Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images

Quantitative Imaging in Medicine and Surgery

article

Jing Yan¹  Xing Xue³  Chen Gao¹  Yifan Guo¹  Linyu Wu¹  Changyu Zhou¹  Feng Chen³  Maosheng Xu¹ 

[1] The First Clinical Medical College of Zhejiang Chinese Medical University;Department of Radiology , The First Affiliated Hospital of Zhejiang Chinese Medical University;Department of Radiology, The First Affiliated Hospital , Zhejiang University School of Medicine

关键词: Ground glass nodule;    subsolid nodule;    Ki-67;    radiomics;    nomogram;   

DOI  :  10.21037/qims-20-1385

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: The Ki-67 proliferation index (PI) reflects the proliferation of cells. However, the conventional methods for the acquisition of the Ki-67 PI, such as surgery and biopsy, are generally invasive. This study investigated a potential noninvasive method of predicting the Ki-67 PI in patients with lung adenocarcinoma presenting with subsolid nodules. Methods: This retrospective study enrolled 153 patients who presented with pulmonary adenocarcinoma appearing as subsolid nodules (SSNs) on computed tomography (CT) images between January 2015 and December 2018. Presence of LUAD with SSNs was confirmed by histopathology. Of these participants, 107 patients were from institution 1 and were divided into a training cohort and an internal validation cohort in a 7:3 ratio. The other 46 patients were from institution 2 and were enrolled as an external validation cohort. All patients underwent conventional CT scans with thin-slice (≤1.25 mm) reconstruction, and 1,316 quantitative radiomic features were extracted from the CT images for each nodule. The minimum redundancy maximum relevance and the least absolute shrinkage and selection operator were used for feature selection, and the radiomics signature was constructed based on these selected features. Clinical features were examined using univariate logistic regression analysis. The nomogram was developed based on the radiomics signature and the independent clinical risk factors. The Delong test and t test were employed for statistical analysis. The performance of different models was assessed by the receiver operating characteristic (ROC) curve. Results: 0.05). Conclusions: The nomogram provides a novel strategy for determining the Ki-67 PI in predicting the proliferation of subsolid nodules, which may be beneficial for the management of patients with SSNs.

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