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Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

术前基于 CT 的影像组学联合术中冰冻切片预测肺结节浸润性腺癌的多中心研究

  • 影响因子:4.08
  • DOI:10.1007/s00330-019-06597-8
  • 作者列表:"Wu, Guangyao","Woodruff, Henry C.","Sanduleanu, Sebastian","Refaee, Turkey","Jochems, Arthur","Leijenaar, Ralph","Gietema, Hester","Shen, Jing","Wang, Rui","Xiong, Jingtong","Bian, Jie","Wu, Jianlin","Lambin, Philippe
  • 发表时间:2020-01-31
Abstract

Objectives Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). Methods This multicenter study cohort of 623 lung adenocarcinomas was split into training ( n  = 331), testing ( n  = 143), and external validation dataset ( n  = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. Results The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. Conclusions Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. Key Points • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

摘要

目的建立基于 CT 的影像组学模型,并将其与冰冻切片 (FS) 和临床数据相结合,以区分侵袭性腺癌 (IA) 和侵袭前病变/微创腺癌 (PM)。方法将 623 例肺腺癌的多中心研究队列分为培训 (n = 331) 、测试 (n = 143) 、和外部验证数据集 (n = 149)。使用选定的放射组学特征、 FS 结果、病变体积、临床和语义特征及其组合构建随机森林模型。受试者操作者特征曲线下面积 (AUC) 用于评价模型性能。测试模型的诊断准确性、校准和决策曲线。结果基于 radiomics 的模型在区分 IA 和 PM 方面显示了良好的预测性能和诊断准确性,在训练、测试和验证数据集中的 auc 为 0.89 、 0.89 和 0.88, 分别和相应的精度为 0.82,0.79 和 0.85。增加病变体积和 FS 显著提高了模型的性能,auc 为 0.96 、 0.97 和 0.96,在三个数据集中的准确性为 0.91 、 0.94 和 0.93。富集 radiomics 和 volume 的 FS 模型与单独富集 volume 的 FS 模型的 AUC 无显著差异,而前者具有更高的准确性。结合所有可用信息的模型显示,与富含放射组学和体积的 FS 模型相比,AUC 和准确性有轻微的非显著改善。结论放射组学标记是 IA 风险的潜在生物标志物,尤其是与 FS 联合应用,有助于指导肺结节患者的手术策略。要点 • 基于 CT 的影像组学模型可能是术前预测肺结节患者浸润性腺癌的有价值的工具。•放射组学联合冰冻切片可帮助指导肺结节患者的手术策略。

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影响因子:3.94
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DOI:10.1016/j.taap.2019.114847
作者列表:["Bernstein DM","Toth B","Rogers RA","Kling DE","Kunzendorf P","Phillips JI","Ernst H"]

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DOI:10.1042/BST20191010
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