CT radiomics model for predicting the Ki-67 proliferation index of pure-solid non-small cell lung cancer: a multicenter study

Fen Liu; Qingcheng Li; Zhiqiang Xiang; Xiaofang Li; Fangting Li; Yingqiong Huang; Ye Zeng; Huashan Lin; Xiangjun Fang; Qinglai Yang

doi:10.3389/fonc.2023.1175010

CT radiomics model for predicting the Ki-67 proliferation index of pure-solid non-small cell lung cancer: a multicenter study

Front Oncol. 2023 Aug 29:13:1175010. doi: 10.3389/fonc.2023.1175010. eCollection 2023.

Authors

Fen Liu¹, Qingcheng Li¹, Zhiqiang Xiang¹, Xiaofang Li², Fangting Li³, Yingqiong Huang⁴, Ye Zeng⁵, Huashan Lin⁶, Xiangjun Fang¹, Qinglai Yang⁷

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
² Department of Radiology, The Affiliated Huaihua Hospital, Hengyang Medical School, University of South China, Huaihua, China.
³ Department of Radiology, People's Hospital of Zhengzhou, Zhengzhou, China.
⁴ Department of Radiology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China.
⁵ Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
⁶ Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China.
⁷ Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumor Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Abstract

Purpose: This study aimed to explore the efficacy of the computed tomography (CT) radiomics model for predicting the Ki-67 proliferation index (PI) of pure-solid non-small cell lung cancer (NSCLC).

Materials and methods: This retrospective study included pure-solid NSCLC patients from five centers. The radiomics features were extracted from thin-slice, non-enhanced CT images of the chest. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to reduce and select radiomics features. Logistic regression analysis was employed to build predictive models to determine Ki-67-high and Ki-67-low expression levels. Three prediction models were established: the clinical model, the radiomics model, and the nomogram model combining the radiomics signature and clinical features. The prediction efficiency of different models was evaluated using the area under the curve (AUC).

Results: A total of 211 NSCLC patients with pure-solid nodules or masses were included in the study (N=117 for the training cohort, N=49 for the internal validation cohort, and N=45 for the external validation cohort). The AUC values for the clinical models in the training, internal validation, and external validation cohorts were 0.73 (95% CI: 0.64-0.82), 0.75 (95% CI:0.62-0.89), and 0.72 (95% CI: 0.57-0.86), respectively. The radiomics models showed good predictive ability in diagnosing Ki-67 expression levels in the training cohort (AUC, 0.81 [95% CI: 0.73-0.89]), internal validation cohort (AUC, 0.81 [95% CI: 0.69-0.93]) and external validation cohort (AUC, 0.78 [95% CI: 0.64-0.91]). Compared to the clinical and radiomics models, the nomogram combining both radiomics signatures and clinical features had relatively better diagnostic performance in all three cohorts, with the AUC of 0.83 (95% CI: 0.76-0.90), 0.83 (95% CI: 0.71-0.94), and 0.81 (95% CI: 0.68-0.93), respectively.

Conclusion: The nomogram combining the radiomics signature and clinical features may be a potential non-invasive method for predicting Ki-67 expression levels in patients with pure-solid NSCLC.

Keywords: Ki-67; multicenter study; nomogram; non-small cell lung cancer; radiomics.

Grants and funding

This work was supported by the Scientific research project of the Hunan Provincial Health Commission of China (202209014713) and Clinical Medical Research Project 4310 of the University of South China (20214310NHYPY01).