A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma

Chuanjun Li; Changsi Jiang; Jingshan Gong; Xiaotao Wu; Yan Luo; Guopin Sun

doi:10.21037/qims-20-724

A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma

Quant Imaging Med Surg. 2020 Oct;10(10):1984-1993. doi: 10.21037/qims-20-724.

Authors

Chuanjun Li¹, Changsi Jiang², Jingshan Gong², Xiaotao Wu¹, Yan Luo², Guopin Sun¹

Affiliations

¹ Department of Radiology, Pingshan District People's Hospital of Shenzhen, Shenzhen, China.
² Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.

Abstract

Background: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based logistic regression model to predict STAS in lung adenocarcinoma.

Methods: This retrospective study was approved by the institutional review board of two centers and included 578 patients (462 from center I and 116 from center II) with pathologically confirmed lung adenocarcinoma. STAS was identified from 90 center I patients (19.5%) and 28 center II patients (24.1%) from. The maximum diameter, nodule area, and area of solid components in part-solid nodules were measured. Twenty-one semantic characteristics were assessed. Univariate analysis was used to select CT characteristics, which were associated with STAS in the patient cohort of center I. Multivariable logistic regression was used to develop a CT characteristics-based model on those variables with statistical significance. The model was validated in the validation cohort and then tested in the external test cohort (patients from center II). The diagnostic performance of the model was measured by area under the curve (AUC) of receiver operating characteristic (ROC).

Results: At univariate analysis, age and 11 CT characteristics, including the maximum diameter of the tumor, the maximum area of the tumor, the area and ratio of the solid component, nodule type, pleural thickening, pleural retraction, mediastinal lymph node enlargement, vascular cluster sign, and lobulation, specula were found to be significantly associated with STAS. The optimal logistic regression model included age, maximum diameter and ratio of solid component with odds ratio (OR) value of 0.967 (95% CI: 0.944-0.988), 1.027 (95% CI: 1.008-1.046) and 5.14 (95% CI: 2.180-13.321), respectively. This model achieved an AUC of 0.801 (95% CI: 0.709-0.892) and 0.692 (95% CI: 0.518-0.866) in the validation cohort and the external test cohort, respectively. The difference was not statistically significant (P=0.280).

Conclusions: CT-based logistic regression machine learning model could preoperatively predict STAS in lung adenocarcinoma with excellent diagnosis performance, which could be supplementary to routine CT interpretation.

Keywords: Spread through air space (STAS); computed tomography (CT); lung adenocarcinoma.