A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Chengyu Chen; Qun Geng; Gesheng Song; Qian Zhang; Youruo Wang; Dongfeng Sun; Qingshi Zeng; Zhengjun Dai; Gongchao Wang

doi:10.3389/fonc.2023.1066360

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Front Oncol. 2023 Mar 7:13:1066360. doi: 10.3389/fonc.2023.1066360. eCollection 2023.

Authors

Chengyu Chen^{1

2}, Qun Geng³, Gesheng Song⁴, Qian Zhang⁵, Youruo Wang⁶, Dongfeng Sun², Qingshi Zeng⁴, Zhengjun Dai⁷, Gongchao Wang¹

Affiliations

¹ Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
² Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
³ Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
⁴ Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China.
⁵ Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
⁶ Elite Class of 2017, Shandong First Medical University, Jinan, China.
⁷ Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China.

Abstract

Objective: To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).

Materials and methods: Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.

Results: Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.

Conclusion: Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

Keywords: CT; lung cancer; nomogram; radiomics; subcentimeter solid nodules.

Grants and funding

This research study was supported by the Science and Technology Development Plan of Jinan (201907061).