Li Chuanjun, Jiang Changsi, Gong Jingshan, Wu Xiaotao, Luo Yan, Sun Guopin
Department of Radiology, Pingshan District People's Hospital of Shenzhen, Shenzhen, China.
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.
Quant Imaging Med Surg. 2020 Oct;10(10):1984-1993. doi: 10.21037/qims-20-724.
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.
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).
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).
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.
气腔播散（STAS）是肺腺癌一种新的侵袭模式，也是肺腺癌复发及预后不良的危险因素。本研究旨在建立并验证基于计算机断层扫描（CT）的逻辑回归模型，以预测肺腺癌中的STAS。
本回顾性研究经两个中心的机构审查委员会批准，纳入578例经病理证实的肺腺癌患者（中心I 462例，中心II 116例）。从中心I的90例患者（19.5%）和中心II的28例患者（24.1%）中识别出STAS。测量了最大直径、结节面积以及部分实性结节中实性成分的面积。评估了21项语义特征。采用单因素分析选择与中心I患者队列中STAS相关的CT特征。多变量逻辑回归用于基于具有统计学意义的变量建立基于CT特征的模型。该模型在验证队列中进行验证，然后在外部测试队列（来自中心II的患者）中进行测试。通过受试者操作特征（ROC）曲线下面积（AUC）来衡量模型的诊断性能。
在单因素分析中，发现年龄和11项CT特征，包括肿瘤最大直径、肿瘤最大面积、实性成分面积及比例、结节类型、胸膜增厚、胸膜凹陷、纵隔淋巴结肿大、血管集束征和分叶、毛刺，与STAS显著相关。最佳逻辑回归模型包括年龄、最大直径和实性成分比例，比值比（OR）值分别为0.967（95%CI：0.944 - 0.988）、1.027（95%CI：1.008 - 1.046）和5.14（95%CI：2.180 - 13.321）。该模型在验证队列和外部测试队列中的AUC分别为0.801（95%CI：0.709 - 0.892）和0.692（95%CI：0.518 - 0.866）。差异无统计学意义（P = 0.280）。
基于CT的逻辑回归机器学习模型能够在术前很好地预测肺腺癌中的STAS，诊断性能良好，可作为常规CT解读的补充。
