Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Cancer Research Institute, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Eur Radiol. 2019 Apr;29(4):1674-1683. doi: 10.1007/s00330-018-5739-x. Epub 2018 Sep 25.
To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model.
This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity.
Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively).
Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations.
• Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules. • Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules. • Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.
开发和验证一个使用简单大小测量值的简明预测模型，用于区分偶然发现的拟切除亚实性结节（SSN）中的浸润性肺腺癌（IPA），并比较其与布罗克（Brock）模型的诊断性能。
本回顾性机构审查委员会批准的研究纳入了 407 名患者的 427 例手术切除的 SSN（121 例侵袭前病变/微浸润性腺癌[MIA]和 306 例 IPA）。将研究人群分层随机分为训练集和验证集（3:1）后，使用结节大小、实性比例和类型构建一个简单的逻辑模型，用于 IPA 的鉴别。使用 DeLong 法比较该模型与原始和改良布罗克模型的诊断性能，通过接收者操作特征曲线（AUC）下面积和 McNemar 检验比较诊断敏感性和特异性。
我们提出的模型在验证集中的 AUC 为 0.859，而原始布罗克模型的 AUC 为 0.775（p = 0.035），改良布罗克模型的 AUC 为 0.787（p = 0.006）。在同样高的特异性为 90%时，我们提出的模型的敏感性（65.8%）明显高于原始和改良布罗克模型（38.2%和 50.0%；p < 0.001 和 0.008）。
我们的研究结果表明，所提出的简明模型优于布罗克模型，表明其有潜力作为一种特定工具，用于区分拟切除的侵袭前病变和 MIA 与 IPA。需要对包括小 SSN 在内的偶然发现的 SSN 人群进行外部验证研究，以证实我们的观察结果。
• 大小测量值为偶然发现的亚实性结节的手术候选者提供了足够的风险分层信息。• 与布罗克模型相比，我们提出的简明模型在偶然发现的亚实性结节中具有更高的诊断性能。• 我们提出的模型可以专门区分偶然发现的亚实性结节中的浸润性腺癌，减少对惰性亚实性结节的过度治疗。
