and adenocarcinoma in situ(AIS)from minimally invasive adenocarcinoma(MIA)and invasive adenocarcinoma(IAC). Methods Our study recruited 332 patients with 364 lung pGGNs during Dec. 2018 and Jun. 2021 in Shandong Provincial Hospital Affiliated to Shandong First Medical University. According to pathological types, the patients were divided into prodromal lesion group(n=157, including AAH and AIS)and invasive lesion group(n=207, including MIA and IAC). Image features were extracted from each nodular region of interests on NCCT and CECT images. Least absolute shrinkage and selection operator(LASSO)was employed for feature selection, and the chosen characteristics were used to build a radiomics signature according to their weights in LASSO. Clinical variables and CT morphological features were combined to develop a clinical factor model. A nomogram including independent clinical variables and Rad-score was developed. The nomogram performance was confirmed by receiver operating characteristic(ROC)curve, calibration and decision curve analysis(DCA). Results The tri-phase including non-contrast, arterial and venous phase Rad-score showed a good discrimination in an average area under ROC curve(AUC)of 0.915 and 0.841 in the training and validation sets, higher than those in normal scan(0.882, 0.796), arterial phase(0.884, 0.814), venous phase(0.897, 0.841), and clinical factor model(0.805, 0.747). Radiomics nomogram consisting of tri-phase Rad-score and clinical factors had a better performance(AUC=0.928, 0.854)than the performance of Rad-score and clinical factor models. Conclusion The radiomic features extracted from CECT images can improve the determination ability, and the radiomics nomogram can help in predicting the invasiveness of adenocarcinoma manifesting as pGGNs.

Key words: Radiomics, Lung, Pure ground-glass nodule, Adenocarcinoma, Computed tomography

CLC Number: 

  • R445.3
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