Purpose

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. The aims of this study are to develop and validate a computed tomography (CT)‑based radiomics model for preoperative prediction of STAS in lung adenocarcinoma.

Methods and materials

This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson’s correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

Conclusion

CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance.

Key Points

• CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy.

• The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

中文翻译：

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基于CT的放射学和机器学习可预测肺腺癌在空气中的扩散。

目的

通过空气间隙（STAS）扩散是肺腺癌的一种新型侵入性模式，并且也是肺腺癌复发和预后较差的危险因素。这项研究的目的是开发和验证基于计算机断层扫描（CT）的放射线学模型，用于术前预测肺腺癌中的STAS。

方法和材料

这项回顾性研究获得了机构审查委员会的批准，纳入了462名经病理证实的肺腺癌患者（平均年龄58.06岁）。在90例患者中发现了STAS（19.5％）。两名经验丰富的放射科医生对术前薄层CT图像上的放射线影像学特征进行了分割和提取，并独立扩展了放射线影像学。使用类内相关系数（ICC）和Pearson相关来排除那些低可靠（ICC <0.75）和冗余（r > 0.9）功能。应用单因素逻辑回归来选择与STAS相关的放射学特征。开发了使用随机森林（RF）的基于Radiomics的机器学习预测模型，并通过五重交叉验证对其进行了校准。通过接收器工作特性（ROC）的曲线下面积（AUC）来测量模型的诊断性能。

结果

通过单变量分析，发现12个放射线学特征和年龄与STAS显着相关。RF模型可实现0.754的AUC（灵敏度为0.880，特异性为0.588）来预测STAS。

结论

基于CT的放射学模型可以在术前预测肺腺癌中的STAS，具有良好的诊断性能。

关键点

•基于CT的放射学和机器学习模型可以高度准确地预测肺腺癌在气隙（STAS）中的扩散。

•随机森林（RF）模型预测STAS的AUC为0.754（灵敏度为0.880，特异性为0.588）。