拓扑不变影像学特征与生物标志物联合用于立体定向消融放疗治疗肺癌前准确预测症状性放射性肺炎：一项回顾性分析。

Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis.

机构信息

Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan.

Faculty of Medical Sciences, Division of Medical Quantum Science, Department of Health Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan.

出版信息

PLoS One. 2022 Jan 31;17(1):e0263292. doi: 10.1371/journal.pone.0263292. eCollection 2022.

OBJECTIVES

We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer.

METHODS

A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP-(grade 1)) and 30 cases (8 RP+ and 22 RP-) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models.

RESULTS

The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively.

CONCLUSION

This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.

目的

本研究旨在探索拓扑不变贝蒂数（BN）特征签名与生物标志物相结合，用于准确预测立体定向消融放疗（SABR）治疗肺癌前有症状（≥2 级）放射性肺炎（RP+）的发生。

方法

本研究共纳入 272 例接受 SABR 治疗的早期非小细胞肺癌患者。使用支持向量机（SVM）模型，通过对计划计算机断层扫描（pCT）图像中提取的基于 BN 的特征签名和预处理生物标志物血清 Krebs von den Lungen-6（BN+KL-6 模型）进行训练，预测 RP+的发生。总共 242 例（20 例 RP+和 222 例 RP-（1 级））和 30 例（8 例 RP+和 22 例 RP-）分别用于训练和测试模型。从 BN 图谱中提取 BN 特征，该图谱通过对图谱进行基于直方图和纹理的特征计算，来描述 pCT 图像上潜在 RP+肺部区域的拓扑不变异质特征。SVM 模型用于构建基于最小绝对值收缩和选择算子逻辑回归模型的 BN 特征的 RP+患者预测。根据受试者工作特征曲线（AUC）下面积和测试中的准确性评估预测模型。将 BN+KL-6 模型的性能与 BN、常规原始 pCT 和小波分解（WD）模型的性能进行了比较。